Spatio-Temporal Estimation of Glacier Dynamics under Climate Change Scenarios Using Machine Learning Techniques

Malik Abid Hussain Khokhar; Isma Younas; Adnan Ahmad Tahir

Authors

Malik Abid Hussain Khokhar Ph.D. Scholar, Institute of Geography, University of the Punjab, Lahore 54590, Pakistan
Isma Younas Institute of Geography, University of the Punjab, Lahore 54590, Pakistan
Adnan Ahmad Tahir Department of Environmental Sciences, COMSATS University Islamabad (CUI), Abbottabad Campus, Abbottabad 22060, Pakistan

Keywords:

Glacier Dynamics, Machine Learning (ML), Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), Climate Change

Abstract

Glaciers of the Upper Indus Basin (UIB) play a vital role in providing 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 to that 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.

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