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

39294218
71053
10.1038/s41598-024-71053-7
Article
Susceptibility assessment of multi-hazards using random forest—back propagation neural network coupling model: a Hangzhou city case study
Yu Bofan 12
Xing Huaixue 57670204@qq.com

1
Yan Jiaxing 2
1 grid.452954.b 0000 0004 0368 5009 China Geological Survey Nanjing Center, Nanjing, 210016 People’s Republic of China
2 https://ror.org/04gcegc37 grid.503241.1 0000 0004 1760 9015 China University of Geosciences (Wuhan), The Institute of Geological Survey of China University of Geosciences (Wuhan), Wuhan, 430074 People’s Republic of China
18 9 2024
18 9 2024
2024
14 2178325 6 2024
23 8 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/.
As the demand for regional geological disaster risk assessments in large cities continues to rise, our study selected Hangzhou, one of China’s megacities, as a model to evaluate the susceptibility to two major geological hazards in the region: ground collapse and ground subsidence. Given that susceptibility assessments for such disasters mainly rely on knowledge-driven models, and data-driven models have significant potential for application, we proposed a high-accuracy Random Forest—Back Propagation Neural Network Coupling Model. By using nine evaluation factors selected based on field surveys and expert recommendations, along with disaster data, the model's predictive results indicate a 3–40% improvement in model performance metrics such as AUC, accuracy, precision, recall, and F1-score, compared to single models and traditional SVM and logistic regression models. Ultimately, using the predictive results of this model, we created susceptibility maps for individual disasters and developed a muti-hazards susceptibility map by employing the expert weight discrimination method and the overlay evaluation method. Furthermore, we discussed the feature importance in the prediction process. Our study validated the feasibility of using advanced machine learning models for urban geological disaster assessment, providing a replicable template for other cities.

Subject terms

Natural hazards
Environmental impact
Laboratory of Geological Safety of Underground Space in Coastal Cities, Ministry of Natural ResourcesProject No. BHKF2022Z02 Yan Jiaxing issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

With the continuous expansion of urban construction, the conflict between urban geological disasters and urban development has become increasingly prominent1,2. The prevention and management of geological disasters have thus become critical considerations for every city3. Hangzhou, one of China’s megacities, boasts a population exceeding 10 million and achieved a GDP of $259 billion in 20224. However, due to its location in the Hangjiahu Plain and ongoing human-induced modifications to the geological environment5,6, issues like land subsidence and ground collapse in Hangzhou’s main urban areas have emerged as significant challenges during its development. Specifically, subsidence primarily results from extensive urban development and the prevalent distribution of Quaternary soft soil layers, which are susceptible to compression under structural loads. In contrast, collapses are mainly triggered by the presence of artificial fill and the failure of underground pipelines. Like many cities, Hangzhou has started to undertake regional geological disaster assessments to ensure urban geological safety. The assessment and prediction of these disasters necessitate susceptibility evaluation, as it reflects geological conditions and enables reasonable zoning of the study area7–10. However, assessments based solely on single disaster types might not accurately represent the geological safety of an area, especially as more cities begin to emphasize the overall impact of regional geological issues. Therefore, comprehensive evaluations encompassing multiple types of disasters, or multi-hazards, are commonly employed11–13. Current assessments, especially those concerning subsidence and collapse, primarily rely on knowledge-driven models such as the Analytic Hierarchy Process (AHP), which entails subjective scoring of selected indicators to determine their weights for susceptibility analysis14–16, Particularly for collapse-related disasters, which occur over small areas, machine learning-based susceptibility assessments of such disasters are, to our knowledge, currently an unexplored area. Meanwhile, in the context of comprehensive disaster evaluations, experts commonly employ AHP to score and determine weights17–19, when assessing the relative weights of different types of disasters within a specific area, the lack of learning samples means that reliance on expert judgment is the only viable option.

