Rainfall-induced Landslide Susceptibility Prediction Considering Spatial Heterogeneity
ObjectiveLandslides represent a severe and frequent natural hazard, posing significant threats to human life and property. Current models for predicting landslide susceptibility exhibit two primary limitations: the inability to fully capture the spatial heterogeneity of environmental factors such as...
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Editorial Department of Journal of Sichuan University (Engineering Science Edition)
2025-07-01
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| Series: | 工程科学与技术 |
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| Online Access: | http://jsuese.scu.edu.cn/thesisDetails#10.12454/j.jsuese.202400283 |
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| author | ZHANG Xingfu JIANG Yuanjun ABI Erdi |
| author_facet | ZHANG Xingfu JIANG Yuanjun ABI Erdi |
| author_sort | ZHANG Xingfu |
| collection | DOAJ |
| description | ObjectiveLandslides represent a severe and frequent natural hazard, posing significant threats to human life and property. Current models for predicting landslide susceptibility exhibit two primary limitations: the inability to fully capture the spatial heterogeneity of environmental factors such as terrain, soil, and vegetation, and the failure to accurately distinguish between landslides induced by extreme and non-extreme rainfall events. These shortcomings hinder accurate forecasting and reduce the models' adaptability to diverse environmental conditions and rapidly changing climatic patterns. Therefore, this study introduces an innovative approach that combines Deep Embedded Clustering (DEC) with a Dynamic Rainfall Threshold (DRT) model based on a mixed distribution. In addition, a Multi-Task Learning Adaptive Neural Tree (MLANT) model has been developed to enhance model flexibility and prediction accuracy, particularly in varying environmental conditions and during extreme weather events.MethodsThis research applied three key methodologies to address the limitations of existing landslide susceptibility models. Deep Embedded Clustering (DEC): DEC was utilized to resolve spatial heterogeneity issues. Using deep learning techniques, environmental variables such as terrain, soil, and vegetation were embedded into a low-dimensional space to capture their complex, nonlinear relationships. The model clustered these representations to identify sub-regions with similar geological and environmental features. This clustering-based zoning enhanced the model's capability to analyze landslide susceptibility by managing spatial heterogeneity more effectively than traditional methods. Dynamic Rainfall Threshold (DRT) model: The DRT model was introduced to improve forecasting accuracy by distinguishing between the effects of extreme and non-extreme rainfall on landslide initiation. It integrated Gamma and Generalized Pareto Distributions (GPD) to represent rainfall events of varying intensities. Bayesian methods were employed to dynamically update model parameters, enabling real-time adaptation to changing rainfall conditions. This allowed for greater prediction precision and timeliness under both extreme and non-extreme rainfall events. Multi-Task Learning Adaptive Neural Tree (MLANT): The MLANT model was developed to address the inflexibility and limited adaptability of traditional prediction models. Unlike conventional models, which struggled to adjust to diverse prediction tasks and shifting environmental conditions, MLANT dynamically modifies its processing strategies to match specific tasks and environmental variations. This multi-task learning approach enhances the model's accuracy and flexibility in predicting landslides, particularly in nonlinear environments with complex terrain and rapidly changing weather conditions. The models were validated using real-world data from Tongjiang County, a region prone to landslides. Inputs such as terrain features, soil characteristics, vegetation indices, and rainfall patterns were utilized to train and evaluate the models. The DEC, DRT, and MLANT models were compared to traditional susceptibility models to assess improvements in prediction accuracy and adaptability.Results and DiscussionsThe results demonstrated significant improvements in prediction accuracy when using the proposed models. DEC-Based Clustering: The DEC model predicted higher landslide densities in high- and very-high-susceptibility zones by capturing spatial heterogeneity. Specifically, it predicted landslide densities of 0.036 92 and 0.046 92 events per square kilometer in high and very high-risk zones, respectively, identifying a total of 59 landslide events. This marked an improvement over traditional models that did not consider spatial heterogeneity, such as the overall effective rainfall coefficient model, which predicted fewer events and lower densities. These findings highlighted the importance of integrating spatial heterogeneity in susceptibility modeling. Dynamic Rainfall Threshold Model (DRT): The DRT model further enhanced accuracy by effectively distinguishing the effects of different rainfall intensities. Its mixed-distribution approach, incorporating both Gamma and GPD distributions, improved the accuracy of landslide predictions under dynamic climatic conditions. In the practical application to Tongjiang County, the DRT model also predicted 59 landslide events in high and very high susceptibility zones, with densities of 0.036 92 and 0.046 92 events per square kilometer, respectively. This outperformed the DEC-based effective rainfall coefficient model, which