Forecasting Monthly Rainfall using Neural Networks – A Case of Mbombela

Researcher: Naledi Mogane, University of the Witwatersrand, Johannesburg
Supervisors: Dr Ritesh Ajoodha and Dr Ashwini Jadhav, University of the Witwatersrand, Johannesburg

Forecasting rainfall is a critical technique for preventing climate-related risks and ensuring long-term management. This study presents the application of neural networks to predict rainfall. To accomplish this, we used a Long Short-Term Memory (LSTM), a type of recurrent neural network (RNN), and we compared the results with the Autoregressive Integrated Moving Average (ARIMA) model. The statistical effectiveness of the models reveals that the LSTM model can predict monthly rainfall in the catchment with reasonable accuracy.