Rainfall Forecasting in Sub-Sahara Africa-Ghana using LSTM Deep Learning Approach

Prediction of rainfall is a critical activity, and rainfall is a vital event for Human activities. Rainfall prediction is challenging and even more complicated due to the weather's dynamic nature. In this report, we make hourly rainfall predictions about Axim in the western region of Ghana. Rainfall data were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF). We study the predictive capacity of deep learning and builtt an Artificial Neural Networks (ANN) model by using the Long Short-Term Memory (LSTM) algorithm and the Spearman coefficient based on selected parameters (seven) reported by the weather station. A correlation analysis was performed on the chosen parameters to get the best combination, which significantly influenced rainfall. Validations were carried out first on each parameter, Precipitation, Relative Humidity, MSLP, and Temperature; Secondly, on Precipitation (R) against Relative Humidity (H), Pressure (M), and temperature; Thirdly on (R) against (T) /(M), (R) against (T)/(H), (R) against (M)/(H). Finally, the testing on the (R) against (T) /(M)/(H) produced the best MSE result of 0.002 with a MAE result of 0.021.