With advancements in technology and the growing complexity of geological disaster conditions, the limitations of the AHP are becoming increasingly apparent20. Its reliance on expert judgment for weighting factors is inherently subjective and can be influenced by factors such as emotions, fatigue, among others, particularly with numerous evaluation criteria. Consequently, researchers are increasingly turning to data-driven models. Unlike traditional models, data-driven models are capable of handling more complex datasets, thereby improving prediction accuracy. They have been extensively applied in evaluating susceptibility to large-scale geological disasters, such as landslides and earthquakes21–25. Recently, the integration of coupling machine learning models, such as the combination of BP neural networks with SVM models, has demonstrated potential in optimizing predictions by leveraging each model’s strengths and mitigating single-model flaws, thus achieving higher accuracy in case studies26,27. However, there is still significant potential for development in the field of ensemble models, particularly in urban geological disasters. When facing datasets that include complex geological environments and human activity factors, enhancing the predictive performance of models is crucial for the accuracy of prediction results.

Our study assesses the susceptibility of multi-hazards, including ground subsidence and ground collapse, in commonly filled and silty areas in Hangzhou by employing Random Forest - Back Propagation Neural Network coupling model. We explored the feasibility of utilizing coupled models over single models to enhance evaluation effectiveness, particularly providing a viable template for applying machine learning to small-scale, collapse-type disasters. Our research flowchart is illustrated in Fig. 1.Fig. 1 Research flowchart.

Materials and methodology

Selection of the study area

The silty clay soil in Hangzhou City is primarily distributed in the plain areas. Due to the topographic and geomorphological characteristics, the plain areas with accumulative, alluvial, and marine plains are the most typical regions. Considering the comprehensive disaster situation, this paper selects the filled soil-silty clay typical area along the south bank of the Qiantang River in Binjiang District as the study area. Covering an area of 31,970,000m2 (31.97 km2), it includes urban arterials such as subways, expressways, and elevated roads, marking it as a region in Hangzhou City with significant human activity modifications. The study area is located along the western edge of the Xiaoshao Plain, north to the Hangzhou duplex hill, and south to the Puyang River Plain. The terrain is dominated by plains with a few low hills, featuring monotonous geomorphological types with clear boundaries, flat terrain, a dense river network, and mainly consists of sandy silty soil and silt, with the sand and gravel layer buried at a depth of about 35–50 m. Human activities have profoundly affected the area, with high-rise buildings typically using sand-gravel layers or bedrock as the bearing stratum. The sediments mainly consist of gray to dark gray silty mud clay and silty fine clay, characterized by distinct horizontal stratification and high-water content. The Quaternary strata mainly belong to the lower part of the Holocene, formed during the early Fuyang marine transgression, primarily through flood alluviation, often appearing as river valley plains, river terraces, and other landforms. The strata are composed of sand and gravel, with good sorting and rounding, relatively loose structure, and a thickness of 2–12 m. The geographical location of the study area is shown in Fig. 2.Fig. 2 Location of the study area (This figure was created using ArcGIS Pro software. Desktop GIS Software | Mapping Analytics | ArcGIS Pro (esri.com)) (Map image is the intellectual property of Esri and is used herein under license.

Copyright © 2020 Esri and its licensors. All rights reserved. Esri, NASA, NGA, USGS, Sources: Esri, USGS, Esri, © OpenStreetMap contributors, TomTom, Garmin, FAO, NOAA, USGS.)

Selection of evaluation factors

Considering the complexity of the urban geological environment and the unique characteristics of the study area, it is imperative to select evaluation factors that comprehensively consider the intensity of human activities in the area and conduct in-depth analyses in conjunction with geological conditions and cultural characteristics. To determine which influencing factors are most closely associated with ground collapse and subsidence in Hangzhou, we collaborated with experts from the Hangzhou urban geological safety assessment at the Zhejiang Geological Survey.

Based on field inspections and expert analyses, ground collapses in the study area are primarily attributed to the following factors: geological conditions such as artificial fill, sandy loam, and concealed ditches; deficiencies in underground drainage pipe structures; hydrogeological influences; and disruptions caused by human engineering activities. Meanwhile, ground subsidence is mainly due to the study area being in a plain, with the distribution of soft soil layers, human activities, and hydrological factors being the primary reasons for ground subsidence occurrences. Given these factors, we identified 9 key factors as preliminary evaluation indicators and conducted a correlation analysis to prevent issues such as overfitting or frequent misjudgments in the machine learning process, as depicted in the correlation heatmap (Fig. 3). The evaluation factors used for the disaster, data sources, and data attributes are detailed in Table 1 and Fig. 4.Fig. 3 Correlation heatmap of evaluation factors.

