太阳成8722(中国)有限公司-GREEN NO.1

杜清运、博士生张慧荟

标题: Semi-empirical prediction method for monthly precipitation prediction based on environmental factors and comparison with stochastic and machine learning models

作者: Huihui Zhang, Hugo A. Loáiciga ,Fu Ren , QingYun Du（通讯作者）, Da Ha

来源出版物: Hydrological Sciences Journal DOI: 10.1080/02626667.2020.1784901 出版年:JUN 2020

摘要: Precipitation prediction is central in hydrology and water resources planning and management. This paper introduces a semi-empirical predictive model to predict monthly precipitation and compares its predictive skill with those of machine learning (ML) methods. The stochastic method presented herein estimates monthly precipitation with one-step-ahead prediction properties. The ML predictive skill of the algorithms is evaluated by predicting monthly precipitation relying on the statistical association between precipitation and environmental and topographic factors. The semi-empirical predictive model features non-negative matrix factorization (NMF) for investigating the influence of multiple predictor variables on precipitation. The semi-empirical predictive model’s parameters are optimized with the hybrid genetic algorithm (GA) and Levenberg-Marquardt algorithm (LM), or GALMA, yielding a validated model with high predictive skill. The methodologies are illustrated with data from Hubei Province, China, which comprise 27 meteorological station datasets from 1988–2017. The empirical results provide valuable insights for developing semi-empirical rainfall prediction models.

关键词：precipitation, prediction, machine learning algorithms, environmental factors, semi-empirical model

电子邮箱：qydu@whu.edu.cn