基于改进的GRNN—Markov水质预测模型研究及应用

徐睿; 张晓斌; 薛鹏松

引用本文:	徐睿,张晓斌,薛鹏松.基于改进的GRNN—Markov水质预测模型研究及应用[J].灌溉排水学报,0,():-.
	XU Rui,ZHANG Xiaobin,XUE Pengsong.基于改进的GRNN—Markov水质预测模型研究及应用[J].灌溉排水学报,0,():-.

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基于改进的GRNN—Markov水质预测模型研究及应用
徐睿¹, 张晓斌², 薛鹏松³
1.运城市水利勘测设计研究院有限公司;2.运城学院;3.陕西锦科环保工程有限公司

摘要:

【目的】构建改进GRNN—Markov水质预测模型，为模拟不确定性、复杂多变的河流水质变化趋势提供可靠的方法依据。【方法】以汾河入黄口主要污染物CODcr为研究指标，采用准则对水质监测资料进行预处理，利用灰色关联分析（GRA）改进GRNN网络，解决GRNN网络输入节点不能自动寻优的缺点，为了得到更准确的结果，降低GRNN网络的预测误差，构建改进GRNN—Markov水质预测模型，以期提高水质预测精度，为水环境保护与治理提供新思路、新方法。【结果】研究结果表明：改进GRNN—Markov水质预测模型，可以提高水质预测结果的精度，使相对误差从-38.27% ~ -15.71% 提高到-25.77% ~ -5.25%，修正结果更加接近实测值。【结论】验证了组合模型在小样本水质预测中的可行性，为水环境管理提供了科学依据。

关键词: 灰色关联分析（GRA）；改进GRNN —Markov模型；汾河入黄口；水质预测

DOI：

分类号:X 832；TP 183

基金项目:国家自然科学基金（51479215）；山西省水利厅科技项目（TZ2019026），运城学院学科经费资助

Research and application on improved GRNN-Markov water quality prediction model

XU Rui¹, ZHANG Xiaobin², XUE Pengsong³

1.Yuncheng Water Conservancy Survey and Design Research Institute Co., Ltd;2.Yuncheng University;3.Shaanxi Jinke Environmental Protection Engineering Co., Ltd.

Abstract:

【Objective】Build an improved GRNN-Markov water quality prediction model, which provides a reliable method for simulating uncertain, complex and variable river water quality trends.【Method】 According to actual situation of water quality in Fen River’s estuary to Yellow River, taking the main pollutant CODcr as the research index, the water quality monitoring data were preprocessed using the Laida criteria. Using Grey Relational Analysis to determine input nodes of GRNN Network which solved the problem that the GRNN network unable to auto select and optimize input nodes. In order to obtain more accurate results and reduce the prediction error of the GRNN network, an improved GRNN-Markov water quality prediction model was constructed to improve the water quality prediction accuracy.【Result】The combined model provides new ideas and methods for water environmental protection and governance.The research show that the improved GRNN-Markov water quality prediction model can improve the accuracy of water quality prediction results. Relative error was-38.27% ~ -15.71% based on GRA—GRNN model, Relative error was-25.77% ~ -5.25% based on improved GRNN-Markov water quality prediction model, the correction result was close to the measured value.【Conclusion】This combination model can be used in water quality prediction based on small sample data. This study provides a scientific basis for water environment management.

Key words: grey relational analysis (GRA); improved GRNN-Markov model; Fen River’s estuary to Yellow River; water quality prediction