Table 1 Evaluation factors information.

Factors	Disasters	Methods of acquisition	file type	
Groundwater richness	Ground subsidence	Zhejiang Geological Survey	Shapefile	
Burial depth of the top layer of saturated silty sand soil	Ground collapse	China Geology Survey, Nanjing Center	Raster Cell Size (X, Y) (290,290)	
Burial depth of the underground confined water level	Both	China Geology Survey, Nanjing Center	Shapefile	
The thickness of the surface fill layer	Ground collapse	China Geology Survey, Nanjing Center	Raster Cell Size (X, Y) (32,32)	
Rainfall (weekly) (2022)	Both	Zhejiang Geological Survey	Raster Cell Size (X, Y) (32,32)	
The thickness of soft soil layer	Both	China Geology Survey, Nanjing Center	Raster Cell Size (X, Y) (400,400)	
Ground subsidence rate (2022)	Ground subsidence	Zhejiang Geological Survey	Raster Cell Size (X, Y) (520,520)	
The density of the drainage pipe network	Both	Zhejiang Geological Survey	Raster Cell Size (X, Y) (30,30)	
Distance to underground rivers and blind ditches	Ground collapse	Zhejiang Geological Survey	Shapefile	

Fig. 4 Assessment factors used in our research.

Random forest—back propagation neural network coupling model

To further enhance model accuracy and ensure that when predicting ground collapse disasters, the small scope of disaster areas leading to fewer data points does not impact the prediction results, we propose the Random Forest - Back Propagation Neural Network coupling model. Random Forest represents a collaborative approach in machine learning, comprising numerous decision trees working in unison28,29. Each tree receives a randomized subset of data and features, enhancing the model’s overall accuracy and versatility. This approach excels at managing a diverse array of inputs and discerning obscure patterns. The operation of Random Forest can be concisely expressed using the following equations to enhance the understanding of its ensemble methodology:1 VarianceReduction=Var(S)-SleftSVarSleft+SrightSVarSright

where Var(S) represents the variance of the target variable in the entire dataset S, |Sleft| and |Sright| denote the number of samples in the left and right subsets post-split, respectively. Equation (1) highlights how Random Forest effectively reduces overfitting by incorporating diverse data samples.2 GS=1-∑i=1npi2

where pi2 indicates the proportion of the samples in set S that belong to class i, and n is the total number of classes. This measure is critical in determining the best split at each node within the trees. The Eq. (2) not only allows Random Forest to handle a variety of input types effectively but also enhances its capability to detect subtle patterns in complex datasets.

Conversely, the Backpropagation Neural Network is a pivotal component in machine learning, particularly adept at complex tasks that defy linear analysis30,31. It comprises multiple layers and enhances its predictive capability through iterative adjustments of its strategy (weights and biases). To further elucidate the learning process within a Backpropagation Neural Network, the following equations can be considered:3 Wijnew=Wijold-η∂L∂Wij

where Wijold and Wijnew are the old and new values of the weight between nodes i and j, η is the learning rate, and ∂L∂Wij represents the gradient of the loss function L with respect to the weight Wij Eq. (3) delineates the core mechanism by which the network learns by iteratively adjusting its weights.4 σx=11+e-x

where σx is the sigmoid activation function, which normalizes the input x into an output range between 0 and 1, enabling the network to handle non-linear relationships within the data. Equation (4) illustrates how the sigmoid activation function enables the neural network to transform linear inputs into outputs bounded between 0 and 1, facilitating the handling of probabilistic decisions and non-linear complexities.

