Prediction of glaciated area fraction over the Sikkim Himalayan Region, India: a comparative study using response surface method, random forest, and artificial neural network.

Abstract

Glacier area fraction at high altitude mountains is a serious worry in today's time triggered by climate change. The current information on this natural resource is very important for the survival of humanity as it affects the water, food, and energy security of people dependent on it. Due to its problematic accessibility and tough environmental condition, ground monitoring is quite challenging. This study investigates the impact of environmental parameters and pollutants on glacier area fraction over the Eastern Himalaya region and its prediction through random forest (RF), multilayer perceptron (MLP), radial basis function analysis (RBFN), and response surface methodology (RSM) models. The data are obtained from the Goddard Earth Sciences Data and Information Services Center (GES DISC), NASA's data archive portal ( https://giovanni.gsfc.nasa.gov ). The collinearity of independent variables reveals that all selected input parameters are highly correlated with R2 value > 0.9. The RSM and RF model provided valuable insight of the predictor's significance in addition to their capability to predict the response. The model performance was evaluated in terms of R2 value and the error matrices. The model's R2 value was found to be 0.843, 0.839, 0.838, and 0.743 for MLP, RBFN, RF, and RSM respectively. Although, the neural network model R2 values are the highest, but the most reliable and suitable model is RF as the error matrices for this model are much lower than others. This study encourages the investigation of the hybridization of these models for more accurate prediction.