Predicting Seasonal Rainfall Patterns and Trends in Juba County, South Sudan Using Artificial Neural Networks

Abstract

A simple Feed Forward Neural Network (FFNN) model with a learning back-propagation algorithm was applied to forecast rainfall data from 1997-2016 of Juba County, South Sudan. Annual rainfall data were aggregated into three seasons MAMJ, JAS and OND and later trained for best predictions for the period 2017-2034 using the Alyuda Forecaster XL software. Best training was attained once the minimum error or cost function of the weight  was attained during gradient descent and expressed as Mean Square Error (MSE) and AE of the input variable.  The results showed that for MAMJ and JAS months, the number good forecasts were over 97% whereas this was between 60-80% for OND months. The Seasonal Kendal (SK) test on future rainfall forecasts as well as the Theil-Sen slope showed a declining monotonic trend in the mean amounts in all three seasons with MAMJ, JAS at OND at 100, 150 and 80 mm respectively towards the end of 2034.  Declining onset of MAMJ rains is expected to significantly affect the timing for land preparation and crop planting. The forecast accuracy of the FFNN can be used as a vital tool for decision makers in projecting future rainfall events.