Chai, S.S, Wong, W.K, Goh, K.L., Wang, H.H. and Wang, Y.C. (2019) Radial Basis Function (RBF) Neural Network: Effect of Hidden Neuron Number, Training Data Size, and Input Variables on Rainfall Intensity Forecasting. International Journal on Advanced Science Engineering Information Technology, 9, 1921-1926.
https://doi.org/10.18517/ijaseit.9.6.10239

has been cited by the following article:

• TITLE: Development of Trees Management System Using Radial Basis Function Neural Network for Rain Forecast

  AUTHORS: Hasnul Auzani, Khairusy Syakirin Has-Yun, Farah Aniza Mohd Nazri

  KEYWORDS: Tree Management, Radial Basis Function, Rain Prediction, Artificial Neural Network

  JOURNAL NAME: Computational Water, Energy, and Environmental Engineering, Vol.11 No.1, December 1, 2021

  ABSTRACT: 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.