Water levels forecast in Thailand: A case study of Chao Phraya river

Abstract

It is always desirable to be able to manage level of water in river, dam, and reservoir. Models have been constructed for predicting the level of these bodies of water, and good models can help increase the effectiveness of water management. Presently, the model that is employed by the Hydrographic Department of the Royal Thai Navy for predicting the level of water in Chao Phraya river is a harmonic method of tidal modeling. This model can predict the overall trend well but with high individual prediction error. Many machine learning algorithms for making predictions have also been introduced in recent years. Therefore, it was attempted in this study to compare the prediction performance of several machine learning models to that of the Royal Thai Navys model. These models were the following: linear regression, kernel regression, support vector regression, k-nearest neighbors, and random forest. The data input into these models were water level time series data of past 24, 48, and 72 hours measured at the Royal Thai Navy headquarters station, Phra Chulachomklao Fort, thirteen other stations along the river, and the output were predictions for the next 24 hours. It was found that all of the machine learning techniques were able to achieve better performances than that of the harmonic method of tidal modeling. The support vector regression model with Radial basis function kernel and 72-hour past time series data yielded prediction results with the least errors, at 0.117 m and 0.116 m for the water levels at the Royal Thai Navy headquarters station and Phra Chulachomklao Fort, respectively.