exhibited lower accuracy and fewer correctly identified events. MLANT Performance: The MLANT model significantly improved flexibility and prediction performance across varied tasks. MLANT efficiently addressed various environmental conditions by dynamically adjusting its internal strategies, particularly in response to landslides triggered by extreme rainfall. Evaluation metrics, such as precision, F1 score, and ROC-AUC, demonstrated that MLANT outperformed traditional models that rely on static thresholds. In Tongjiang County, MLANT increased predicted landslide density from 0.038 events/km² and 44 events (using traditional methods) to 0.044 events/km² and 59 events, demonstrating superior performance in both frequent and rare landslide scenarios.ConclusionsThe models developed in this research effectively overcome the limitations of traditional landslide susceptibility prediction approaches by integrating Deep Embedded Clustering (DEC), a Dynamic Rainfall Threshold (DRT) model based on mixed distributions, and a Multi-Task Learning Adaptive Neural Tree (MLANT). Spatial heterogeneity is addressed through DEC clustering, which dynamically partitions regions based on environmental characteristics, improving prediction accuracy. Rainfall differentiation is achieved through the DRT model, which significantly improves the model's capability to predict landslides triggered by both extreme and non-extreme rainfall events by adapting to varying climatic conditions. The MLANT model provides flexibility and adaptability, delivering improved accuracy across multiple prediction tasks and in rapidly changing environmental conditions, outperforming traditional single-task models. These findings demonstrate the critical importance and effectiveness of integrating DEC, DRT, and MLANT models in enhancing landslide susceptibility prediction, particularly in areas characterized by complex environmental conditions and frequent extreme weather events. This research contributes both theoretical and practical advancements, providing valuable tools for enhancing landslide risk management and mitigation strategies. |
| format | Article |
| id | doaj-art-a77cf331bb4346d881b60be7781b48c2 |
| institution | DOAJ |
| issn | 2096-3246 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Editorial Department of Journal of Sichuan University (Engineering Science Edition) |
| record_format | Article |
| series | 工程科学与技术 |
| spelling | doaj-art-a77cf331bb4346d881b60be7781b48c22025-08-20T02:48:09ZengEditorial Department of Journal of Sichuan University (Engineering Science Edition)工程科学与技术2096-32462025-07-0157122884271730Rainfall-induced Landslide Susceptibility Prediction Considering Spatial HeterogeneityZHANG XingfuJIANG YuanjunABI ErdiObjectiveLandslides represent a severe and frequent natural hazard, posing significant threats to human life and property. Current models for predicting landslide susceptibility exhibit two primary limitations: the inability to fully capture the spatial heterogeneity of environmental factors such as terrain, soil, and vegetation, and the failure to accurately distinguish between landslides induced by extreme and non-extreme rainfall events. These shortcomings hinder accurate forecasting and reduce the models' adaptability to diverse environmental conditions and rapidly changing climatic patterns. Therefore, this study introduces an innovative approach that combines Deep Embedded Clustering (DEC) with a Dynamic Rainfall Threshold (DRT) model based on a mixed distribution. In addition, a Multi-Task Learning Adaptive Neural Tree (MLANT) model has been developed to enhance model flexibility and prediction accuracy, particularly in varying environmental conditions and during extreme weather events.MethodsThis research applied three key methodologies to address the limitations of existing landslide susceptibility models. Deep Embedded Clustering (DEC): DEC was utilized to resolve spatial heterogeneity issues. Using deep learning techniques, environmental variables such as terrain, soil, and vegetation were embedded into a low-dimensional space to capture their complex, nonlinear relationships. The model clustered these representations to identify sub-regions with similar geological and environmental features. This clustering-based zoning enhanced the model's capability to analyze landslide susceptibility by managing spatial heterogeneity more effectively than traditional methods. Dynamic Rainfall Threshold (DRT) model: The DRT model was introduced to improve forecasting accuracy by distinguishing between the effects of extreme and non-extreme rainfall on landslide initiation. It integrated Gamma and Generalized Pareto Distributions (GPD) to represent rainfall events of varying intensities. Bayesian methods were employed to dynamically update model parameters, enabling real-time adaptation to changing rainfall conditions. This allowed for greater prediction precision and timeliness under both extreme and non-extreme rainfall events. Multi-Task Learning Adaptive Neural Tree (MLANT): The MLANT model was developed to address the inflexibility and limited adaptability of traditional prediction models. Unlike conventional models, which struggled to adjust to diverse prediction tasks and shifting environmental conditions, MLANT dynamically modifies its processing strategies to match specific tasks and environmental variations. This multi-task learning approach enhances the model's accuracy and flexibility in predicting landslides, particularly in nonlinear environments with complex terrain and rapidly changing weather conditions. The models were