基于改进遗传算法的河流水质模型多参数识别  

Multi-parameter identification of river water quality model based on an improved genetic algorithm

作　　者：刘洁 陈昊辉 张丰帆 姜德迅 许崇品 南军 王鹏 LIU Jie;CHEN Haohui;ZHANG Fengfan;JIANG Dexun;XU Chongpin;NAN Jun;WANG Peng(School of Environment,Harbin Institute of Technology,Harbin 150090,China;School of Conservancy and Civil Engineering,Northeast Agricultural University,Harbin 150030,China;School of Information Engineering,Harbin University,Harbin 150086,China)

机构地区：[1]哈尔滨工业大学环境学院,哈尔滨150090 [2]东北农业大学水利与土木工程学院,哈尔滨150030 [3]哈尔滨学院信息工程学院,哈尔滨150086

出　　处：《东北农业大学学报》2020年第1期73-82,共10页Journal of Northeast Agricultural University

基　　金：国家自然科学基金面上项目（51779066）;中国博士后科学基金面上项目（2018M631935）

摘　　要：结合多种群遗传算法和自适应遗传算法,提出基于多种群-自适应遗传算法(MPAGA)水质模型多参数识别方法,实现对河流水质模型参数断面平均流速u、河流离散系数D和污染物降解系数K识别和估计。利用河流示踪剂实验监测数据,分别对美国特拉基河3个不同流量河段作水质模型多参数识别和估计。结果表明,与传统遗传算法(TGA)相比,MPAGA算法对3种不同流量河段水质模型参数估计结果准确和可靠。基于MPAGA水质模型多参数识别方法和分河段水质模型参数估计结果可提高河流水污染预测精度,判定河流水污染危险区域,为保障工农业用水安全提供重要科学依据和技术支撑。In this study,a multiple population-adaptive genetic algorithm(MPAGA),with the integration of multi-population and adaptive genetic algorithm,is proposed to identify and estimate water quality parameters,such as water flow rate u,dispersion coefficient D and rate constant of pollutant degradation K.Then,the developed method was applied to Truckee River,America for multi-parameter identification,according to the river trace experimental data under three scenarios of different river discharges.Comparing with traditional genetic algorithm(TGA),the results demonstrate that MPAGA could obtain more accurate and reliable parameter identification results under all the three scenarios of different river discharges.The multi-parameter identification method MPAGA for water quality model and the multi-parameter identification results for different reaches,could effectively enhance pollution prediction accuracy,identify river hazardous area,and provide scientific basis and technique support for industrial and agricultural water safety.

关 键 词：工农业用水安全 示踪剂实验 水质模型 多参数识别 改进遗传算法 

分 类 号：X323[环境科学与工程—环境工程]