Forecasting monthly precipitation using sequential modelling

• Published in: Hydrological sciences journal 2019-04, Vol.64 (6), p.690-700
• Main Authors: Kumar, Deepak, Singh, Anshuman, Samui, Pijush, Jha, Rishi Kumar
• Format: Article
• Language: English
• Subjects: Atmospheric precipitations; Climatic extremes; deep learning; Environmental risk; Forecasting; Hydrologic cycle; Hydrologic data; Hydrologic models; Hydrological cycle; Hydrology; Long short-term memory; Machine learning; Mathematical models; Monthly; Monthly precipitation; Monthly rainfall; Neural networks; Precipitation; Precipitation data; Rain; Rainfall; Rainfall forecasting; rainfall prediction; Recurrent neural networks; Regions; sequential modelling; Time series; Water management; Water resources; Water resources management; Weather forecasting
• ISSN: 0262-6667; 2150-3435
• DOI: 10.1080/02626667.2019.1595624

In the hydrological cycle, rainfall is a major component and plays a vital role in planning and managing water resources. In this study, new generation deep learning models, recurrent neural network (RNN) and long short-term memory (LSTM), were applied for forecasting monthly rainfall, using long sequential raw data for time series analysis. "All-India" monthly average precipitation data for the period 1871-2016 were taken to build the models and they were tested on different homogeneous regions of India to check their robustness. From the results, it is evident that both the trained models (RNN and LSTM) performed well for different homogeneous regions of India based on the raw data. The study shows that a deep learning network can be applied successfully for time series analysis in the field of hydrology and allied fields to mitigate the risks of climatic extremes.