
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39261576
72446
10.1038/s41598-024-72446-4
Article
A method to integrate hydraulic structure models into 3D terrain models for irrigation infrastructure visualization
He Liang heliang@njxzc.edu.cn

123
Han Baoji 1
Ji Haojie 1
Mao Guangsheng 1
Chen Junyi joel59cjy@163.com

4
1 https://ror.org/03fnv7n42 grid.440845.9 0000 0004 1798 0981 School of Environmental Science, Nanjing Xiaozhuang University, Nanjing, 211171 Jiangsu China
2 grid.260474.3 0000 0001 0089 5711 Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing, 210023 Jiangsu China
3 https://ror.org/045yewh40 grid.511454.0 Jiangsu Center for Collaborative Innovation in Geographic Information Resource Development and Application, Nanjing, 210023 Jiangsu China
4 grid.218292.2 0000 0000 8571 108X Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, 650500 Yunnan China
11 9 2024
11 9 2024
2024
14 2125531 12 2023
6 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Seamless integration of three-dimensional (3D) terrain models and hydraulic structure models is a technical challenge in the construction of 3D virtual scenes for irrigation areas. This study proposes a level-of-detail (LOD)-based dynamic classification integration method for hydraulic structure models and 3D terrain models, called CM-D-LOD. Hydraulic structures are classified according to their point, line, and surface morphologies, as well as their dependence on or independence of the terrain into four categories: point-like hydraulic structures independent of terrain, line-like hydraulic structures dependent on terrain, surface-like hydraulic structures dependent on terrain, and surface-like hydraulic structures independent of terrain. By utilizing the proposed model classification integration method, a visualization management platform for virtual geographical environments of irrigation areas is developed, and experiments are conducted in the Zhuluo Ba Irrigation Area within the large economic zone along China’s eastern coast. Results demonstrate that the integration accuracy can be controlled between 0.2 and 0.7 m and that the 3D virtual scene of the irrigation area can be updated in real time. The proposed classification integration method transforms the traditional global model integration approach into a more efficient one, significantly improving the efficiency of constructing virtual geographical scenes for irrigation areas.

Subject terms

Environmental sciences
Natural hazards
Engineering
http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 42301487 He Liang Natural Science Research of Basic Disciplines in Universities of Jiangsu Province23KJD170004 He Liang Key Laboratory of Virtual Geographic Environment, Ministry of Education, Open Fund Project2023VGE03 He Liang Nanjing Xiaozhuang University Nature Science High-level Research Project2022NXY03 He Liang issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Sustainable development of modern agriculture constitutes the forefront for all global nations1. The construction of smart irrigation areas, as a crucial facet of modern agricultural management, is extensively utilized within the field of agricultural production2. Smart irrigation helps improve water resource utilization efficiency and enhances crop yield and quality. It achieves a balance between agricultural production and environmental protection, supporting the long-term sustainable development of agriculture. Additionally, it promotes the integration and innovation of cutting-edge technologies such as the Internet of Things (IoT), big data analytics, and artificial intelligence (AI) in the agricultural sector3. Particularly, the construction of a virtual geographical environment for irrigation areas is a critical component in the evolution of smart irrigation areas, aimed at rapidly constructing an artificial irrigation area environment with geographical attributes and spatial features4. By simulating the geographical elements and processes of genuine irrigation areas and transforming them into digital expression, the virtual geographical environments of the irrigation area provide powerful support for agricultural management decisions.

In modern agricultural management, 3D terrain models and hydraulic structure models are crucial for decision-making processes such as precise irrigation, water resource management, and farmland planning. A key issue in constructing virtual geographical environments for irrigation areas is the integration of 3D terrain models and hydraulic structure models5. Inefficient integration methods often result in prolonged data processing times, low model accuracy, and increased operational complexity, all of which negatively impact agricultural production efficiency6. For example, if the design of an irrigation system is based on inaccurate terrain and hydraulic structure models, it may lead to uneven water distribution, insufficient or excessive irrigation, thereby affecting crop growth, reducing yield, and increasing water wastage. As the demand for detailed 3D visualization in irrigation areas intensifies, the requirements for the detail and accuracy of terrain data are escalating, and the display requirements for hydraulic structure models need to adapt to terrain changes. However, current research on optimizing integration methods, improving accuracy, and enhancing user-friendliness is relatively scarce. This results in limitations in the practical application of existing technologies, especially when dealing with complex terrains and large hydraulic structures. Existing methods often lack the flexibility and accuracy needed to meet the demands of precise agricultural management7. Therefore, addressing the integration of hydraulic structures and terrain in the virtual geographical environment of irrigation areas becomes a pressing engineering issue that needs to be resolved.

Traditional integration methods of 3D terrain models and hydraulic structure models are classified into two primary categories. Both methodologies are competent in resolving the inappropriateness between terrain models and hydraulic structure models, thereby generating the constructed 3D virtual environment of irrigation areas8. One approach involves constructing an integral point set of hydraulic structures and terrain, based on the design outcomes of hydraulic structure models, thereby integrating both into a unified model9. This approach reconstructs all the patches within the scene that require display, thereby contributing to low efficiency in 3D modeling. Another method is to construct hydraulic structure models and 3D terrain models according to the idea of “divide, conquer, and merge”10. To achieve integration, the triangular irregular network (TIN) terrain is initially trimmed along the boundaries of the hydraulic structure models. Then, the hydraulic structure models are incorporated into this modified TIN terrain. This method utilizes the original terrain TIN to improve the efficiency of modeling, and it realizes the relatively fast integration of hydraulic structure models and terrain models. However, with the progressive popularization of massive high-precision terrain and ground object data, the magnitude of the model data increases, amplifying the issues of inefficient integration11.

