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

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

doi:10.3390/ijgi10050315

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.

Highlights

The Himalayan–Karakoram Mountain (HKM) in northern Pakistan are among the most important mountain ranges globally, with changing topographic and climatic characteristics [1,2]
Thirteen conditioning factors were considered and their TOL and Variance Inflation Factor (VIF) values were calculated to verify the absence of multicollinearity for each variable
Logistic Regression (LR), WoE, Frequency Ratio (FR), and Shannon Entropy (SE), were used to develop geohazard susceptibility maps, and the suitability of each model was validated through Area Under the Receiver Operating Characteristic (AUROC) curves

Summary

Introduction

The Himalayan–Karakoram Mountain (HKM) in northern Pakistan are among the most important mountain ranges globally, with changing topographic and climatic characteristics [1,2]. The irregular topography makes this region extremely vulnerable to geohazards, including landslides, debris flow, glacial erosion, flash floods, river incision, and periglacial action [3,4]. These geohazards cause severe damages to human lives and infrastructure [5,6,7,8]. The China–Pakistan Economic Corridor (CPEC) project extends from China to Pakistan through this mountain range. This project has been a driver of the recent rapid growth of urban and rural areas in Pakistan. The expected development of new infrastructure in this region faces many challenges as geological surveying and assessment activities have been impeded by geohazards

Objectives

Methods

Results

Conclusion