四川水力发电2025，Vol.44Issue(3)：94-99,6.DOI:10.20196/j.cnki.scslfd.20250325
基于机器算法的水库下泄水温减缓措施效果预测模型研究
Study on Effect Prediction Model for Reservoir Downstream Water Temperature Mitigation Measures Based on Machine Algorithms
朴虹奕 1周湘山 1徐劲草 1张磊 1秦甦1
作者信息
- 1. 中国电建集团成都勘测设计研究院有限公司,四川 成都 611130
- 折叠
摘要
Abstract
This paper aims to use machine learning algorithms to build a prediction model for evaluating the ef-fectiveness of reservoir downstream water temperature mitigation measures.With the construction of reser-voirs,the downstream water temperature has become lower than the natural water temperature,which not only affects the ecological environment of the reservoir but also has negative impacts on downstream aquatic organ-isms.Therefore,adopting effective measures to mitigate the decrease in reservoir water temperature is crucial.The core of the study involves training and learning from historical data using machine learning algorithms-in-cluding linear regression,naive Bayes model,support vector machines(SVM),random forests,and neural net-works-to construct the prediction model.By comparing and optimizing the parameters and prediction effects of different machine learning algorithm models,it is found that support vector machines perform best in predic-ting reservoir downstream water temperatures.The model achieves a prediction accuracy of 89%,demonstra-ting high practical value.关键词
机器学习/统计预测模型/水温Key words
machine learning/statistical prediction model/water temperature分类
建筑与水利引用本文复制引用
朴虹奕,周湘山,徐劲草,张磊,秦甦..基于机器算法的水库下泄水温减缓措施效果预测模型研究[J].四川水力发电,2025,44(3):94-99,6.
