Rainfall forecasting in the Barak river basin, India using a LSTM network based on various climate indices

Abstract

The proposed study employs a long short-term memory (LSTM) neural network (NN) to forecast monthly rainfall in the Barak river basin in the northeastern region of India for a prediction horizon up to 4 months in advance. Out of nine significant climate variables, sea surface temperature (SST), sea level pressure (SLP), Nino 3.4 index, the Indian summer monsoon rainfall (ISMR) anomalies and dipole mode index (DMI) were identified to be the best-suited predictors and were introduced as the inputs in the NN. The LSTM is a special kind of recurrent neural network (RNN) which specializes in feature extraction and storing memory in its cell state cumulatively. The model results display strong correlations between the potential predictor sets and the rainfall distribution across the basin. The obtained forecast results were scrutinized in terms of various statistical measures and the predictions were found to be at par with the real time observations (correlations greater than 0.90 and hit score greater than 85%). The testing phase of model produced root mean square errors in the range of 12.45% to 15.65% highlighting satisfactory model performance. The proposed method of incorporating different climate indices form a novel approach to forecast rainfall in the region which may lead to timely and effective management of water resources.