To deal with massive terrain and ground object data, the multiresolution LOD model has been widely used in the field of 3D visualization12. Traditionally, complex 3D geographical attributes are converted into visual representations of different levels of detail (LODs) and then transformed into LOD models that display within suitable distance range constraints13. Vector features are superimposed on a realistic 3D terrain to facilitate a precise spatial analysis14. Alternatively, the extraction of ground objects’ outlines may be utilized as restricting constraints for the terrain TIN model, effectuating the integration of the terrain models and ground object models in 3D15. For extensive buildings, the ground object data can serve as a correction factor to modify the terrain, thereby achieving the 3D integrated display between ground objects and terrains16. In addition, an alternative approach involves extending the building model to the lowest section of the terrain model encompassed by the building model while simultaneously reconstructing the terrain region covered by this model17. For strip-like ground objects, the axial line must initially be identified, followed by parallel double lines to construct their boundaries. Subsequently, the terrain region beneath the coverage of the strip-like ground objects undergoes a network reconstruction18. The aforementioned methods focus on the treatment of cracks at different levels in the construction of terrain models, and the determination of transition evaluation factors at diverse detail levels19. Furthermore, the integration algorithm commonly utilized for terrains and ground objects adopts a holistic network reconstruction, resulting in inadequate precision and slow integration speed.

This study proposes a novel dynamic classification integration method for 3D terrain models and hydraulic structure models called CM-D-LOD. In view of the TIN terrain organizational framework, this study continues to use the idea of divide, conquer, and merge, thereby proposing the integration algorithm of point-like hydraulic structure models independent of terrain and terrains, the integration algorithm of line-like hydraulic structure models dependent on terrain and terrains, the integration algorithm of surface-like hydraulic structure models dependent on terrain and terrains, and the integration algorithm of surface-like hydraulic structure models independent of terrain and terrains. The CM-D-LOD model transforms the traditional holistic approach into a classified approach, achieving a shift from single integration to multiple integrations, and from global multi-level model integration to local classified model integration. This targeted integration method enhances fusion efficiency to meet the high-precision visualization needs of virtual geographical environments in irrigation areas. By addressing these research gaps, we can significantly improve the quality of agricultural decision-making, promote the widespread adoption of advanced agricultural technologies, and thereby enhance the sustainability and efficiency of agricultural production.

Study area and data source

Study area

The study area is located in the Zhuluo Ba Irrigation Area, Huaiyin District, Huai’an City, along China’s eastern coastal region, which falls within the downstream region of the Yangtze River Delta20. The geographical coordinates are 33° 34′ 45′′–33° 53′ 55′′ N and 118° 45′ 10′′–118° 55′ 15′′ E, as shown in Fig. 1a and b. The total area of this irrigation area is 342 km2, including cultivated land spanning 215 km2, water bodies encompassing 50.5 km2, designed irrigation coverage of 210.6 km2, and effective irrigation coverage of 173.3 km2, thereby making it a large irrigation area in China. The entire irrigation area takes the form of a long strip, stretching approximately 35 km from north to south and between 9 and 13 km from east to west. The terrain of this irrigation area presents an inclined topography with a high center, with the highest elevation at 12.8 m and the lowest elevation at 9.2 m, featuring two depressions in the southern portion, namely, the Southwest and the Xiajia Hu polders21. The geographical location of the study area and the spatial distribution of hydraulic structures are shown in Fig. 1c.Fig. 1 Geographical location and spatial distribution of hydraulic structures in the study area: (a) Urban regions along the eastern coast of China; (b) Geographical location of the study area; (c) Distribution of hydraulic structures in the study area. The figure is created used ArcMap 10.8, http://www.arcgis.com.

Rivers around the main channel of the irrigation area mainly encompass the Middle Canal, the abandoned Yellow River, and other watershed rivers, along with important rivers, such as the Yuejin and Quxi Rivers. The Middle Canal, spanning a length of 179 km, belongs to the northern section of the Beijing–Hangzhou Grand Canal in Jiangsu Province and serves as a crucial channel for transporting water in the eastern phase of the South-to-North Water Transfer Project22. The Yuejin and Quxi Rivers, extending over lengths of 34.6 km and 36.6 km, respectively, are north–south rivers originating from the Middle Canal in the south and draining into the Liutang He Land Culvert in the north. The primary water supply for the irrigation area is derived from the Huai River of the Hongze Lake via the Middle Canal, with supplementing from the Yangtze River water during arid years through the Jiangdu Pumping Station23. The water intakes of the irrigation area are the Zhuluo Ba Intake Sluice and the Xiajia Hu Intake Sluice. Presently, the permitted annual quota of water intake in the Zhuluo Ba Irrigation Area is 101.44 million m3.

Data source

Experimental data were sourced from the National Earth System Science Data Center–Yangtze River Delta Branch Center, Huaiyin District Water Resource Management Department of Huai’an City, Jiangsu Province and field surveys24. The basic geographical information data incorporate the digital line graphic (DLG), digital elevation model (DEM), digital orthophoto map (DOM), and remote sensing data of the irrigation area25. The hydrological data encompass the annual runoff alteration data of rivers from 2010 to 2022, the seasonal flow rate change data of rivers from 2010 to 2022, and the water level shift data of rivers from 2010 to 202226. The construction data of hydraulic structures comprises 12 rivers, 48 ditches, 6 main canals, 70 branch canals, 10 sluices, 10 pumping stations, and 1 land culvert. Support facilities data encompass 13 solar measuring instruments and water depth measuring instruments, 10 electric management stations, and 87 video surveillance stations27. Attribute data specifically include hydrological circumstances data, engineering conditions monitoring data, analytical and testing data, river maintenance data, and associated device data.

