Enhancing landslide disaster prediction by evaluating non landslide area sampling in machine learning models for Spiti Valley India
Abstract Landslides are well-known natural or quasi-natural hazards which can harm human lives, infrastructure, and the environment, particularly in mountainous regions like Spiti Valley, Himachal Pradesh, India. The Spiti Valley in Himachal Pradesh, India, is prone to landslides due to its fragile...
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Nature Portfolio
2025-04-01
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| Online Access: | https://doi.org/10.1038/s41598-025-95087-7 |
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| author | Devraj Dhakal Kanwarpreet Singh Kennedy C. Onyelowe Steven Alejandro Salazar Cazco Abhishek Sharma Nassir Alarifi Fakhrul Islam Randeep Krishna Prakash Arunachalam Youssef M. Youssef |
| author_facet | Devraj Dhakal Kanwarpreet Singh Kennedy C. Onyelowe Steven Alejandro Salazar Cazco Abhishek Sharma Nassir Alarifi Fakhrul Islam Randeep Krishna Prakash Arunachalam Youssef M. Youssef |
| author_sort | Devraj Dhakal |
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| description | Abstract Landslides are well-known natural or quasi-natural hazards which can harm human lives, infrastructure, and the environment, particularly in mountainous regions like Spiti Valley, Himachal Pradesh, India. The Spiti Valley in Himachal Pradesh, India, is prone to landslides due to its fragile geology, steep slopes, and unpredictable climate. These dangers threaten life, infrastructure, and the environment, requiring comprehensive landslide susceptibility mapping (LSM) for mitigation and planning. Advanced machine learning and new sampling procedures may improve LSM accuracy and reliability in high-risk zones. This investigation aims to enhance LSM through a systematic evaluation of innovative sampling methodologies for non-landslide areas. This study focuses on assessing the effectiveness of two novel sampling methods: Buffer Zone Safe Points (BZSP) and Slope Buffer Safe Points (SBSP). Detailed susceptibility zonation maps were created employing advanced statistical techniques, specifically Extreme Gradient Boosting (XGBoost), Random Forest (RF), and K-Nearest Neighbors (KNN), allowing for an in-depth comparison of their predictive performance. The findings clearly indicate the advantages of the SBSP technique, showcasing notable improvements in performance across all metrics. In the analysis of Category-II, XGBoost showed a significant rise in the Area Under Curve (AUC) from 0.91 to 0.97, RF increased from 0.89 to 0.97, and KNN improved from 0.87 to 0.94, with corresponding enhancements in accuracy, sensitivity, kappa values, and F1-scores. These advancements highlight the capability of the SBSP method to enhance susceptibility predictions and reduce overestimation in areas of high vulnerability. The Landslide Density Index (LDI) supports these findings, as Category-II sampling provides more dependable estimates across all susceptibility classes, minimizing variability and improving interpretive confidence. This study emphasizes the essential importance of sophisticated sampling techniques in enhancing the dependability of LSM and establishes a fundamental framework for upcoming research focused on reducing landslide hazards in intricate landscapes. The results highlight the importance of integrating various conditioning factors and flexible approaches to enhance regional hazard evaluations and strengthen disaster readiness. |
| format | Article |
| id | doaj-art-50a9c4e23c4442ee834946c17d5afc2b |
| institution | DOAJ |
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| publishDate | 2025-04-01 |
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| spelling | doaj-art-50a9c4e23c4442ee834946c17d5afc2b2025-08-20T03:10:09ZengNature PortfolioScientific Reports2045-23222025-04-0115112510.1038/s41598-025-95087-7Enhancing landslide disaster prediction by evaluating non landslide area sampling in machine learning models for Spiti Valley IndiaDevraj Dhakal0Kanwarpreet Singh1Kennedy C. Onyelowe2Steven Alejandro Salazar Cazco3Abhishek Sharma4Nassir Alarifi5Fakhrul Islam6Randeep7Krishna Prakash Arunachalam8Youssef M. Youssef9Department of Civil Engineering, Chandigarh UniversityDepartment of Civil Engineering, Chandigarh UniversityDepartment of Civil Engineering, School of Engineering and Applied Sciences, Kampala International UniversityFacultad de Informática y Electrónica, Escuela Superior Politécnica de Chimborazo (ESPOCH)Department of Civil Engineering, Chandigarh UniversityDepartment of