validated using real-world data from Tongjiang County, a region prone to landslides. Inputs such as terrain features, soil characteristics, vegetation indices, and rainfall patterns were utilized to train and evaluate the models. The DEC, DRT, and MLANT models were compared to traditional susceptibility models to assess improvements in prediction accuracy and adaptability.Results and DiscussionsThe results demonstrated significant improvements in prediction accuracy when using the proposed models. DEC-Based Clustering: The DEC model predicted higher landslide densities in high- and very-high-susceptibility zones by capturing spatial heterogeneity. Specifically, it predicted landslide densities of 0.036 92 and 0.046 92 events per square kilometer in high and very high-risk zones, respectively, identifying a total of 59 landslide events. This marked an improvement over traditional models that did not consider spatial heterogeneity, such as the overall effective rainfall coefficient model, which predicted fewer events and lower densities. These findings highlighted the importance of integrating spatial heterogeneity in susceptibility modeling. Dynamic Rainfall Threshold Model (DRT): The DRT model further enhanced accuracy by effectively distinguishing the effects of different rainfall intensities. Its mixed-distribution approach, incorporating both Gamma and GPD distributions, improved the accuracy of landslide predictions under dynamic climatic conditions. In the practical application to Tongjiang County, the DRT model also predicted 59 landslide events in high and very high susceptibility zones, with densities of 0.036 92 and 0.046 92 events per square kilometer, respectively. This outperformed the DEC-based effective rainfall coefficient model, which exhibited lower accuracy and fewer correctly identified events. MLANT Performance: The MLANT model significantly improved flexibility and prediction performance across varied tasks. MLANT efficiently addressed various environmental conditions by dynamically adjusting its internal strategies, particularly in response to landslides triggered by extreme rainfall. Evaluation metrics, such as precision, F1 score, and ROC-AUC, demonstrated that MLANT outperformed traditional models that rely on static thresholds. In Tongjiang County, MLANT increased predicted landslide density from 0.038 events/km² and 44 events (using traditional methods) to 0.044 events/km² and 59 events, demonstrating superior performance in both frequent and rare landslide scenarios.ConclusionsThe models developed in this research effectively overcome the limitations of traditional landslide susceptibility prediction approaches by integrating Deep Embedded Clustering (DEC), a Dynamic Rainfall Threshold (DRT) model based on mixed distributions, and a Multi-Task Learning Adaptive Neural Tree (MLANT). Spatial heterogeneity is addressed through DEC clustering, which dynamically partitions regions based on environmental characteristics, improving prediction accuracy. Rainfall differentiation is achieved through the DRT model, which significantly improves the model's capability to predict landslides triggered by both extreme and non-extreme rainfall events by adapting to varying climatic conditions. The MLANT model provides flexibility and adaptability, delivering improved accuracy across multiple prediction tasks and in rapidly changing environmental conditions, outperforming traditional single-task models. These findings demonstrate the critical importance and effectiveness of integrating DEC, DRT, and MLANT models in enhancing landslide susceptibility prediction, particularly in areas characterized by complex environmental conditions and frequent extreme weather events. This research contributes both theoretical and practical advancements, providing valuable tools for enhancing landslide risk management and mitigation strategies.http://jsuese.scu.edu.cn/thesisDetails#10.12454/j.jsuese.202400283landslide susceptibilityDeep Embedded Clustering (DEC)Spatial heterogeneityMixed distribution rainfall thresholdMulti-task learning adaptive neural tree model |
| spellingShingle | ZHANG Xingfu JIANG Yuanjun ABI Erdi Rainfall-induced Landslide Susceptibility Prediction Considering Spatial Heterogeneity 工程科学与技术 landslide susceptibility Deep Embedded Clustering (DEC) Spatial heterogeneity Mixed distribution rainfall threshold Multi-task learning adaptive neural tree model |
| title | Rainfall-induced Landslide Susceptibility Prediction Considering Spatial Heterogeneity |
| title_full | Rainfall-induced Landslide Susceptibility Prediction Considering Spatial Heterogeneity |
| title_fullStr | Rainfall-induced Landslide Susceptibility Prediction Considering Spatial Heterogeneity |
| title_full_unstemmed | Rainfall-induced Landslide Susceptibility Prediction Considering Spatial Heterogeneity |
| title_short | Rainfall-induced Landslide Susceptibility Prediction Considering Spatial Heterogeneity |
| title_sort | rainfall induced landslide susceptibility prediction considering spatial heterogeneity |
| topic | landslide susceptibility Deep Embedded Clustering (DEC) Spatial heterogeneity Mixed distribution rainfall threshold Multi-task learning adaptive neural tree model |
| url | http://jsuese.scu.edu.cn/thesisDetails#10.12454/j.jsuese.202400283 |
| work_keys_str_mv | AT zhangxingfu rainfallinducedlandslidesusceptibilitypredictionconsideringspatialheterogeneity AT jiangyuanjun rainfallinducedlandslidesusceptibilitypredictionconsideringspatialheterogeneity AT abierdi rainfallinducedlandslidesusceptibilitypredictionconsideringspatialheterogeneity |