Prediction of River Stage Using Multistep-Ahead Machine Learning Techniques for a Tidal River of Taiwan

Abstract

Time-series prediction of a river stage during typhoons or storms is essential for flood control or flood disaster prevention. Data-driven models using machine learning (ML) techniques have become an attractive and effective approach to modeling and analyzing river stage dynamics. However, relatively new ML techniques, such as the light gradient boosting machine regression (LGBMR), have rarely been applied to predict the river stage in a tidal river. In this study, data-driven ML models were developed under a multistep-ahead prediction framework and evaluated for river stage modeling. Four ML techniques, namely support vector regression (SVR), random forest regression (RFR), multilayer perceptron regression (MLPR), and LGBMR, were employed to establish data-driven ML models with Bayesian optimization. The models were applied to simulate river stage hydrographs of the tidal reach of the Lan-Yang River Basin in Northeastern Taiwan. Historical measurements of rainfall, river stages, and tidal levels were collected from 2004 to 2017 and used for training and validation of the four models. Four scenarios were used to investigate the effect of the combinations of input variables on river stage predictions. The results indicated that (1) the tidal level at a previous stage significantly affected the prediction results; (2) the LGBMR model achieves more favorable prediction performance than the SVR, RFR, and MLPR models; and (3) the LGBMR model could efficiently and accurately predict the 1–6-h river stage in the tidal river. This study provides an extensive and insightful comparison of four data-driven ML models for river stage forecasting that can be helpful for model selection and flood mitigation.

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

“…In this study, five regression techniques of machine learning are applied to generate data-driven models. The primary process when developing these models is called the "learning phase," where the relationship between the input and output variables of the system is established (Guo et al, 2021):…”

“…In this study, five regression techniques of machine learning are applied to generate data‐driven models. The primary process when developing these models is called the “learning phase,” where the relationship between the input and output variables of the system is established (Guo et al, 2021): $$$y=f(x)$$$with the available data: $$$[({x}_{1},{y}_{1}),({x}_{2},{y}_{2}),\text{\unicode{x02026}}\,({x}_{n},{y}_{n})]={\{{x}_{i},{y}_{i}\}}_{i=1}^{n},$$$where x is the input vector, y is the output vector, n is the number of observations and f is the regression function.…”

Multi-step-ahead prediction of river flow using NARX neural networks and deep learning LSTM

“…not considering studies in single-step-ahead forecasting. There is one interesting recent study by Guo et al (2021) pointing out that gradient boosting machine, a new type of machine learning algorithm, performs favourably for some step-ahead prediction. This application could be explored in the future.…”

Short-duration prediction of urban storm-water levels using the residual-error ensemble correction technique

“…The results suggested that the LSTM model is the most accurate in their study area and can be helpful for the development of a flood operational forecasting system. Guo et al (2021) proposed and employed four DD models, namely, SVR, random forest regression (RFR), MLPR, and light gradient boosting machine (LightGBM), to predict the river stage at the tidal reach of the Lan-Yang River Basin, Taiwan. Based on their simulated results with 1-6 h lead times, the LightGBM model achieved the best overall performance among the four tested models.…”