Kayal, Partha2023-07-172023-07-172022-0721p.http://hdl.handle.net/10263/7389Dissertation under the supervision of Dr. Sarbani PalitRemote sensing data is a rich resource of information, as it provides a time-wise sequence of data, and therefore can be used for prediction purposes. In this paper, we addressed the challenge of using time series on satellite images to predict the Glacial Lake Outburst Flood(GLOF). In order to predict GLOF, we proposed two-step approach. In the first step, our aim is to extract the pixel-wise information about water, snow, and soil at different time stamps and prepare them for use in the training input. The second step we use is Long Short Term Memory (LSTM) network in order to learn temporal features and thus predict the future pixel value of water, snow, and soil.enGlacial Lake Outburst Flood(GLOF)Normalized Difference Water Index(NDWI)Normalized Difference Snow Index(NDSI)Normalized Difference Soil Index(NDSI)LSTMOther