Comparison of random forests and other statistical methods for the prediction of lake water level: a case study of the Poyang Lake in China

• Published in: Hydrology Research Vol. 47; no. S1; pp. 69 - 83
• Main Authors: Li, Bing, Yang, Guishan, Wan, Rongrong, Dai, Xue, Zhang, Yanhui
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.12.2016
• Subjects: Accuracy; Aquifers; Artificial intelligence; Artificial neural networks; Carbon; Case studies; Computer simulation; Daily forecasts; Decision making; Decision trees; Discharge; Explicit knowledge; Forecasting; Groundwater; Hydrologic models; Hydrology; Information dissemination; Lake water; lake water level; Lakes; Mathematical models; Mean square values; Methods; Neural networks; poyang lake; Precipitation; random forests; Regression analysis; Resource management; Rivers; Root-mean-square errors; Soft computing; Statistical methods; Statistical prediction; Support vector machines; support vector regression; Sustainable development; Time lag; Time series; Trends; variable importance analysis; Water levels; Water management; Water resources; Water resources management; China; Poyang Lake; Yangtze River
• DOI: 10.2166/nh.2016.264
• ISSN: 0029-1277; 1998-9563

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.