Comparative analysis of machine learning and multi-criteria decision making techniques for landslide susceptibility mapping of Muzaffarabad district

Frontiers in Environmental Science, vol. 10

Abstract

Landslides are natural disasters deliberated as the most destructive among the others considered. Using the Muzaffarabad as a case study, this work compares the performance of three conventional Machine Learning (ML) techniques, namely Logistic Regression (LGR), Linear Regression (LR), Support Vector Machine (SVM), and two Multi-Criteria Decision Making (MCDM) techniques, namely Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for the susceptibility mapping of landslides. Most of these techniques have been used in the region of Northern Pakistan before for the same purpose. However, this study for landslide susceptibility assessment compares the performance of various techniques and provides additional insights into the factors used by adopting multicollinearity analysis. Landslide-inducing factors considered in this research are lithology, slope, flow direction, fault lines, aspect, elevation, curvature, earthquakes, plan curvature, precipitation, profile curvature, Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), roads, and waterways. Results show that SVM performs better than LGR and LR among ML models. On the other hand, the performance of AHP was better than TOPSIS. All the models rank slope, precipitation, elevation, lithology, NDWI, and flow direction as the top three most imperative landslide-inducing factors. Results show 80% accuracy in Landslide Susceptibility Maps (LSMs) from ML techniques. The accuracy of the produced map from the AHP model is 80%, but for TOPSIS, it is less (78%). In disaster planning, the produced LSMs can significantly help the decision-makers, town planners, and local management take necessary measures to decrease the loss of life and assets.

Authors 6

1. Umer Khalil

   International Institute for Geo-Information Science and Earth Observation · University of Twente

   Affiliation as printed

   ITC Faculty of Geo-information Science and Earth Observation , University of Twente , Enschede , Netherlands,

   ITC Faculty of Geo-information Science and Earth Observation, University of Twente, Enschede, Netherlands,

2. Iqra Imtiaz

   COMSATS University Islamabad

   Affiliation as printed

   COMSATS University Islamabad, Islamabad Campus, Islamabad, Pakistan

3. Bilal Aslam

   Northern Arizona University

   Affiliation as printed

   School of Informatics, Computing and Cyber Systems , Northern Arizona University , Flagstaff , AZ , United States,

   School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ, United States,

4. Israr Ullah Aachen Division of Earth Sciences and Geography

   RWTH Aachen University

   Affiliation as printed

   Division of Earth Sciences and Geography , RWTH Aachen University , Aachen , Germany,

   Division of Earth Sciences and Geography, RWTH Aachen University, Aachen, Germany,

5. Aqil Tariq

   Wuhan University · State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing · Mississippi State University

   Affiliation as printed

   Department of Wildlife, Fisheries and Aquaculture , Mississippi State University , Mississippi State , MS , United States,

   State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing , Wuhan University , Wuhan , China,

   Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, Mississippi State, MS, United States,

   State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China,

6. Shujing Qin corresponding

   Wuhan University · State Key Laboratory of Water Resources and Hydropower Engineering Science

   Affiliation as printed

   State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China