GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India
The main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For thi...
Main Authors: | , , , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2021-01-01
|
Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2021/9914650 |
id |
doaj-8c6561a4204348c3bbe324303605dc6b |
---|---|
record_format |
Article |
spelling |
doaj-8c6561a4204348c3bbe324303605dc6b2021-09-06T00:00:06ZengHindawi LimitedMathematical Problems in Engineering1563-51472021-01-01202110.1155/2021/9914650GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, IndiaTrung-Hieu Tran0Nguyen Duc Dam1Fazal E. Jalal2Nadhir Al-Ansari3Lanh Si Ho4Tran Van Phong5Mudassir Iqbal6Hiep Van Le7Hanh Bich Thi Nguyen8Indra Prakash9Binh Thai Pham10University of Transport TechnologyUniversity of Transport TechnologyDepartment of Civil EngineeringDepartment of CivilUniversity of Transport TechnologyInstitute of Geological SciencesDepartment of Civil EngineeringUniversity of Transport TechnologyUniversity of Transport TechnologyDDG (R) Geological Survey of IndiaUniversity of Transport TechnologyThe main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For this purpose, data of 91 past landslide locations and ten landslide influencing factors, namely, slope degree, curvature, aspect, land cover, slope forming materials (SFM), elevation, distance to rivers, geomorphology, overburden depth, and distance to roads were considered in the models study. Thematic maps of the Geological Survey of India (GSI), Google Earth images, and Aster Digital Elevation Model (DEM) were used for the development of landslide susceptibility maps in the Geographic Information System (GIS) environment. Landslide locations data was divided into a 70 : 30 ratio for the training (70%) and testing/validation (30%) of the three models. Standard statistical measures, namely, Positive Predicted Values (PPV), Negative Predicted Values (NPV), Sensitivity, Specificity, Mean Absolute Error (MAE), Root Mean Squire Error (RMSE), and Area under the ROC Curve (AUC) were used for the evaluation of the models. All the three soft computing models used in this study have shown good performance in the accurate development of landslide susceptibility maps, but performance of the ADT and MLP is better than NB. Therefore, these models can be used for the construction of accurate landslide susceptibility maps in other landslide-prone areas also.http://dx.doi.org/10.1155/2021/9914650 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Trung-Hieu Tran Nguyen Duc Dam Fazal E. Jalal Nadhir Al-Ansari Lanh Si Ho Tran Van Phong Mudassir Iqbal Hiep Van Le Hanh Bich Thi Nguyen Indra Prakash Binh Thai Pham |
spellingShingle |
Trung-Hieu Tran Nguyen Duc Dam Fazal E. Jalal Nadhir Al-Ansari Lanh Si Ho Tran Van Phong Mudassir Iqbal Hiep Van Le Hanh Bich Thi Nguyen Indra Prakash Binh Thai Pham GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India Mathematical Problems in Engineering |
author_facet |
Trung-Hieu Tran Nguyen Duc Dam Fazal E. Jalal Nadhir Al-Ansari Lanh Si Ho Tran Van Phong Mudassir Iqbal Hiep Van Le Hanh Bich Thi Nguyen Indra Prakash Binh Thai Pham |
author_sort |
Trung-Hieu Tran |
title |
GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India |
title_short |
GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India |
title_full |
GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India |
title_fullStr |
GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India |
title_full_unstemmed |
GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India |
title_sort |
gis-based soft computing models for landslide susceptibility mapping: a case study of pithoragarh district, uttarakhand state, india |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1563-5147 |
publishDate |
2021-01-01 |
description |
The main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For this purpose, data of 91 past landslide locations and ten landslide influencing factors, namely, slope degree, curvature, aspect, land cover, slope forming materials (SFM), elevation, distance to rivers, geomorphology, overburden depth, and distance to roads were considered in the models study. Thematic maps of the Geological Survey of India (GSI), Google Earth images, and Aster Digital Elevation Model (DEM) were used for the development of landslide susceptibility maps in the Geographic Information System (GIS) environment. Landslide locations data was divided into a 70 : 30 ratio for the training (70%) and testing/validation (30%) of the three models. Standard statistical measures, namely, Positive Predicted Values (PPV), Negative Predicted Values (NPV), Sensitivity, Specificity, Mean Absolute Error (MAE), Root Mean Squire Error (RMSE), and Area under the ROC Curve (AUC) were used for the evaluation of the models. All the three soft computing models used in this study have shown good performance in the accurate development of landslide susceptibility maps, but performance of the ADT and MLP is better than NB. Therefore, these models can be used for the construction of accurate landslide susceptibility maps in other landslide-prone areas also. |
url |
http://dx.doi.org/10.1155/2021/9914650 |
work_keys_str_mv |
AT trunghieutran gisbasedsoftcomputingmodelsforlandslidesusceptibilitymappingacasestudyofpithoragarhdistrictuttarakhandstateindia AT nguyenducdam gisbasedsoftcomputingmodelsforlandslidesusceptibilitymappingacasestudyofpithoragarhdistrictuttarakhandstateindia AT fazalejalal gisbasedsoftcomputingmodelsforlandslidesusceptibilitymappingacasestudyofpithoragarhdistrictuttarakhandstateindia AT nadhiralansari gisbasedsoftcomputingmodelsforlandslidesusceptibilitymappingacasestudyofpithoragarhdistrictuttarakhandstateindia AT lanhsiho gisbasedsoftcomputingmodelsforlandslidesusceptibilitymappingacasestudyofpithoragarhdistrictuttarakhandstateindia AT tranvanphong gisbasedsoftcomputingmodelsforlandslidesusceptibilitymappingacasestudyofpithoragarhdistrictuttarakhandstateindia AT mudassiriqbal gisbasedsoftcomputingmodelsforlandslidesusceptibilitymappingacasestudyofpithoragarhdistrictuttarakhandstateindia AT hiepvanle gisbasedsoftcomputingmodelsforlandslidesusceptibilitymappingacasestudyofpithoragarhdistrictuttarakhandstateindia AT hanhbichthinguyen gisbasedsoftcomputingmodelsforlandslidesusceptibilitymappingacasestudyofpithoragarhdistrictuttarakhandstateindia AT indraprakash gisbasedsoftcomputingmodelsforlandslidesusceptibilitymappingacasestudyofpithoragarhdistrictuttarakhandstateindia AT binhthaipham gisbasedsoftcomputingmodelsforlandslidesusceptibilitymappingacasestudyofpithoragarhdistrictuttarakhandstateindia |
_version_ |
1717780281076744192 |