Agriculture and farming are mainly dependent on weather especially in
Malaysia as it received heavy rainfall throughout the years. An efficient crop
or tree management system with a weather forecast needed for suitable planning
of farming operation. Radial Basis Function Neural Network (RBFNN) algorithm
was used in this study to predict rainfall and the main focus of this study is
to analyze the factor that affects the
performance of neural model. This study found that the model works better the
more hidden nodes and the optimum learning rate is 0.01 with the RMSE 49% and
the percentage accuracy is 57%. Besides that, it is found that the meteorology
data also affect the model performance. Future research can be conducted to
improve the rainfall forecast of this study and improve the
tree management system.
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