Rainfall forecasting models using focused time-delay neural networks

Rainfall forecasting is vital for making important decisions and performing strategic planning in agriculture-dependent countries. Despite its importance, statistical rainfall forecasting, especially for long-term, has been proven to be a great challenge due to the dynamic nature of climate phenomena and random fluctuations involved in the process. Artificial Neural Networks (ANNs) have recently become very popular and they are one of the most widely used forecasting models that have enjoyed fruitful applications for forecasting purposes in many domains of engineering and computer science. The main contribution of this research is in the design, implementation and comparison of rainfall forecasting models using Focused Time-Delay Neural Networks (FTDNN). The optimal parameters of the neural network architectures were obtained from experiments while networks were trained to perform one-step-ahead predictions. The daily rainfall dataset, obtained from Malaysia Meteorological Department (MMD), was converted to monthly, biannually, quarterly and monthly datasets. Training and testing were performed on each of the datasets and corresponding accuracies of the forecasts were measured using Mean Absolute Percent Error. For testing data, results indicate that yearly rainfall dataset gives the most accurate forecasts (94.25%). As future work, more parameters such as temperature, humidity and sunshine data can be incorporated into the neural network for superior forecasting performance.