In addition, to ensure the precision of the simulation model, the acquired data through field investigation and sampling include the CAD drawings for the internal and external construction of the irrigation area. The empirical dataset also encompasses field environmental data, such as building modeling, tree modeling, and monitor station modeling; texture data, such as walls, ground surfaces, river channels, and ditches; specific layout data for each residential building and detection station; and panoramic shooting data28. The summary of raw data is shown in Table 1.Table 1 Summary of raw data.

Data type	Data interpretation	File type	
Spatial data	Vector data	Geographical boundary map

Professional chart map

DLG data

Factor graph data

	.shp

.shx

.dbf

.prj

.xml

.mxs

.ixs

	
Hydraulic structures’ distribution data

Management stations’ distribution data

Monitoring stations’ distribution data

Rivers’ section data

	
Grid data	Remote sensing imagery

DEM data

DOM data

	.tiff

.msi

.GeoTiff

	
Attribute data	Hydrological circumstance data

Engineering circumstance data

Analytical and testing data

River maintenance data

Associated device data

	.xls

.mdb

.dbf

.dat

.txt

	

Methodology

Integration process for hydraulic structure models and 3D terrain models

Integrating 3D terrain models and hydraulic structure models can enhance the navigability of the daily management of irrigation areas and assist the government in making intelligent water diversion decisions for the irrigation area29. According to the two schemes of hydraulic structure classification and LOD multiresolution model dynamic integration, a LOD-based dynamic classification integration method for hydraulic structure models and 3D terrain models is proposed. Depending on the geometric characteristics of hydraulic structure models, entities with different geometric features are selected for loading at different terrain LOD levels30. This method avoids jumping and highlighting for the loading model, thereby exerting a favorable transitional role, augmenting modeling visualization efficiency, and refining the modeling scene texture.

Hydraulic structures are commonly divided into diverse classifications according to their attribute information. To effectively classify hydraulic structures in the geographical environment of irrigation areas, the most suitable classification approach based on geometric characteristics must be determined. Subsequently, integrative methods can be developed at different levels to address this classification.

Classification for hydraulic structure models

In view of the characterization of hydraulic structures, one classification attribute frequently fails to distinctly reflect the typical features of hydraulic structures. To mirror the unique attribute information distinct from other hydraulic structures, a comprehensive classification description of hydraulic structures in irrigation areas should be performed based on diverse attribute information31. The comprehensive classification model for hydraulic structures is shown in Table 2.Table 2 Comprehensive classification model for hydraulic structures.

Model name	Attribute name	Attribute information	Model instance	
Hydraulic structure model	2D Representation	Point	Revetment tree, monitoring devices, small buildings, road lights, surveillance cameras, channel direction signs	
Line	Rivers at a far view, main channels, branch channels, ditches	
Surface	Large buildings, rivers at a near view, lakes, pumping stations, sluices, land culverts	
Relationship with the terrain	Dependent on terrain	Rivers, channels, lakes, main channels, branch channels, ditches	
Independent of terrain	Revetment tree, buildings, pumping stations, sluices, monitoring devices, road lights	

Comprehensive integration process for the entire model

The model integration of this study utilizes primarily the foundational geographical information furnished by the National Earth System Science Data Center–Yangtze River Delta Branch Center. This suite of datasets also incorporates data from irrigation management departments and field surveys. These data are standardized to construct the spatial and attribute databases for the irrigation area. Initially, 3D terrain models and hydraulic structure models for the irrigation area are constructed32. Subsequently, the type of hydraulic structures is, determined and corresponding integration algorithms are selected, deriving the integration model for 3D terrains and hydraulic structures. The virtual geographical environment of the irrigation area is constructed finally. The comprehensive integration process for the entire model is shown in Fig. 2.Fig. 2 Comprehensive integration process for the entire model.

Hydraulic structures are classified according to their point, line, and surface morphologies, as well as their dependence on or independence of the terrain into four categories: point-like hydraulic structures independent of terrain, line-like hydraulic structures dependent on terrain, surface-like hydraulic structures dependent on terrain, and surface-like hydraulic structures independent of terrain. The following discourse will elucidate these types of hydraulic structure model.

Model classification integration method

Point-like hydraulic structures independent of terrain

Under the real surface terrain of irrigation areas, point-like hydraulic structures independent of terrain primarily consist of revetment trees, monitoring devices, and infrastructures, such as road lights, surveillance cameras, and channel direction signs distributed along the channels. When such hydraulic structures are integrated with TIN terrain, the attributes of the terrain are not modified, and only position registration is required. In view of the efficiency of the interpolation algorithm and the distortion effect when the dynamic details increase, this study proposes the integration algorithm of dynamic levels based on LOD. This algorithm avoids the distortion of hydraulic structures penetrating into the earth or floating in the air when the resolution level changes.

