Sequence model based cloudburst prediction for the Indian state of Uttarakhand

Abstract

Predicting the phenomenon of cloudburst has been a larger than life challenge to many weather and rain scientists. The very nature of cloudburst occurrence itself complicates the prediction of cloudburst. Since, cloudburst downpour occurs over a short span of time and is confined to very narrow geographic location, it is highly difficult for weather scientists to make any cloudburst predictions. In this work, the authors propose a cloudburst prediction model that leverages deep learning techniques to predict the occurrence of cloudburst in a location. The authors have collected the data pertaining to the cloudburst events that have occurred in the Indian State of Uttarakhand over the past decade and developed the model. Experiments were conducted using time series sequence models namely Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). Predictive Power Score (PPS) has been used to extract the essential features that are fed as input to these sequence models. The performance of sequence models has been discussed in terms of loss function and accuracy and the results are promising for GRU based model in comparison with other sequence models.