To optimize our Random Forest Classifier, we conducted a grid search exploring various hyperparameters32, including the number of trees (ranging from 50 to 200 in increments of 50) and max features (options including 'auto', 'sqrt', and 'log2'). The optimal configuration was determined to be 100 trees, ensuring repeatability with a fixed random seed of 42. Similarly, for the Neural Network Classifier, our grid search covered different network architectures, focusing on varying the number of neurons in the hidden layers (options including 50, 100, and 150 for the first layer and 25, 50, and 75 for the second layer) and the maximum number of iterations (500, 1000, and 1500 iterations were tested). The most effective configuration found consists of two hidden layers with 100 and 50 neurons respectively, with a maximum of 1000 iterations, again using the random seed of 42 to maintain consistency. Each set of parameters was evaluated using 5-fold cross-validation33, ensuring that our selection process was robust and the results reliable. This comprehensive approach to hyperparameter tuning enhances the individual models' predictive accuracy before integrating them into the Stacking Classifier. The Stacking Classifier utilizes these two models as base models and integrates their predictions using another Random Forest configured with 100 trees as the final meta-model to make the ultimate decision. Subsequently, the Backpropagation Neural Network refines the analysis with its specialized capabilities. Together, they constitute a robust model adept at assimilating extensive information, distilling it, and producing precise predictions. The principle of the ensemble model is depicted in Fig. 5.Fig. 5 Schematic diagram of RF-BP neural network coupling model structure.

Evaluation and analysis

Model performance analysis

In this study, we selected 27898 data points using the "Raster to Point" function in ArcGIS, the sample size was determined by dividing the study area and evaluation factors into 32m x 32m pixels. Under these sample conditions, the dataset maintained relative balance, the computation time was reasonable, and subsequent model evaluation metrics also demonstrated the high accuracy of the model predictions. For ground subsidence susceptibility evaluation, susceptible areas were delineated using regional cumulative subsidence data. To ensure model training accuracy, during the ground subsidence susceptibility evaluation, 70% of the total data were randomly extracted for the training set using Python software. For ground collapse susceptibility evaluation, a 1:1 ratio of ground collapse disaster points to non-disaster points was employed to segment the training set (with disaster points labeled as 1 and non-disaster points as 0). Due to the limited scope of collapse areas, we obtained a total of 300 collapse data points, from which we randomly selected 210 collapse points and 210 non-collapse points to serve as the training set. This balanced approach helps to ensure that the model is not biased towards the more prevalent non-collapse conditions. The trained model was subsequently used to predict the remaining points, providing the probability of ground collapse for each pixel (ranging from 0 to 1) and the predicted cumulative subsidence value for each pixel.

To further demonstrate that our Random Forest-Back Propagation Neural Network Coupling Model outperforms individual models and ensures optimal results for susceptibility mapping, we evaluated the model's performance using multiple metrics. Since ground collapse prediction is a binary classification problem in our multi-hazard assessment, we primarily used metrics including ROC curves, AUC values, Accuracy, Precision, Recall, and F1-score.

The AUC (Area Under the ROC Curve) represents the model's overall performance across all classification thresholds, with a higher AUC indicating superior classification capability34. Accuracy measures the proportion of correctly predicted instances (true positives and true negatives) out of the total instances, reflecting the model's general effectiveness. Precision evaluates the proportion of true positive predictions among all predicted positives, indicating the model's reliability in predicting positive instances. Recall assesses the proportion of actual positive instances correctly identified by the model, demonstrating its ability to capture positive cases. The F1-score, which is the harmonic mean of precision and recall, provides a balanced measure of both metrics, especially useful for imbalanced datasets. Higher values for these metrics signify better model performance on the given dataset35.

Results showed that the stacked model outperformed others in all metrics in ground collapse, as illustrated in Fig. 6a and b. Following the validation of the stacked model's performance, we employed it to predict susceptibility to non-binary tasks of ground subsidence. The results yielded high AUC values: 0.99 for less than 24mm (low susceptibility), 0.99 for 24mm-64mm (moderate susceptibility), 0.97 for 64mm-96mm (high susceptibility), and 0.99 for 96mm-128mm (very high susceptibility), indicating the model’s high predictive accuracy. The average AUC value of over 0.98 confirms the model's accuracy in predicting ground subsidence in our study and underscores its potential application for non-binary classification tasks. The specific ROC curves and AUC values can be seen in Fig. 6c.Fig. 6 (a) The ROC curves and AUC values in the ground collapse prediction task; (b) The Accuracy, Precision, Recall, and F1-score in the ground collapse prediction task; (c) The ROC curves and AUC values of the stacking model in the ground subsidence prediction task.