The specific approach is as follows. The LOD level of the current two-dimensional positional coordinate of the hydraulic structure is initially extracted. Then, all nodes of the current LOD level are iterated through, as shown in Fig. 3a. Subsequently, the triangle facet ABC, in which the location point P resides, is determined, as shown in Fig. 3b. The elevations of points A, B, and C are represented by h1, h2, and h3, respectively. Three judgments are performed. First, whether the current position point P is a designated construction feature point P1 within the current LOD is determined. If so, then the current elevation point is directly retrieved, and the display of ground objects is configured accordingly. The elevation of point P1 is h1. Second, whether the current position point P is a point P2 on the triangle boundary is determined. If so, then linear interpolation is used to resolve the elevation information of the interpolated location. The elevation of point P2 is h4. Third, whether the current position point P is an internal point P3 of the triangle is evaluated. Linear interpolation cannot achieve requisite data accuracy in this instance. Consequently, bilinear interpolation is utilized to compute the current elevation point information33. The elevation of point P3 is h5, as shown in Fig. 3c. If the scene changes, then the LOD level may need to be adjusted. The aforementioned steps are repeated to facilitate dynamic computation across any LOD level. Consequently, the progressive interpolation of point-like hydraulic structures independent of terrain at varying LOD levels is accomplished. Finally, the point-like hydraulic structures independent of terrain are configured and displayed, as shown in Fig. 3d. Given the utilization of static LOD data in this study, the LOD is generated during the data preprocessing phase34. The elevation data of fixed points remain static at each level, thereby requiring only one calculation, which can subsequently be stored in a database. If the elevation data already exist at the point being accessed, then they are directly retrieved to avoid repetitive computation.Fig. 3 Integration algorithm of point-like hydraulic structure models independent of terrain and terrains: (a) TIN terrain model; (b) Triangle facet where ground objects reside; (c) Elevation acquisition method; (d) Interpolation integration.

Line-like hydraulic structures dependent on terrain

Under the actual surface terrain of irrigation areas, line-like hydraulic structures dependent on terrain predominantly manifest as rivers at a far viewpoint within the same scene. Such hydraulic structures are observed remotely within the same scene, and their widths become imperceptible at a far viewpoint, thereby presenting a linear geometry. In light of data redundancy and rendering efficiency, the integration algorithm of line-like hydraulic structures dependent on terrain and the terrains is proposed. This strategy avoids integrating the scene at a far viewpoint scene in band form, thereby reducing data redundancy and accelerating the scene rendering efficiency.

The specific approach is as follows. Given that the majority of the original river data are vector strip data, the midline of the strip data must be extracted to acquire the essential framework data of the original rivers, as shown in Fig. 4a. Subsequently, the minimum enclosing rectangle for river line data is attained35. Then, the maximum elevation difference within the minimum enclosing rectangular region of the river line is calculated. The ratio of the difference to the length of the river line serves as the threshold D for the current simplification of the terrain. The current river line and terrain point data are simplified within threshold D, utilizing the Douglas–Peucker algorithm for simplification. Line A is formed by connecting the initial and final points of the line segment. The distance from each segment point to line A is calculated separately. The line segment whose point distances are less than threshold D are deleted while retaining others, as shown in Fig. 4b. Thereafter, the current maximum distance point is utilized as the division point, which is connected to the endpoints. The aforementioned evaluation is repeated until simplification is attained, as shown in Fig. 4c. Subsequently, the elevation values of simplified river point data within the local region are obtained, consistent with the method employed to acquire the elevation of point-type hydraulic structures independent of terrain33. Ultimately, the triangular network for the minimum enclosing rectangle occupation by this river line undergoes local TIN reconfiguration, as shown in Fig. 4d. To enhance visual efficiency, this study incorporates the display method of combining static and dynamic for LOD transitions in real-time scenes. The reduction of LOD levels remains constant, whereas the augmentation of LOD levels decreases the simplification threshold. Simultaneously, river information is stored for each LOD level, thereby enhancing the efficiency of the repetitive invocation.Fig. 4 Integration algorithm of line-like hydraulic structure models dependent on terrain and terrains: (a) Pre-simplified river midline; (b) Douglas–Peucker algorithm for simplification; (c) Simplified river midline; (d) Local TIN reconfiguration.

Surface-like hydraulic structures dependent on terrain

Under the real surface terrain of irrigation areas, surface-like hydraulic structures dependent on terrain predominantly manifest as rivers at a near viewpoint within the same scene. The boundaries of such hydraulic structures are typically irregular, necessitating a real-time calculation of the intersection points between their boundaries and terrains in the current scenes to acquire the true elevation. These calculations facilitate an integrated display of such hydraulic structures and terrains. Initially, these hydraulic structures are classified according to various data sources. Subsequently, river point and terrain data are subject to the appropriate simplification process. For spatial points that do not possess elevation data, distinct methods of interpolation are utilized, depending upon their positioning, to calculate the point’s elevation, thereby enhancing display efficiency. Simultaneously, when reconstructing the TIN for river and terrain boundaries, the characteristic of parallelism of the river model boundaries is considered to construct a simplified network structure for multipoint river models. This method reduces the complexity of TIN construction, thereby enhancing rendering efficiency.

The specific approach is as follows. Given the LOD level occupied by the river, the elevation values of nodes are computed via traversal across all nodes within the current LOD level. The blue lines in Fig. 5a represent the river boundaries. The river boundary is then simplified, and the vertices of the TIN obscured by the river and terrain are eliminated. This simplification method is equivalent to the centerline simplification approach of line-like hydraulic structures dependent on terrain, as shown in Fig. 5b. Subsequently, the point-by-point calculation approach is employed to ascertain the elevation information of the simplified river boundary points36. This involves sequentially identifying the intersection points and locations between the current LOD-level triangular network and each point. The method used in this computation aligns with the procedure for deriving the elevation of point-like hydraulic structures independent of terrain, resulting in the current boundary elevation. Then, the interior of the river is subjected to a triangular partitioning process, as shown in Fig. 5c. Ultimately, the river boundary data points are merged with the terrain data points, thereby conducting repartition and restriction of the local TIN37. Furthermore, the current elevation information is archived in real time for expedient later invocation and demonstration, as shown in Fig. 5d.Fig. 5 Integration algorithm of surface-like hydraulic structure models dependent on terrain and terrains: (a) Nodes within the current LOD level; (b) Simplified river model; (c) Internal partition of rivers; (d) Local TIN reconfiguration.

