Predictive Modeling Using Glacier Volume and Depth Estimations with Advanced Statistical and Deep Learning Techniques

This paper addresses the problem of predicting GLOFs with a new type of hybrid deep learning architecture based on geographical factors such as volume and depth of glaciers. Within the proposed system, LSTM is used to emulate temporal dependencies and CNNs derive spatial data. The system has been tested to be highly effective using the data of geophysical and satellite and also having accuracy and precision of 98.7 percent and 98.5 percent, respectively. The system shows a great deal of improvement from the traditional models as the AUC-ROC is 99.1%. Other statistical analysis like error analysis confirms the robustness and adaptability of the system at various places. This paper provides a comprehensive solution for GLOF prediction. It addresses several significant deficiencies found in existing methodologies and has the potential to be used in helping to detect early floods and to manage catastrophe risk in regions that are sustained by glaciers.