基于卷积径向基网络的多变量水位预测模型  

Multivariable water level prediction model based on convolution radial basis network

作　　者：王海麟 朱加良 何正熙[2,3] 周新志 WANG Hailin;ZHU Jialiang;HE Zhengxi;ZHOU Xinzhi(College of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China;Science and Technology on Reactor System Design Technology Laboratory,Nuclear Power Institute of China,Chengdu 610213,China;Key Laboratory of Optical Fiber Sensing&Communications(Ministry of Education),University of Electronic Science and Technology of China,Chengdu 611731,China)

机构地区：[1]四川大学电子信息学院,成都610065 [2]中国核动力研究设计院核反应堆系统设计技术重点实验室,成都610213 [3]电子科技大学光纤传感与通信教育部重点实验室,成都611731

出　　处：《水力发电学报》2023年第3期70-81,共12页Journal of Hydroelectric Engineering

基　　金：四川省科技计划资助(2021YFG0121);四川省科技成果转移转化示范项目(2022ZHCG0042)。

摘　　要：实现河流水位的准确预测对于流域水资源的精准调度与管理具有重要意义。由于水文数据的复杂多变与非线性相关特性,传统机器学习模型的预测精度难以进一步提高。本文提出了一种基于卷积径向基网络的多变量水位预测模型,该模型先通过多层二维卷积网络对水文变量的时空特征进行并行化地充分提取,然后利用径向基网络实现河流水位的高精度预测。针对四川省清溪河流域开展了模型的相关实验研究,结果表明:与四种经典模型相比,其均方根误差最少降低了0.0387,纳什效率系数最少增加了0.0557;与现有的自回归循环网络模型相比,在相同输入特征条件下其最大误差和均方根误差分别降低了0.3482和0.0165,验证了该模型在流域水位预测中具有良好的适用性和有效性。Accurate prediction of river water levels is of great significance for a high-quality dispatching and management of the water resources in the basin,but the prediction accuracy of a traditional machine learning model is usually difficult to improve further due to the complexity and nonlinear correlation of hydrological data.This paper develops a more accurat4e model of multivariable water level prediction based on a convolution radial basis network.It extracts the spatiotemporal features of hydrological variables fully in parallel,using a multi-layer two-dimensional convolution network;then it achieves high-accuracy predictions of river water levels through a radial basis function network.To verify this model,a numerical experiment is carried out focusing on the predictions of the Qingxi River basin in Sichuan.The results show that compared with four classical models,its root-mean-square error is reduced by 0.039 at least,and the Nash efficiency coefficient increased by 0.056 at least.Compared with the AR-RNN model with the same inputs,its maximum error and root-mean-square error are reduced by 0.348 and 0.017 respectively,verifying its good applicability and effectiveness in basin water level predictions.

关 键 词：水位预测 多变量序列 卷积网络 径向基函数 特征提取 相关性分析 

分 类 号：TV124[水利工程—水文学及水资源]