Surface-like hydraulic structures independent of terrain

Under the real surface terrain of irrigation areas, surface-like hydraulic structures independent of terrain often manifest as regular building models of varied shapes, such as pumping stations, sluices, and land culverts. The regular building exhibits a level bottom surface and a vertical longitudinal profile. Hence, the elevation data of the building model’s base must be used as the current terrain data, and the terrain data should be modified to integrate the terrain model with the building successfully. This study employs an integration algorithm that modifies the terrain model to accommodate hydraulic structure models. Primarily, the technique involves utilizing a regular bottom polygon vertex storage and discarding other boundary data points. Consequently, the partition of the bottom polygon employs the rendering algorithm of pertinent triangles, which partitions the base triangles with outstanding efficiency. Furthermore, instead of accounting for the intersection points between polygon boundaries and TIN terrain, an arrangement is directly implemented between bottom polygon vertices and TIN vertices that have not been concealed by the bottom surface. This strategy not only realizes the integrated visual display of buildings and terrain models but also ensures that some plane data is not concealed by undulating terrain data, thereby preventing distortion in the real terrain environment. The accuracy of the integration of building and terrain models is enhanced, achieving true integration of building and terrain models.

The specific approach is as follows. The original TIN terrain model is utilized to ascertain the information data points of the building’s bottom surface, as shown in Fig. 6a. Subsequently, the circumscribing polygon of the bottom surface data points is extracted, as shown in Fig. 6b. The vertex set for the polygon is designated as Nodes = {A, B, C, D, E, F}. Given that the bottom surface of a building is typically regular and flat, regular polygon storage is employed to process the vertex data of the bottom surface38. Subsequently, the current LOD level is determined, and the minimum elevation within the polygon region is extracted as the elevation value of the building’s bottom surface38. Simultaneously, the polygon is subjected to a simple partition algorithm for triangular partition, and the terrain data points concealed by the building are discarded, as shown in Fig. 6c. Finally, the TIN vertex set Box = {6, 7, 8, 9, 10}, which is unmasked but intersects with the bottom polygon of the terrain, is subjected to a network reconstruction with the base polygon of the building, as shown in Fig. 6d. When the LOD level is modified, the aforementioned steps are reiterated, achieving a dynamic integration display of surface-like hydraulic structures independent of terrain and terrains39.Fig. 6 Integration algorithm of surface-like hydraulic structure models independent of terrain and terrains: (a) Original TIN terrain model; (b) Circumscribing polygon of the bottom surface; (c) Internal partition of the building; (d) Local TIN reconfiguration.

Evaluation strategy for model integration accuracy

The successful integration of terrain models and hydraulic structure models requires eliminating distortions that occur when hydraulic structures penetrate into the earth or appear float in the air, ensuring that no inconsistencies with physical laws are visible. This seamless integration in visualization also necessitates a certain degree of data precision to facilitate the positioning, spatial inquiry, and other analytical operations of 3D spatial geographical information. To assess the precision of the 3D visualization integration between hydraulic structure models and 3D terrain models, this study employs mean square error, mean error, and standard deviation as accuracy metrics for the DEM41. The mean square error not only indicates the magnitude of a single error but also signifies the dispersion of the terrain parameters around their true value, as shown in Eq. (1). The mean error effectively reflects whether the error distribution conforms to the normal distribution feature where the mean equals zero, as shown in Eq. (2). Lastly, the standard deviation addresses systematic error that the mean square error might overlook, as shown in Eq. (3). The discrepancy between the actual elevation value and the calculated value is shown in Eq. (4).1 MSE=∑i=1nξi2n,

2 ME=∑i=1nzin,

3 SD=∑i=1n(zi-ME)2n,

4 ξ=Z-z,

where n indicates the number of data points, Z indicates the true value of elevation, and z indicates the calculated value of elevation.

The integration of hydraulic structures and 3D terrains often involves the precise positioning of hydraulic structures relative to terrains, such as monitoring stations and revetment trees. Hydraulic structures play a role in altering the terrains to a certain extent, such as channels and pumping stations. Therefore, the critical evaluation for the precision of 3D scene integration focuses on the accuracy evaluation of the integrated hydraulic structure model. The precision of hydraulic structure models within 3D terrains is typically represented in two aspects: spatial position accuracy and spatial elevation precision.

The precision evaluation of 3D integration involves utilizing the boundary data extracted from the original terrain imagery as true values and calculating the error between the boundary data manifested by the integration and the true values. This serves as a metric for quantifying the accuracy of the 3D integration method. In view of the river model, river midline data must be incorporated simultaneously for comparative analysis. Given the need for 3D integration between multilevel LOD terrain models and river models, the precision analysis of the integration between the river model and terrains is restricted to the same scene at a single level of the LOD terrain model. Give the exclusion of high-precision details displayed during far viewpoints or lower-level LOD terrains, the geometric accuracy of the river must be scrutinized when it exhibits sophisticated model geometries. The data points are confined to 50 boundary data points and 50 midline data points. The height interpolation within boundaries and the elevation of the midline act as the observational values, with the original elevation serving as the true value for calculation.

