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

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 ap...