Modelling and predicting of landslide in Western Arunachal Himalaya, India
Landslides are the indicator of slope instability particularly in mountain terrain and causing different types of reimbursements and threats of life and property. The Himalayan terrains are highly susceptible to different natural hazards as well as disasters particularly land failure activities main...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-05-01
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| Series: | Geosystems and Geoenvironment |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772883822001339 |
| _version_ | 1797851287058907136 |
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| author | Soumik Saha Biswajit Bera Pravat Kumar Shit Debashish Sengupta Sumana Bhattacharjee Nairita Sengupta Paromita Majumdar Partha Pratim Adhikary |
| author_facet | Soumik Saha Biswajit Bera Pravat Kumar Shit Debashish Sengupta Sumana Bhattacharjee Nairita Sengupta Paromita Majumdar Partha Pratim Adhikary |
| author_sort | Soumik Saha |
| collection | DOAJ |
| description | Landslides are the indicator of slope instability particularly in mountain terrain and causing different types of reimbursements and threats of life and property. The Himalayan terrains are highly susceptible to different natural hazards as well as disasters particularly land failure activities mainly due to inherent tectonic activities which further enhanced by various Neo-tectonic and Neolithic activities. This scientific study provides an enhanced framework for the assessment of proper and precise landslide susceptibility in the two districts of Arunachal Pradesh (Tawang and West Kameng) considering both physical and anthropogenic factors and various machine learning models (SVM, AdaBoost and XGBoost). At first, landslide inventory maps were developed based on previous landslide events. Here, 70% of the data were randomly selected for training and remaining was used for validation and optimization of the models using statistical implications and validation assessment methods. The result showed that the high and very high landslide susceptible areas are mainly concentrated in the middle portion along the Bhalukpong-Bomdia road section. Based on the AUC value and other statistical indicators it has been observed that AdaBoost is the most efficient model here (AUC = 0.92). AUC values of SVM and XGBoost are 0.85 and 0.89 respectively. AdaBoost model identifies that very low susceptibility class occupies 60.22% area and very high landslide susceptibility class occupies 15.51% area and it will be considered as more encouraging method for landslide susceptibility determination in this kind of cases for better accurateness. This high accuracy susceptibility map positively helps during the execution of various developmental projects. |
| first_indexed | 2024-04-09T19:14:21Z |
| format | Article |
| id | doaj.art-2cd058ccf25e4b6db5659655cab76619 |
| institution | Directory Open Access Journal |
| issn | 2772-8838 |
| language | English |
| last_indexed | 2024-04-09T19:14:21Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Geosystems and Geoenvironment |
| spelling | doaj.art-2cd058ccf25e4b6db5659655cab766192023-04-06T06:15:21ZengElsevierGeosystems and Geoenvironment2772-88382023-05-0122100158Modelling and predicting of landslide in Western Arunachal Himalaya, IndiaSoumik Saha0Biswajit Bera1Pravat Kumar Shit2Debashish Sengupta3Sumana Bhattacharjee4Nairita Sengupta5Paromita Majumdar6Partha Pratim Adhikary7Department of Geography, Sidho-Kanho-Birsha University, Ranchi Road, P.O. Purulia Sainik School, Purulia 723104, IndiaDepartment of Geography, Sidho-Kanho-Birsha University, Ranchi Road, P.O. Purulia Sainik School, Purulia 723104, India; Corresponding author.PG Department of Geography, Raja Narendralal Khan Women's College (Autonomous), Vidyasagar University, Midnapore 721102, IndiaDepartment of Geology and Geophysics, Indian Institute of Technology (IIT), Kharagpur, West Bengal 721302, IndiaDepartment of Geography, Jogesh Chandra Chaudhuri College (University of Calcutta), 30, Prince Anwar Shah Road, Kolkata 700033, IndiaDepartment of Geography, Diamond Harbour Women's University, Sarisha 743368, IndiaDepartment of Geography, Vidyasagar College for Women, 39 Sankar Ghosh Lane, Kolkata 700006, IndiaICAR Indian Institute Water Management, Bhubaneswar, Odisha 751023, IndiaLandslides are the indicator of slope instability particularly in mountain terrain and causing different types of reimbursements and threats of life and property. The Himalayan terrains are highly susceptible to different natural hazards as well as disasters particularly land failure activities mainly due to inherent tectonic activities which further enhanced by various Neo-tectonic and Neolithic activities. This scientific study provides an enhanced framework for the assessment of proper and precise landslide susceptibility in the two districts of Arunachal Pradesh (Tawang and West Kameng) considering both physical and anthropogenic factors and various machine learning models (SVM, AdaBoost and XGBoost). At first, landslide inventory maps were developed based on previous landslide events. Here, 70% of the data were randomly selected for training and remaining was used for validation and optimization of the models using statistical implications and validation assessment methods. The result showed that the high and very high landslide susceptible areas are mainly concentrated in the middle portion along the Bhalukpong-Bomdia road section. Based on the AUC value and other statistical indicators it has been observed that AdaBoost is the most efficient model here (AUC = 0.92). AUC values of SVM and XGBoost are 0.85 and 0.89 respectively. AdaBoost model identifies that very low susceptibility class occupies 60.22% area and very high landslide susceptibility class occupies 15.51% area and it will be considered as more encouraging method for landslide susceptibility determination in this kind of cases for better accurateness. This high accuracy susceptibility map positively helps during the execution of various developmental projects.http://www.sciencedirect.com/science/article/pii/S2772883822001339Landslide susceptibilityMachine learning modelsNeo-tectonic activitiesHimalayan terrains |
| spellingShingle | Soumik Saha Biswajit Bera Pravat Kumar Shit Debashish Sengupta Sumana Bhattacharjee Nairita Sengupta Paromita Majumdar Partha Pratim Adhikary Modelling and predicting of landslide in Western Arunachal Himalaya, India Geosystems and Geoenvironment Landslide susceptibility Machine learning models Neo-tectonic activities Himalayan terrains |
| title | Modelling and predicting of landslide in Western Arunachal Himalaya, India |
| title_full | Modelling and predicting of landslide in Western Arunachal Himalaya, India |
| title_fullStr | Modelling and predicting of landslide in Western Arunachal Himalaya, India |
| title_full_unstemmed | Modelling and predicting of landslide in Western Arunachal Himalaya, India |
| title_short | Modelling and predicting of landslide in Western Arunachal Himalaya, India |
| title_sort | modelling and predicting of landslide in western arunachal himalaya india |
| topic | Landslide susceptibility Machine learning models Neo-tectonic activities Himalayan terrains |
| url | http://www.sciencedirect.com/science/article/pii/S2772883822001339 |
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