Results

Model application

To validate the reliability of the dynamic classification integration method for hydraulic structure models and 3D terrain models, using the spatial and attribute data of the Zhuluo Ba Irrigation Area, a visualization system of the virtual geographical environment of the irrigation area is designed and developed. The prototype system is deployed on the B/S architecture created by Alibaba Cloud servers, utilizing MySQL and MapGIS to independently manage and store spatial and attribute data40. The client’s operating system version is Windows 7 or above. The development is conducted using JavaScript as the primary language, utilizing 3Ds Max 2021 software to construct the 3D terrain models and BIM models for hydraulic structure models. Five functional modules, namely, scene sand table, intelligent monitoring, scheduling management, document management, and configuration management, are designed using the OpenLayers framework and the Cesium engine. The core functions include the construction and intelligent monitoring of the virtual geographical environments for the irrigation area, intelligent scheduling management for the irrigation area, and comprehensive statistical inquiry for essential elements in the irrigation area.

Model integration

Point-like hydraulic structures independent of terrain

Point-like hydraulic structures independent of terrain comprise video surveillance stations, solar measuring instruments, and water depth measuring instruments. Figure 7 shows the integrated point-like hydraulic structures independent of terrain of the actual and virtual geographical environments for the irrigation area. The video surveillance station (CXJH0501), solar measuring instrument (SXJH0601), and water depth measuring instrument (WXJH0701) within the actual geographical environments of the irrigation area, as shown in Fig. 7a–c, respectively, are all located at the Xiajia Hu Intake Sluice. Corresponding to these, the video surveillance station, solar measuring instrument, and water depth measuring instrument in the virtual geographical environments of the irrigation area are shown in Fig. 7d–f, respectively.Fig. 7 Integrated point-like hydraulic structures independent of terrain of the actual and virtual geographical environments for the irrigation area: (a) Actual video surveillance station; (b) Actual solar measuring instrument; (c) Actual water depth measuring instrument; (d) Virtual video surveillance station; (e) Virtual solar measuring instrument; (f) Virtual water depth measuring instrument.

Different treatments are given to individual points situated in varying positions within the TIN, thereby enhancing the efficiency and interpolated accuracy compared with traditional uniform interpolation. Concurrently, elevation information at the current LOD level can be acquired in real time based on the changes in the LOD levels, ultimately inserting the individual point into the current scene. Through the dynamic simulation technology of irrigation area visualization with real-time adjustment, API interfaces are utilized to incorporate all sorts of sensor equipment in the irrigation area into well-established servers. With real-time multisensor monitoring data, interactive queries and analyses for spatiotemporal information in the virtual geographical environment of the irrigation area can be performed. By viewing the overall outline and local details of the irrigation area water diversion from multiple perspectives and multiple LOD levels, one can have a real-time understanding of the conditions of the irrigation project, and even issue a warning for sudden conditions within the irrigation area.

Line-like hydraulic structures dependent on terrain

Line-like hydraulic structures dependent on terrain include primary and secondary rivers, main channels, branch channels, and ditches, as seen from a far viewpoint. Taking the primary and secondary rivers as an example, the blue lines in Fig. 8a are the primary and secondary rivers in the virtual geographical environment of the irrigation area, primarily including the Middle Canal (W0101), the Yuejin River (W0102), and the Quxi River (W0103). Figure 8b shows the corresponding surface scene at the local region of Fig. 8a as the viewing point zooms closer.Fig. 8 Integrated line-like hydraulic structures dependent on terrain of virtual geographical environments for the irrigation area: (a) Line scene; (b) Surface scene.

Given the inaccessibility of a linear hydraulic structure’s width within the same scene from a far viewpoint, rivers and channels are represented as linear elements. Concurrently, the linear elements of rivers and channels, together with corresponding terrain points, are simplified. This strategy reduces the redundancy in the modeling data of linear ground objects in the irrigation area and, through the mechanism of caching scheduling, enhances the visualization efficiency once again.

Surface-like hydraulic structures dependent on terrain

Surface-like hydraulic structures dependent on terrain incorporate rivers and channels, as seen from a near viewpoint. The Middle Canal (W0101) and the Xiajia Hu Main Channel (C0101) of the actual geographical environments of the irrigation area are positioned upstream and downstream of the Xiajia Hu Intake Sluice, as shown in Fig. 9a and b, respectively. Figure 9c and d show the corresponding Middle Canal and Xiajia Hu Main Channel of the virtual geographical environments within the irrigation area, respectively.Fig. 9 Integrated surface-like hydraulic structures dependent on terrain of the actual and virtual geographical environments for the irrigation area: (a) Actual Middle Canal; (b) Actual Xiajia Hu Main Channel; (c) Virtual Middle Canal; (d) Virtual Xiajia Hu Main Channel.

The efficiency of the river and channel models is enhanced by simplifying the treatment of river and channel data and rendering smoothly the transition of rivers in the actual terrain for different completeness degrees. Simultaneously, real-time integration display can be performed for terrain models with different LOD levels, and redundant computations are eliminated for post-processing model loading.

