Abstract—Water is an important part of our daily lives: food, manufacture, agriculture, etc. When water is not enough for all population, it leads to many undesirable impacts including drought, famine and death. The solution to this problem is the good management of water resources. The management of water resources is planning and designing of projects related to water. The runoff prediction is one major part of planning. It is a complex process and it also needs an adequate modeling technique for accurate prediction. Therefore, we propose to use combined algorithms to improve prediction performance. Our combination includes the two powerful methods: Artificial Neural Network (ANN) and Support Vector Regression (SVR). The root mean square error (RMSE) and the correlation coefficient (R) are two criteria that we use to evaluate the model performance regarding the comparison between actual runoff and the prediction made by our model. We also compare performance of our model against the other algorithms: Linear Regression, ANN, and Support Vector Machines. The comparison results show that our proposed method shows the best performance and the combined model is also quite accurate on predicting the peak runoff values during heavy rain season.
Index Terms—Runoff prediction, artificial neural network, support vector regression, Mun Basin.
The authors are with the School of Computer Engineering, Suranaree University of Technology (SUT), 111 University Avenue, Muang, Nakhon Ratchasima 30000, Thailand. (corresponding author: R. Chanklan; Tel: +66994696164; e-mail: arc_angle@hotmail.com, nuntawut@sut.ac.th, mikaiterng@gmail.com, kerdpras@sut.ac.th, nittaya@sut.ac.th).
Cite: Ratiporn Chanklan, Nuntawut Kaoungku, Keerachart Suksut, Kittisak Kerdprasop, and Nittaya Kerdprasop, "Runoff Prediction with a Combined Artificial Neural Network and Support Vector Regression," International Journal of Machine Learning and Computing vol. 8, no. 1, pp. 39-43, 2018.