Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models release_cwromp5a7rdllgnj5mqo337bnu

by Hilal Ahmad, Chen Ningsheng, Mahfuzur Rahman, Dr Monirul Islam, Hamid Reza Pourghasemi, Syed Fahad Hussain, Jules Maurice Habumugisha, Enlong Liu, Zheng Han, Huayong Ni, Ashraf Dewan

Published in ISPRS International Journal of Geo-Information by MDPI AG.

2021   Volume 10, p315

Abstract

The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE), Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale.
In application/xml+jats format

Archived Files and Locations

application/pdf  37.2 MB
file_oaog3esywngidp4mojsyh75ora
res.mdpi.com (publisher)
web.archive.org (webarchive)
application/pdf  37.2 MB
file_giisii4w4jgdzcsu4eron3edqe
mdpi-res.com (web)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2021-05-07
Language   en ?
Journal Metadata
Open Access Publication
In DOAJ
In ISSN ROAD
In Keepers Registry
ISSN-L:  2220-9964
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 1be58543-ff3b-461c-ab90-c6df120ccbdd
API URL: JSON