Surface-like hydraulic structures independent of terrain

Surface-like hydraulic structures independent of terrain encompass sluices, pumping stations, and land culverts. Figure 10a and b show the Xiajia Hu Intake Sluice (WG0101) and the Zhuoluo Ba Head Pumping Station (PS0101) in the actual geographical environments of the irrigation area, respectively. Figure 10c and d represent the corresponding Xiajia Hu Intake Sluice and Zhuoluo Ba Head Pumping Station in the virtual geographical environments of the irrigation area, respectively.Fig. 10 Integrated surface-like hydraulic structures independent of terrain of the actual and virtual geographical environments for the irrigation area: (a) Actual Xiajia Hu Intake Sluice; (b) Actual Zhuoluo Ba Head Pumping Station; (c) Virtual Xiajia Hu Intake Sluice; (d) Virtual Zhuoluo Ba Head Pumping Station.

By revising existing research, this algorithm has deviated from the traditional methodology of modifying building models to adapt to terrain models. Instead, it adopts an integrated method of modifying the terrain models to accommodate the building models. Different from the conventional method of computing boundary intersections with the TIN, this method directly reconstructs the network between the vertices of the building polygon and the TIN terrains that are not obscured by the polygon. This advanced procedure vastly improves the efficiency of integrating building models with terrain models.

Discussion

Evaluation of model integration accuracy

Several representative ground objects of the irrigation area are selected, the boundary data in remote sensing images serve as true values, and the mean square error between the integrated boundary data and the true value is obtained. Subsequently, the integration accuracy of the 3D terrain models and hydraulic structure models is determined. Particularly, the integration of surface hydraulic structures with irregular boundaries and terrains is the most intricate due to its need to reckon with the undulating terrain variations while exerting a certain degree of reformation upon terrains. Therefore, when performing the accuracy analysis on the integration of rivers and terrains, the river midline data must be incorporated for comparison analysis. Specific methods included the use of the Canny edge detection algorithm and image segmentation techniques (such as threshold segmentation and region growing) to identify and extract the boundaries of water bodies like rivers and lakes. Using image registration techniques to ensure that the 2D images and 3D models were aligned in the same coordinate system, achieving data registration. The experiment indicates that under the TIN terrain, the error in river boundaries is 0.543 m and that in the river midline is 0.672 m. Thus, the precision of the integration of the 3D terrain models and hydraulic structure models is robust for the structure of TIN terrain organization.

The proposed CM-D-LOD method demonstrates its innovation and advantages in several aspects. Compared to existing integration methods, the CM-D-LOD method shows significant improvements in accuracy, efficiency, and applicability. In terms of accuracy, traditional integration methods often rely on global model reconstruction26, which leads to insufficient accuracy when dealing with complex terrains and large hydraulic structures. For example, some studies have reported errors exceeding 1 m when handling complex terrains30. In contrast, our CM-D-LOD method, through classification processing and local TIN reconstruction, controls the error within 0.543 m, significantly enhancing model accuracy. Additionally, in terms of applicability, existing methods have limited applicability under different terrain and climate conditions. For instance, when dealing with mountainous and hilly terrains, the algorithms require extensive adjustments, resulting in poor adaptability37. While the CM-D-LOD method dynamically adjusts LOD levels, better accommodating different terrains and climate conditions, thereby enhancing the method’s generalizability.

Model functionality

In terms of efficiency, traditional methods require global model reconstruction, resulting in long data processing times and high computational complexity33. Ideally, the construction of a virtual geographical environment for each irrigation site should take less than 5 s, a timeframe that is notably swift and meets the demands of practical applications. Integrated within the virtual geographical environments of the irrigation area, the model boasts functions such as perspective transformation, 3D partition, and variable speed navigation capabilities.

Perspective transformation pertains to the transference of third- and first-person perspectives. The first-person perspective is better equipped to simulate an actual observation experience in irrigation areas, enabling irrigation area managers to immerse themselves and perceive and scrutinize the irrigation area environment well, as shown in Fig. 11a. The third-person perspective enables managers to observe the entire environment, obtain a comprehensive view, and accurately evaluate and determine the relative positions and relationships of various elements in the irrigation environment, as shown in Fig. 11b. The combination of these distinct perspectives facilitates access to a thorough visual comprehension of the irrigation environments.Fig. 11 Perspective transformation: (a) First-person perspective; (b) Third-person perspective.

The concept of 3D partition pertains to the slicing of the virtual geographical scenes of irrigation areas along the X, Y, and Z dimensions. This technique can represent the 3D scenes of complex irrigation areas visually, empowering managers to discern the internal structure and components of the models intuitively while gaining an improved understanding of the shapes, sizes, and internal construction of various hydraulic structures. For instance, the Y-dimensional partition of the Xiajia Hu Intake Sluice, as shown in Fig. 12a, offers a detailed view of the specific operational status of the irrigation area and accurately represents the sluice’s closed state. The concept of variable speed navigation encompasses scrutinizing the entire virtual geographical environment of the irrigation area roundly according to prearranged routes. Through this process, speed can be freely adjusted and global observation of the irrigation facilities can be conducted within the irrigation area, inclusive of irrigation channels, pump stations, and sluices. This capability assists managers in examining the integrity, operational status, and maintenance requirements of facilities, promptly identifying issues and initiating repairs and improvements. The variable speed navigation of the Zhuoluo Ba Head Pumping Station, as shown in Fig. 12b, depicts the virtual geographical environments of the Zhuoluo Ba Head Pumping Station at the left side of the route, inclusive of revetment trees and pump stations; the right side showcases the river levee, which includes revetment trees and video surveillance stations. Subsequently, the route executes a turn, continuing to scrutinize the Zhuoluo Ba Head Pumping Station by the prearranged route.Fig. 12 3D partition and variable speed navigation: (a) Y-dimensional partition of the Xiajia Hu Intake Sluice; (b) Variable speed navigation of the Zhuoluo Ba Head Pumping Station.

