Geohazards susceptibility assessment along the upper indus basin using four machine learning and statistical models
|dc.identifier.citation||Ahmad, H. and Ningsheng, C. and Rahman, M. and Islam, M.M. and Pourghasemi, H.R. and Hussain, S.F. and Habumugisha, J.M. et al. 2021. Geohazards susceptibility assessment along the upper indus basin using four machine learning and statistical models. ISPRS International Journal of Geo-Information. 10 (5): Article No. 315.|
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.
|dc.subject||Science & Technology|
|dc.subject||Computer Science, Information Systems|
|dc.subject||Pakistan economic corridor|
|dc.title||Geohazards susceptibility assessment along the upper indus basin using four machine learning and statistical models|
|dcterms.source.title||ISPRS International Journal of Geo-Information|
© 2021 The Authors. Published by MDPI Publishing.
|curtin.department||School of Earth and Planetary Sciences (EPS)|
|curtin.faculty||Faculty of Science and Engineering|
|curtin.contributor.orcid||Dewan, Ashraf [0000-0001-5594-5464]|
|curtin.contributor.researcherid||Dewan, Ashraf [O-2191-2015]|
|curtin.contributor.scopusauthorid||Dewan, Ashraf |