Abstract: This paper develops a new ecological flow forecasting method based on deep learning and a conceptual hydrological model with application to the Jiaojiang River basin in Zhejiang Province to improve the forecast accuracy of ecological flow early warning and the efficiency of ecological operation of water conservancy projects. This method calculates the ecological flow and warning threshold using the hydrological method, and screens model forecast factors through the principal component analysis. The results reveal the check values of most suitable ecological flows are 2.89 m3/s and 1.92 m3/s at the Baizhiao and Shaduan stations, respectively. We use precipitation and evaporation as input factors and the grid search method for optimal parameters searching, and have achieved a 100% qualified rate of the ecological flow warning level forecasts in all the years by using the eXtreme Gradient Boosting (XGBoost) algorithm. Our coupling prediction model based on XGBoost and the Xin’anjiang model can well complete the ecological flow early warning prediction and reservoir ecological flow regulation, laying a basis of decision-making for protection and supervision of water resources in rivers and lakes.

Key words: ecological flow, principal component analysis, deep learning algorithm, Xin’anjiang model, Jiaojiang River basin