Integrating BIM technology and hydraulic structure models into 3D terrain models is not limited to visualization; it also has extensive practical applications. For example, in hydraulic modeling for managing input/output processes, integrating 3D terrain models and hydraulic structure models allows for more accurate simulation of water flow paths and flow variations within irrigation systems. This helps optimize the design and management of irrigation systems, ensuring efficient use of water resources. By simulating different irrigation schemes, optimal water flow paths and flow distribution can be determined, thereby reducing water wastage. Additionally, the integrated model can be used for hydraulic analysis to evaluate the effects of different irrigation schemes and predict potential hydraulic issues such as flood risks and water shortages. By simulating flood scenarios, preventive measures can be formulated in advance to protect farmland and infrastructure.

Furthermore, the integrated model can provide decision support for smart irrigation management by analyzing the effects of different irrigation schemes and selecting the optimal scheme to improve irrigation efficiency and conserve water resources. For instance, by analyzing historical weather data and crop water requirements, precise irrigation plans can be formulated. In the event of emergencies (such as floods or droughts), the integrated model can provide emergency response support by simulating the effects of different emergency plans and selecting the optimal plan to minimize disaster losses. For example, by simulating different water resource allocation schemes, water supply can be ensured during drought periods.

Although the primary proposal of this paper is to achieve the visualization of hydraulic structure features in 3D terrain, its applications extend far beyond this. By integrating BIM technology and hydraulic structure models into 3D terrain models, various irrigation problems can be addressed and analyzed, thereby improving the efficiency and reliability of irrigation systems. We believe that this integration method has broad prospects for practical application.

Limitations and future works

Applicability under Different Terrain and Climate Conditions.

Our method has been primarily validated in flat irrigation areas. However, in complex terrains, such as mountainous and hilly regions, more pronounced terrain variations may necessitate algorithm adjustments to accommodate greater elevation changes. Additionally, hydrological characteristics under different climate conditions can affect the design and layout of hydraulic structures. For instance, in arid and rainy regions, the morphology of rivers and channels may differ significantly, requiring corresponding model adjustments.

(2) Different Scales of Irrigation Areas.

In small-scale irrigation areas, the data volume is relatively low, reducing the computational complexity and data processing demands of the model. Our method can be directly applied, though certain steps may be simplified to enhance efficiency. Conversely, in large-scale irrigation areas, the substantial data volume increases computational complexity and data processing requirements, potentially necessitating the optimization of data processing and computational efficiency, including the introduction of parallel computing and distributed processing technologies.

(3) Different Types of Hydraulic Structures.

The types and structures of hydraulic structures may vary across different regions. For example, some areas may have more dams and reservoirs, while others may primarily consist of channels and pumping stations. Our method needs to be adjusted according to the specific types of structures to ensure the accuracy and reliability of the integration.

(4) Expansion of Application Scenarios.

Beyond agricultural irrigation, our method can extend to urban water management systems, including drainage and stormwater management. It is also vital for flood control and water resource management, where precise 3D terrain and hydraulic structure models are essential for predicting flood risks and optimizing water resource allocation. We believe that with suitable adjustments and optimizations, our method can be broadly applied across various study areas and scenarios. Future research will concentrate on the method’s generalizability and adaptability, ensuring its reliability and effectiveness under diverse conditions.

Conclusion

The study proposes a new method called CM-D-LOD to integrate hydraulic structure models with 3D terrain models in irrigation areas. By using a reconstructed triangular network technology, the method accurately merges both models at their boundaries. Through the verification of the actual scene, the proposed integration method accomplishes the seamless integration between 3D terrain models and hydraulic structure models within irrigation areas, conserving the veracity of both models. The virtual geographical environment constructed for the irrigation area exhibits verisimilitude and simulation, thereby further enhancing its navigational efficiency in the daily operation management of the irrigation area. The visual management platform of the virtual geographical environments in irrigation areas facilitates exploration in the digital and intelligent operation of irrigation area projects, thereby optimizing the cost and resources of operation and maintenance. It also serves as a guiding and exemplary role for the information construction of large irrigation areas globally, delivering substantial societal, ecological, and economic benefits.

Although this study has achieved high-precision integration and display of 3D terrain models and hydraulic structure models in the virtual simulation of geographical environments within irrigation areas, the proposed algorithms require point positioning and terrain rendering during the initial loading of each LOD level, resulting in slow loading and display speeds. The second loading can directly access the previously integrated outcomes, resulting in a faster loading speed. Therefore, the initial loading computation must consider advanced visualization methods and parallel calculations to bolster modeling efficiency. These research areas are essential for achieving high-accuracy integration of terrains and ground objects.

Acknowledgements

We are grateful to the funding support from the National Science Foundation of China (NFSC) (42301487); the General Program for Natural Science Research of Basic Disciplines in Universities of Jiangsu Province (23KJD170004); Key Laboratory of Virtual Geographic Environment, Ministry of Education, Open Fund Project (2023VGE03); Jiangsu Provincial Government Scholarship for Studying Abroad; the Nanjing Xiaozhuang University Nature Science High-level Research Project (2022NXY03).

Author contributions

L.H., B.H.: The main writer. L.H., J.C.: Article modifications and funding acquisition. G.M.: Article modifications. H.J.: Image editing. All authors reviewed the manuscript.

Data availability

All data used in the manuscript comply with field standards and they are available. The data that support the findings of this study are available from the corresponding author, [L.H.], upon reasonable request.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally to this work: Liang He and Baoji Han.
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