Rainfall And Meteorological Drought Forecasting in Albay, Philippines Using Artificial Neural Network

Abstract

Agriculture relies heavily on weather forecasts, and a reliable weather forecasting system can help mitigate the calamities which can affect this industry. Rainfall and meteorological drought duration forecasting are some of the most important yet challenging tasks. This paper presents the creation of feedforward backpropagation artificial neural networks for daily rainfall forecasting and monthly meteorological drought forecasting. Artificial Neural Networks can capture the variability of these phenomena. Rainfall data from nine stations all over Albay, the Philippines, spanning from 1967 to 2000, were used to create the models. The input parameters used for developing the models for daily rainfall forecasting were 14-day antecedent rainfall, current-day rainfall, relative humidity, mean temperature, and sunshine duration. The monthly meteorological drought forecasting parameters were 1-month SPI, current-month rainfall, relative humidity, mean temperature, and sunshine duration. Having the results presented in this paper, the performance of the ANN Models of the stations were compared based on R and RMSE. The rainfall forecasting models and meteorological drought forecasting models have provided satisfactory performance. A satisfactory performance for forecasting has an R-value ranging from 0.2 to 0.5. Sensitivity analysis indicated that the most significant parameter for rainfall forecast is the relative humidity and mean temperature for drought forecast.