Abstract
Modeling of hydrological time series is essential for sustainable development and management of lake water resources. This study aims to develop an efficient model for forecasting lake water level variations, exemplified by the Poyang Lake (China) case study. A random forests (RF) model was first applied and compared with artificial neural networks, support vector regression, and a linear model. Three scenarios were adopted to investigate the effect of time lag and previous water levels as model inputs for real-time forecasting. Variable importance was then analyzed to evaluate the influence of each predictor for water level variations. Results indicated that the RF model exhibits the best performance for daily forecasting in terms of root mean square error (RMSE) and coefficient of determination (R2). Moreover, the highest accuracy was achieved using discharge series at 4-day-ahead and the average water level over the previous week as model inputs, with an average RMSE of 0.25 m for five stations within the lake. In addition, the previous water level was the most efficient predictor for water level forecasting, followed by discharge from the Yangtze River. Based on the performance of the soft computing methods, RF can be calibrated to provide information or simulation scenarios for water management and decision-making.
Highlights
Lake water level forecasting has important applications for identifying the main influencing factors of water level fluctuations, determination of the watershed hydrological cycle variation trends under projections of global climate changes, integration of reservoir management schemes, and ensuring sufficient freshwater supply (Wantzen et al ; Hu et al ; Kourgialas et al )
All the soft computing methods displayed desirable performances with respect to root mean square error (RMSE) and R2, which indicated that the models were fully trained with the 58-year data to provide satisfactory forecasting
The RMSEs of the ANN and random forests (RF) models were found to vary with the number of trees and units in the hidden layer (Figure 6); RF regression had a relatively low RMSE for the testing stage and especially for the training stage
Summary
Lake water level forecasting has important applications for identifying the main influencing factors of water level fluctuations, determination of the watershed hydrological cycle variation trends under projections of global climate changes, integration of reservoir management schemes, and ensuring sufficient freshwater supply (Wantzen et al ; Hu et al ; Kourgialas et al ). Reliable and accurate forecasting of lake water level has always been a challenge for hydrologists and water resource managers. Li et al | Using random forests for water level forecasting in Poyang Lake, China
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