Template-Type: ReDIF-Article 1.0
Author-Name:Malik Abid Hussain Khokhar,Isma Younes, Adnan Ahmad Tahir
Author-Email:abidmalikgeo@gmail.com
Author-Workplace-Name:Institute of Geography, University of the Punjab, Lahore 54590, Pakistan., Department   of   Environmental   Sciences,   COMSATS   University   Islamabad   (CUI), Abbottabad Campus, Abbottabad 22060, Pakistan
Title:Spatio-TemporalEstimation of Glacier Dynamics UnderClimate Change Scenarios Using Machine Learning Techniques
Abstract:Glaciers of the Upper Indus Basin (UIB) play a vital role inproviding water resources, hydropower generation,and livelihood,but they are very vulnerable and sensitive to continuous  climate  change  impacts.  This  research  presents a novel  approach  for accurate  mapping  of  glacier  extent,  clean  ice,  debris  cover,  seasonal  snow,and  glacier  melt across the Hunza Basin. We have used Grey Level Co-occurrence Matrix (GLCM), Machine Learning (ML) techniques of Random Forest (RF), Artificial Neural Networks (ANN),and Support Vector Machines (SVM) to conduct the purposeful research. ML models were trained on multispectral (Landsat, Sentinel-1 & 2, MODIS,and SPOT-5 from the last 35 years) and textural datasets.  Overall, 6628 samples for training and 988 samples for testing were used to maintain a 70/  30  ratio  to  evaluate  overall  accuracy  (OA)  and  kappa  coefficient  (k̂).    RF ensured the best results (OA = 95.4 %, k̂= 0.965) in comparison of ANN (OA = 94%, k̂= 0.92) and SVM (OA = 92 %, k̂= 0.89). The accuracy of clean ice and seasonal snow remained consistent (producer accuracy and user accuracy >93%) compared tothat of debris cover and glacier melt. Glacier retreat, increased ablation, formation of clean ice loss,and frequency of supraglacial melt due to expansion of debris cover up to 23.31% were witnessed spatially in the basin. Proposed approaches prove that ML techniques are very useful for the estimation of risk assessment in the climate-prone mountain basins and offer a robust way forward for hydrological modelling, glacier change monitoring,and water resource management.
Keywords:Glacier  Dynamics;  ML  (machine  learning);  RF  (random  forest); ANN  (artificial neural network); SVM (support vector machine)
Journal: International Journal of Innovations in Science and Technology
Pages:2126-2152
Volume:7
Issue:3
Year: 2025
Month:August
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1561/2235
File-Format: Application/pdf
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1561
File-Format: text/html
Handle: RePEc:abq:IJIST:v:7:y:2025:i:3:p:2126-2152