Prediction of the water level at the Kien Giang River based on regression techniques

Abstract

Abstract Model accuracy and runtime are two key issues for flood warnings in rivers. Traditional hydrodynamic models, which have a rigorous physical mechanism for flood routine, have been widely adopted for water level prediction in river, lake, and urban areas. However, these models require various types of data, in‐depth domain knowledge, experience with modeling, and intensive computational time, which hinders short‐term or real‐time prediction. In this paper, we propose a new framework based on machine learning methods to alleviate the aforementioned limitation. We develop a wide range of machine learning models such as linear regression (LR), support vector regression (SVR), random forest regression (RFR), multilayer perceptron regression (MLPR), and light gradient boosting machine regression (LGBMR) to predict the hourly water level at Le Thuy and Kien Giang stations of the Kien Giang river based on collected data of 2010, 2012, and 2020. Four evaluation metrics, that is, R2, Nash–Sutcliffe efficiency, mean absolute error, and root mean square error, are employed to examine the reliability of the proposed models. The results show that the LR model outperforms the SVR, RFR, MLPR, and LGBMR models.

Keywords

• LGBMR
• linear regression
• machine learning
• MLPR
• RFR
• SVR