Runoff Probability Prediction Model Based on Natural Gradient Boosting with Tree-Structured Parzen Estimator Optimization

Abstract

Accurate and reliable runoff prediction is critical for solving problems related to water resource planning and management. Deterministic runoff prediction methods cannot meet the needs of risk analysis and decision making. In this study, a runoff probability prediction model based on natural gradient boosting (NGboost) with tree-structured parzen estimator (TPE) optimization is proposed. The model obtains the probability distribution of the predicted runoff. The TPE algorithm was used for the hyperparameter optimization of the model to improve the prediction. The model was applied to the prediction of runoff on the monthly, weekly and daily scales at the Yichang and Pingshan stations in the upper Yangtze River. We also tested the prediction effectiveness of the models using exponential, normal and lognormal distributions for different flow characteristics and time scales. The results show that in terms of deterministic prediction, the proposed model improved in all indicators compared to the benchmark model. The root mean square error of the monthly runoff prediction was reduced by 9% on average and 7% on the daily scale. In probabilistic prediction, the proposed model can provide reliable probabilistic prediction on weekly and daily scales.