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.
Highlights
Trees generally play a critical role across numerous significant aspects in human’s livelihood
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 discovers that the hidden neuron value has less affect on the performance of the Root Mean Square Error (RMSE) value which is between 49.92% up to 51.10%
Summary
Trees generally play a critical role across numerous significant aspects in human’s livelihood. Even though the data stemming from the monitoring of tree management activities could only be tracked once trees have been planted but there are some studies which have demonstrated that the data stemming would affected by the tree development [1]. This is not an overnight process and intensive sources with higher number of datasets are frequently difficult to find and access
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