A MaxEnt-TRIGRS hybrid model with dynamic safety factor mapping for enhanced debris flow susceptibility assessment in rainfall-triggered terrains
Abstract Traditional statistical models for debris-flow susceptibility often overlook critical triggering mechanisms and geotechnical parameters. To address this, we propose an innovative framework that couples the Maximum Entropy (MaxEnt) statistical model with the TRIGRS physical model, which simu...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-11284-4 |
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| author | Xinlong Xu Yue Qiang Li Li Siyu Liang Tao Chen Wenjun Yang Xinyi Tan Xi Wang He Yang |
| author_facet | Xinlong Xu Yue Qiang Li Li Siyu Liang Tao Chen Wenjun Yang Xinyi Tan Xi Wang He Yang |
| author_sort | Xinlong Xu |
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| description | Abstract Traditional statistical models for debris-flow susceptibility often overlook critical triggering mechanisms and geotechnical parameters. To address this, we propose an innovative framework that couples the Maximum Entropy (MaxEnt) statistical model with the TRIGRS physical model, which simulates transient rainfall infiltration and grid-based regional slope stability. Focusing on seven towns in Beichuan County, China, we integrated thirteen environmental factors, geotechnical parameters, and historical hazard records to build a dual-driven “statistical–physical” evaluation framework. Our methodology consists of three steps: (1) Use TRIGRS to compute rainfall-induced safety factors (FS) and identify unstable zones (FS < 1), which serve as the positive-sample database for MaxEnt; (2) Employ the MaxEnt model—using the TRIGRS-derived positive samples and historical debris-flow factors—to predict the spatial distribution of susceptibility; (3) Integrate both outputs spatially in GIS using dynamic weighting. Validation shows that the hybrid model improves prediction accuracy by 21% compared to MaxEnt alone (AUC = 0.845). Its susceptibility map corrects 34.7% of the overpredicted areas from the statistical model and enlarges stable zones by 1.8 times. Additionally, to determine the optimal weighting between machine learning and the physical model, we tested three weight combinations and found that a 0.55:0.45 ratio (MaxEnt: TRIGRS) yields the best performance. Using an independent validation set from another study area, we correctly identified 83.6% of the historical debris-flow events in Changtan, demonstrating the framework’s ability to integrate geostatistical patterns with geomechanical processes. This coupled framework offers a paradigm for multi‐hazard chain assessment in complex terrain and can be directly applied to debris-flow early warning and regional disaster mitigation planning. |
| format | Article |
| id | doaj-art-39540e1fe1714d27853b4dcddb15a8cb |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-39540e1fe1714d27853b4dcddb15a8cb2025-08-20T03:45:56ZengNature PortfolioScientific Reports2045-23222025-07-0115112210.1038/s41598-025-11284-4A MaxEnt-TRIGRS hybrid model with dynamic safety factor mapping for enhanced debris flow susceptibility assessment in rainfall-triggered terrainsXinlong Xu0Yue Qiang1Li Li2Siyu Liang3Tao Chen4Wenjun Yang5Xinyi Tan6Xi Wang7He Yang8Civil Engineering College, Chongqing Three Gorges UniversityCivil Engineering College, Chongqing Three Gorges UniversityCivil Engineering College, Chongqing Three Gorges UniversityCivil Engineering College, Chongqing Three Gorges UniversityCivil Engineering College, Chongqing Three Gorges UniversityCivil Engineering College, Chongqing Three Gorges UniversityCivil Engineering College, Chongqing Three Gorges UniversityCivil Engineering College, Chongqing Three Gorges UniversityKey Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education, Chongqing Jiaotong UniversityAbstract Traditional statistical models for debris-flow susceptibility often overlook critical triggering mechanisms and geotechnical parameters. To address this, we propose an innovative framework that couples the Maximum Entropy (MaxEnt) statistical model with the TRIGRS physical model, which simulates transient rainfall infiltration and grid-based regional slope stability. Focusing on seven towns in Beichuan County, China, we integrated thirteen environmental factors, geotechnical parameters, and historical hazard records to build a dual-driven “statistical–physical” evaluation framework. Our methodology consists of three steps: (1) Use TRIGRS to compute rainfall-induced safety factors (FS) and identify unstable zones (FS < 1), which serve as the positive-sample database for MaxEnt; (2) Employ the MaxEnt model—using the TRIGRS-derived positive samples and historical debris-flow factors—to predict the spatial distribution of susceptibility; (3) Integrate both outputs spatially in GIS using dynamic weighting. Validation shows that the hybrid model improves prediction accuracy by 21% compared to MaxEnt alone (AUC = 0.845). Its susceptibility map corrects 34.7% of the overpredicted areas from the statistical model and enlarges stable zones by 1.8 times. Additionally, to determine the optimal weighting between machine learning and the physical model, we tested three weight combinations and found that a 0.55:0.45 ratio (MaxEnt: TRIGRS) yields the best performance. Using an independent validation set from another study area, we correctly identified 83.6% of the historical debris-flow events in Changtan, demonstrating the framework’s ability to integrate geostatistical patterns with geomechanical processes. This coupled framework offers a paradigm for multi‐hazard chain assessment in complex terrain and can be directly applied to debris-flow early warning and regional disaster mitigation planning.https://doi.org/10.1038/s41598-025-11284-4Integrating modelMachine learning modelPhysical modelDebris flow susceptibility mappingBeichuan country |
| spellingShingle | Xinlong Xu Yue Qiang Li Li Siyu Liang Tao Chen Wenjun Yang Xinyi Tan Xi Wang He Yang A MaxEnt-TRIGRS hybrid model with dynamic safety factor mapping for enhanced debris flow susceptibility assessment in rainfall-triggered terrains Scientific Reports Integrating model Machine learning model Physical model Debris flow susceptibility mapping Beichuan country |
| title | A MaxEnt-TRIGRS hybrid model with dynamic safety factor mapping for enhanced debris flow susceptibility assessment in rainfall-triggered terrains |
| title_full | A MaxEnt-TRIGRS hybrid model with dynamic safety factor mapping for enhanced debris flow susceptibility assessment in rainfall-triggered terrains |
| title_fullStr | A MaxEnt-TRIGRS hybrid model with dynamic safety factor mapping for enhanced debris flow susceptibility assessment in rainfall-triggered terrains |
| title_full_unstemmed | A MaxEnt-TRIGRS hybrid model with dynamic safety factor mapping for enhanced debris flow susceptibility assessment in rainfall-triggered terrains |
| title_short | A MaxEnt-TRIGRS hybrid model with dynamic safety factor mapping for enhanced debris flow susceptibility assessment in rainfall-triggered terrains |
| title_sort | maxent trigrs hybrid model with dynamic safety factor mapping for enhanced debris flow susceptibility assessment in rainfall triggered terrains |
| topic | Integrating model Machine learning model Physical model Debris flow susceptibility mapping Beichuan country |
| url | https://doi.org/10.1038/s41598-025-11284-4 |
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