An Integrated Approach for Development of a Decision Support System for Early Surveillance of Japanese Encephalitis: a Remote Sensing-based study in Conjunction with Epidemiological Scenario in Assam, India

Our analysis demonstrates the capability of machine learning models in estimating glacier mass balance in terms of performance metrics and dataset availability. Feature importance analysis suggests that ablation features are significant. This is in agreement with the predominantly negative mass balance observations. We show that ensemble tree models typically depict the best performance. However, neural network models are preferable for biased inputs and kernel-based models for smaller datasets.