Susceptibility analysis

After the model’s prediction was completed, the RF-BP Neural Network Model’s results were used as a benchmark to import all grid points into ArcGIS 10.8 for evaluating the susceptibility zones of ground collapses and ground subsidence. First, regarding single-hazard susceptibility maps, for ground collapse, the probability of occurrence in the study area (ranging from 0 to 1) was reclassified using the equal interval method. This led to its division into four categories: low susceptibility (0–0.25), medium susceptibility (0.25–0.5), high susceptibility (0.5–0.75), and very high susceptibility (0.75–1). Specifically, Fig. 7a shows the susceptibility map of ground collapses in the study area under this classification, and Table 2 details the specific susceptibility area, its percentage of the total area, and the distribution of disaster cases in the study area. For ground subsidence, the model’s predicted subsidence map was compared with the actual monitored cumulative subsidence map. Susceptibility areas based on cumulative subsidence were categorized as low (0–24 mm), medium (24 mm–64 mm), high (64 mm–96 mm), and very high (96 mm–128 mm) susceptibility areas. Subsequently, the overlap degree was calculated using the grid pixel data. Statistically, the overlap between the actual and the model-simulated cumulative subsidence was 90.31%, with specific overlap degrees for each interval detailed in Table 3. This also reflects the accuracy of the stacking model. Figure 7b shows the susceptibility map of ground subsidence in the study area.Fig. 7 (a) Susceptibility map of ground collapses; (b) Susceptibility map of ground subsidence.

Table 2 Susceptibility area for ground collapse disasters (Evaluated by Raster Cell Size (X, Y) (32,32).

Susceptibility level	Area of the Zone/km2	Percentage of total area/%	Number of ground subsidence disaster points	Percentage of total disaster points/%	
Low susceptibility zone	14.16	44.31	0	0	
Moderate susceptibility zone	7.33	22.94	1	7.41	
High susceptibility zone	4.59	14.35	1	7.41	
Very high susceptibility zone	5.89	18.40	12	85.71	

Table 3 Comparison of overlap degrees across different ground subsidence intervals.

Ground cumulative settlement	Actual monitoring results (points)	Model prediction results (points)	Overlap degree (%)	
0 mm–24 mm	9069	10,157	89.29	
24 mm–64 mm	11,064	11,437	96.74	
64 mm–96 mm	5090	4376	85.97	
96 mm–128 mm	6902	6161	89.26	

For the comprehensive susceptibility map of hazards, we employed two approaches. First, three experts from the Zhejiang Geological Survey were invited to reference the basic data of the two primary hazards affecting the study area, ground collapse and ground subsidence. They combined this information with their understanding of the study area, using the Analytic Hierarchy Process (AHP) to determine the impact magnitude (weights) of these two hazards on the study area. This process entails decomposing decision-making elements into hierarchies, including objectives, criteria, and alternatives, followed by qualitative and quantitative analysis to determine the weights of these disasters in the study area. The objective layer in the judgment process focused on the weight values of ground collapse and subsidence, while the criteria layer comprised ten indicators, including the impacts on the economy and human safety, relationship with geological conditions, current monitoring and management status, susceptibility assessment results, and causes and probabilities of occurrence. Of these, the first five indicators were weight determinants for ground collapse, and the latter five pertained to ground subsidence. Finally, by summing the weights of the disaster indicators for ground collapse and subsidence, the overall weight of the disaster was ascertained, leading to an aggregate judgment on the weights of multi-hazard indicators. The specific AHP judgment table and the results are shown in Table 4 and Fig. 8. Table 4 Results of AHP judgment.

