CWEEE  Vol.11 No.1 , January 2022
Development of Trees Management System Using Radial Basis Function Neural Network for Rain Forecast
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.
Cite this paper: Auzani, H. , Has-Yun, K. and Nazri, F. (2022) Development of Trees Management System Using Radial Basis Function Neural Network for Rain Forecast. Computational Water, Energy, and Environmental Engineering, 11, 1-10. doi: 10.4236/cweee.2022.111001.

[1]   Avolio, M.L., Pataki, D.E., Gillespie, T.W., Jenerette, G.D., McCarthy, H.R., Stepha-nie, P. and Clarke, L.W. (2015) Tree Diversity in Southern California’s Urban Forest: The Interacting Roles of Social and Environmental Variables. Frontier in Ecology and Evolution, 3, 3-15.

[2]   Mallikarjun, M., Farheen, Saraswathi, L. and Prakash, L. (2020) Artificial Neural Network Based Weather Pridiction System. International Journal of Scientific & Technology Research, 9, 5587-5594.

[3]   Iqbal, H., Rasel, H.M., Monzur, A.I. and Mekanik, F. (2019) Long-Term Seasonal Rainfall Forecasting Using Linear and Non-Linear Modelling Approaches: A Case Study for Western Australia. Meteorology and Atmospheric Physics, 132, 131-141.

[4]   Lee, J., Kim, C.G., Lee, J.E., Kim, N.W. and Kim, H. (2018) Application of Artificial Neural Networks to Rainfall Forecasting in the Geum River Basin, Korea. Water, 10, 1448.

[5]   Ishwarya, G., Santhrupthi, M.B., Shanthi, B. and Varsha, N. (2021) Prediction of Rainfall Using Machine Learning Algorithms. International Journal of Scientific Research & Engineering Trends, 7, 2124-2128.

[6]   Suhaimi, S. and Rosmina, A.B. (2009) Rainfall Runoff Modeling Using Radial Basis Function Neural Network for Sungai Tinjar Catchment, Miri, Sarawak. UNIMAS E-Journal of Civil Engineering, 1, 1-7.

[7]   Mansor, M.A., Mohd Jamaludin, S.Z., Mohd Kasihmuddin, M.S., Alzaeemi, S.A., Md Basir, M.F. and Sathasivam, S. (2020) Systematic Boolean Satisfiability Programming in Radial Basis Function Neural Network. Processers, 8, 1-16.

[8]   Cramer, S., Kampouridis, M., Freitas, A.A. and Alexandridis, A.K. (2017) An Extensive Evaluation of Seven Machine Learning Methods for Rainfall Prediction in Weather Derivatives. Expert Systems with Applications, 85, 169-181.

[9]   Chai, S.S., Wong, W.K. and Goh, K.L. (2016) Backpropagation vs. Radial Basis Function Neural Model: Rainfall Intensity Classification for Flood Prediction Using Meteorology Data. Journal of Computer Science, 12, 191-200.

[10]   Vivekanandan, N. (2014) Prediction of Rainfall Using MLP and RBF Networks. International Journal Advanced Networking and Applications, 5, 1974-1979.

[11]   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.

[12]   Madhiarasan, M. (2020) Accurate Prediction of Different Forecast Horizons Wind Speed Using a Recursive Radial Basis Function Neural Network. Protection and Control of Modern Power Systems, 5, 22.

[13]   Mary, N.A., Thomas, J.A. and Akintunde, A.A. (2017) Rainfall Rate Prediction Based on Artificial Neural Networks for Rain Fade Mitigation over Earth-Satellite link. IEEE Africon 2017 Proceeding, Cape Town, 18-20 September 2017, 579-584.

[14]   Broomhead, D.S. (1988) Multivariate Functional Interpolation and Adaptive Networks. Complex Systems, 2, 321-355.

[15]   Amrul, F., Hudan, P.A., Shamsul Faisal, M.H., Che Munira, C.R., Aminaton, M. and Shahrum, S.A. (2020) Deep Learning-Based Forecast and Warning of Floods in Klang River, Malaysia. International Information and Engineering Technology Association, 25, 365-370.