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dc.contributor.authorAhmad, H.
dc.contributor.authorNingsheng, C.
dc.contributor.authorRahman, M.
dc.contributor.authorIslam, M.M.
dc.contributor.authorPourghasemi, H.R.
dc.contributor.authorHussain, S.F.
dc.contributor.authorHabumugisha, J.M.
dc.contributor.authorLiu, E.
dc.contributor.authorZheng, H.
dc.contributor.authorNi, H.
dc.contributor.authorDewan, Ashraf
dc.date.accessioned2021-11-17T08:43:35Z
dc.date.available2021-11-17T08:43:35Z
dc.date.issued2021
dc.identifier.citationAhmad, 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.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/86449
dc.identifier.doi10.3390/ijgi10050315
dc.description.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.

dc.languageEnglish
dc.publisherMDPI
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectScience & Technology
dc.subjectTechnology
dc.subjectPhysical Sciences
dc.subjectComputer Science, Information Systems
dc.subjectGeography, Physical
dc.subjectRemote Sensing
dc.subjectComputer Science
dc.subjectPhysical Geography
dc.subjectChina&#8211
dc.subjectPakistan economic corridor
dc.subjectlandslides
dc.subjectdebris flows
dc.subjectgeohazards
dc.subjectremote sensing
dc.subjectLOGISTIC-REGRESSION
dc.subjectFREQUENCY RATIO
dc.subjectLANDSLIDE RISK
dc.subjectDEBRIS FLOWS
dc.subjectGIS
dc.subjectHIMALAYA
dc.subjectEARTHQUAKE
dc.subjectPAKISTAN
dc.subjectAREA
dc.subjectLIFE
dc.titleGeohazards susceptibility assessment along the upper indus basin using four machine learning and statistical models
dc.typeJournal Article
dcterms.source.volume10
dcterms.source.number5
dcterms.source.titleISPRS International Journal of Geo-Information
dc.date.updated2021-11-17T08:43:34Z
curtin.note

© 2021 The Authors. Published by MDPI Publishing.

curtin.departmentSchool of Earth and Planetary Sciences (EPS)
curtin.accessStatusOpen access
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidDewan, Ashraf [0000-0001-5594-5464]
curtin.contributor.researcheridDewan, Ashraf [O-2191-2015]
curtin.identifier.article-numberARTN 315
dcterms.source.eissn2220-9964
curtin.contributor.scopusauthoridDewan, Ashraf [15925234800]


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