Geology & Geophysics, College of Science, King Saud UniversityUniversity of Chinese Academy of SciencesDepartment of Civil Engineering, Dayalbagh Educational Institute (Deemed to be University)Departamento de Ciencias de la Construcción, Facultad de Ciencias de la Construcción Ordenamiento Territorial, Universidad Tecnológica MetropolitanaGeological and Geophysical Engineering Department, Faculty of Petroleum and Mining Engineering, Suez UniversityAbstract Landslides are well-known natural or quasi-natural hazards which can harm human lives, infrastructure, and the environment, particularly in mountainous regions like Spiti Valley, Himachal Pradesh, India. The Spiti Valley in Himachal Pradesh, India, is prone to landslides due to its fragile geology, steep slopes, and unpredictable climate. These dangers threaten life, infrastructure, and the environment, requiring comprehensive landslide susceptibility mapping (LSM) for mitigation and planning. Advanced machine learning and new sampling procedures may improve LSM accuracy and reliability in high-risk zones. This investigation aims to enhance LSM through a systematic evaluation of innovative sampling methodologies for non-landslide areas. This study focuses on assessing the effectiveness of two novel sampling methods: Buffer Zone Safe Points (BZSP) and Slope Buffer Safe Points (SBSP). Detailed susceptibility zonation maps were created employing advanced statistical techniques, specifically Extreme Gradient Boosting (XGBoost), Random Forest (RF), and K-Nearest Neighbors (KNN), allowing for an in-depth comparison of their predictive performance. The findings clearly indicate the advantages of the SBSP technique, showcasing notable improvements in performance across all metrics. In the analysis of Category-II, XGBoost showed a significant rise in the Area Under Curve (AUC) from 0.91 to 0.97, RF increased from 0.89 to 0.97, and KNN improved from 0.87 to 0.94, with corresponding enhancements in accuracy, sensitivity, kappa values, and F1-scores. These advancements highlight the capability of the SBSP method to enhance susceptibility predictions and reduce overestimation in areas of high vulnerability. The Landslide Density Index (LDI) supports these findings, as Category-II sampling provides more dependable estimates across all susceptibility classes, minimizing variability and improving interpretive confidence. This study emphasizes the essential importance of sophisticated sampling techniques in enhancing the dependability of LSM and establishes a fundamental framework for upcoming research focused on reducing landslide hazards in intricate landscapes. The results highlight the importance of integrating various conditioning factors and flexible approaches to enhance regional hazard evaluations and strengthen disaster readiness.https://doi.org/10.1038/s41598-025-95087-7Landslide susceptibility mapping (LSM)Landslide predictionNon-Landslide samplingsMachine learning modelsSpiti ValleyGeoinformatics |
| spellingShingle | Devraj Dhakal Kanwarpreet Singh Kennedy C. Onyelowe Steven Alejandro Salazar Cazco Abhishek Sharma Nassir Alarifi Fakhrul Islam Randeep Krishna Prakash Arunachalam Youssef M. Youssef Enhancing landslide disaster prediction by evaluating non landslide area sampling in machine learning models for Spiti Valley India Scientific Reports Landslide susceptibility mapping (LSM) Landslide prediction Non-Landslide samplings Machine learning models Spiti Valley Geoinformatics |
| title | Enhancing landslide disaster prediction by evaluating non landslide area sampling in machine learning models for Spiti Valley India |
| title_full | Enhancing landslide disaster prediction by evaluating non landslide area sampling in machine learning models for Spiti Valley India |
| title_fullStr | Enhancing landslide disaster prediction by evaluating non landslide area sampling in machine learning models for Spiti Valley India |
| title_full_unstemmed | Enhancing landslide disaster prediction by evaluating non landslide area sampling in machine learning models for Spiti Valley India |
| title_short | Enhancing landslide disaster prediction by evaluating non landslide area sampling in machine learning models for Spiti Valley India |
| title_sort | enhancing landslide disaster prediction by evaluating non landslide area sampling in machine learning models for spiti valley india |
| topic | Landslide susceptibility mapping (LSM) Landslide prediction Non-Landslide samplings Machine learning models Spiti Valley Geoinformatics |
| url | https://doi.org/10.1038/s41598-025-95087-7 |
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