Indicator	Eigenvector	Weight value(%)	Total weight(%)	
Ground collapse	The impact on the economy and human safety	1.477	14.772	38.768	
Geological conditions of the disaster research area	0.833	8.331	
Current status of monitoring and management	0.519	5.195	
Results of susceptibility assessment	0.553	5.532	
Causes and Likelihood disaster occurrence	0.494	4.938	
Ground subsidence	The impact on the economy and human safety	2.075	20.753	61.232	
Geological conditions of the disaster research area	1.222	12.217	
Current status of monitoring and management	0.988	9.883	
Results of susceptibility assessment	1.1	11.001	
Causes and Likelihood disaster occurrence	0.738	7.378	

Fig. 8 (a) The result of AHP assessment; (b) Comprehensive susceptibility map based on the AHP assessment. (This figure was created using ArcGIS Pro software. Desktop GIS Software|Mapping Analytics|ArcGIS Pro (esri.com)).

Second, we adopted a common overlay evaluation method used in multi-hazard susceptibility assessments36,37. This method involves overlaying susceptibility maps of individual hazards to determine the risk of different areas experiencing various hazards. For example, it helps to identify regions at high risk for a single hazard, areas at high risk for multiple hazards simultaneously, or areas where multiple hazards are unlikely to occur. Although this overlay method is relatively simple, it greatly reduces subjective judgments and, assuming the individual hazard susceptibility maps are accurate, provides highly valuable reference data. We selected areas with high and very high susceptibility levels from the four susceptibility categories. In Fig. 9, the three colors represent areas in the study region with high risks of ground subsidence, ground collapse, and both disasters simultaneously. The remaining areas represent regions with low risk for both types of disasters.Fig. 9 Comprehensive susceptibility map using overlay evaluation method. (This figure was created using ArcGIS Pro software. Desktop GIS Software | Mapping Analytics | ArcGIS Pro (esri.com)) (Map image is the intellectual property of Esri and is used herein under license.

Copyright © 2020 Esri and its licensors. All rights reserved. Esri, NASA, NGA, USGS, Sources: Esri, USGS, Esri, © OpenStreetMap contributors, TomTom, Garmin, FAO, NOAA, USGS.)

Discussion

Prediction optimization

In the process of assessing regional disaster susceptibility using machine learning models, optimization is essential to achieve more accurate predictions. In our study, we focused on two main aspects of optimization: first, we employed grid search to obtain the optimal hyperparameters for individual models, ensuring the best performance of the stacked model; second, we optimized the dataset by using multi-source data and selecting an appropriate pixel size (32 m*32 m) to ensure that the dataset, particularly for the binary classification task of ground subsidence prediction, was not extremely imbalanced between disaster and non-disaster points.

Although we adopted reasonable methods to achieve good predictive accuracy, we acknowledge there is still room for improvement. For instance, we could explore different data partition ratios. In our case, we used a 70%:30% ratio for the training and test sets. While this ratio is widely used in many established cases of geological hazard susceptibility mapping, other ratios, such as 80%:20%38, might be more optimal for different scenarios. Also, Sameen39 proposed a systematic subdivision method that captures training samples well-representative of the entire dataset. By subdividing the training/testing set based on Hellinger distance and applying a minimization method to reduce feature interdependence, they successfully enhanced the predictive capability of landslide susceptibility models.

Additionally, considering more multi-source data and evaluation metrics can improve the assessment results and increase the credibility of model predictions. In our predictions of ground subsidence and ground settlement susceptibility, we selected 7 and 6 evaluation metrics, respectively, based on field investigations and expert discussions. While these were reasonable choices, future work could include more metrics to enhance model robustness. Finally, for model hyperparameter optimization, we chose the commonly used grid search method due to the relatively simple nature of our dataset, leading to satisfactory optimization results. However, when considering more evaluation metrics and additional sample points, evaluating and quantifying model uncertainty40 or opting for more efficient search methods, such as ant colony clustering algorithm in random search41 or Bayesian optimization42, can yield better optimization results.

