Abstract
Accurate rainfall forecasting plays a significant role for weather stations as it serves to warn people about incoming natural disasters. This paper presents an implementation of week-ahead rainfall forecast that utilizes Multilayer Perceptron Neural Network (MLPNN) in processing historical rainfall data. Proper data preparation, model implementation and performance evaluation were conducted to two MLPNN models which yields promising results in predicting week-ahead rainfall. The MLPNN architecture was a supervised feed-forward neural network having 11 input neurons consisting of different weather variables along with various hidden neurons and 7 output neurons representing the week-ahead forecast. The MLPNN models which were SCG-Tangent and SCG-Sigmoid, produced a MAE of 0.01297 and 0.1388 and RMSE of 0.01512 and 0.01557, respectively. This viable implementation of MLPNN in rainfall forecasting hopes to provide organizations and individuals with lead-time for the strategic and tactical planning of activities and courses of action related to rainfall.
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