Philippine E-Journals

Rainfall Forecasting Model in the Province of Isabela

Reyson P. Raymundo

Discipline: Technology

 

Abstract:

Climate and rainfall are highly non-linear and complicated phenomena, which require sophisticated computer modeling and simulation for accurate prediction. An artificial intelligence technology allows knowledge processing and can be used as forecasting tool. The application of Artificial Neural Network (ANN), to predict the behaviors of nonlinear systems has become an attractive alternative to traditional statistical methods. This study introduces two fundamentally different approaches for designing a model, the statistical method based on Multiple Regression Analysis (MRA) and the emerging computationally powerful techniques based on ANN. To evaluate the prediction efficiency, twenty (20) years of mean annual rainfall data from year 1990 to 2010 and within the 75 km radius of PAG-ASA, Echague, Isabela, Philippines was used. The ANN and the MRA approaches are applied to the data to derive the weights and the coefficients respectively. Better performance of the ANN or MRA models were measured by the criteria of the least Root Mean Square Error (RMSE), and highest Efficiency Coefficient (EC). The study reveals that ANN model can be used as an appropriate forecasting tool to predict the rainfall, which out performs the MRA model in the province of Isabela.