Prediction interpretability

The "black box" effect of machine learning models often leads to their predictive results not being directly trusted by urban decision-makers. Understanding the influence of data on model decisions and the model judgment process has become increasingly important among scholars. In our model, we used a Random Forest (RF) model, which acts as the final meta-model, to assess feature importance, specifically, feature importance is derived by calculating each feature's cumulative contribution to impurity reduction across multiple decision trees, then averaging and standardizing these contributions. As shown in Fig. 10, in predicting ground subsidence, the thickness of the surface fill layer and rainfall (weekly) (2022) contribute significantly. In predicting ground settlement, aside from the density of the drainage pipe network and burial depth of the underground confined water level, other factors also have notable contributions.Fig. 10 Feature importance in predicting ground subsidence and collapse.

While our method provides the contribution of each evaluation factor to the model output, this explanation might not be comprehensive. Recently, SHAP (SHapley Additive exPlanations), which uses Shapley values from game theory to analyze and illustrate the impact of each input feature on model decisions, has become a popular choice among scholars43,44. However, its application in ensemble models, especially those combining different types of models, has not yet been reliably verified. This is an area that we may need to consider in future research. Additionally, integrating physical models with machine learning models to evaluate disaster mechanisms, rather than solely focusing on susceptibility assessment, often yields more convincing results45,46.

Conclusion

This study focused on a typical plain area in Hangzhou affected by urban geological hazards, creating susceptibility maps for the primary geological hazards in the study area: ground subsidence and ground collapse. The specific conclusions are as follows:To obtain reliable and reasonable susceptibility assessment results, we conducted field investigations and discussions with the Zhejiang Geological Survey, and selected nine evaluation factors with low correlation coefficients, including seven for ground collapse and six for ground subsidence. Then we used a pixel size of 32 m × 32 m, with 27,898 data samples for model prediction. After optimizing the model using the grid search method, we obtained an optimized Random Forest—Back Propagation Neural Network Coupling Model. By introducing single models and comparing with SVM and logistic regression models, the results showed that the ensemble model improved the AUC, Accuracy, Precision, Recall, and F1-score by 3% to 40% in the binary classification task of ground collapse prediction. For the non-binary task of ground subsidence prediction, the average AUC value reached 0.98.

To achieve the highest accuracy in susceptibility mapping results for the study area, we ultimately selected the best-performing stacked model's prediction results to create susceptibility maps for ground collapse and ground subsidence. To further evaluate the overall hazard situation in the study area, we sought assistance from three experts from the Zhejiang Geological Survey. Using the AHP method, the study assessed the impact weights of ground collapse and ground subsidence on the study area and created a comprehensive hazard susceptibility map. Additionally, we used the traditional overlay method to objectively determine the hazard risk in different areas. After completing the mapping, we investigated the feature importance during the prediction process. The results indicated that for ground collapse prediction, the thickness of the surface fill layer and weekly rainfall (2022) were the most significant contributors. For ground subsidence prediction, besides the density of the drainage pipe network and burial depth of the underground confined water level, other factors showed significant contributions.

Acknowledgements

This research is supported by the China Geological Survey, Nanjing Center, Zhejiang Geological Survey and China University of Geosciences, Wuhan. The work described in this paper was funded by Laboratory of Geological Safety of Underground Space in Coastal Cities, Ministry of Natural Resources (Project No. BHKF2022Z02), and the China Geological Survey, Nanjing Center (Project No.DD20190281).

Author contributions

Bofan Yu: Conceptualization, Investigation, Methodology, Software, Writing—original draft, Writing—review & editing Jiaxing Yan: software, validation Huaixue Xing: Resources, Project administration, Funding acquisition.

Data availability

All data generated or analyzed during this study are included in this published article.

Code availability

Name of the code/library: RF-BP Neural Network Coupling Model Contact:1378747279@qq.com. Hardware requirements: Processor: A multi-core processor is recommended for efficient running of machine learning models. Memory: At least 8 GB RAM is advised for effective processing of the datasets and machine learning models. Storage Space: Sufficient storage space to accommodate the datasets, model files, and output files. Program language: Python. Software required: Python Interpreter, preferably version 3.8 or higher. Python Libraries: pandas, scikit-learn, joblib, matplotlib. Excel or a compatible software for reading and writing Excel files. Program size: code:4 KB data:818 KB model:5463 KB. The source codes are available for downloading at the link: https://github.com/xygbb/RF-BP-Neural-Network-Coupling- Model2.git.

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.
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