基于遗传算法-BP神经网络法的深埋地下水水质评价
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摘要
为探究开封市地下水水质特征及成因,依据开封市31眼深度600～1 600m地下水开采井的水质检测资料,系统研究了各亚含水层的水化学特征,利用遗传算法-BP神经网络法进行了水质评价,并从埋深、富水性两个方面分析了水质特征的分布规律。结果表明,开封市600～800m亚含水层地下水水质最好,800～1 400m次之,1 400～1 600m亚含水层水质最差,随着埋深的增加,水质变差,富水性越强、水质越好。可见遗传算法-BP神经网络法能够客观地描述地下水水质综合情况,避免了人为主观影响,使评价结果更为合理。
In order to explore the characteristics and causes of groundwater quality in Kaifeng City,the hydrochemical characteristics of sub-aquifers were systematically studied based on the water quality test data of 31 groundwater exploitation wells with depths of 600-1 600 min Kaifeng City.The hybrid genetic algorithm and BP neural network method was used to evaluate water quality,and the distribution law of water quality characteristics was analyzed from two aspects of burial depth and water-rich.The results show that the groundwater quality of 600-800msub-aquifer is the best in Kaifeng City,followed by 800-1 400 m,and 1 400-1 600 mis the worst.With the increase of burial depth,water quality becomes worse,water richness becomes stronger,and water quality becomes better.It can be seen that the hybrid genetic algorithm and BP neural network method can objectively describe the comprehensive situation of groundwater quality,avoid the subjective influence of human,and make the evaluation results more reasonable.
In order to explore the characteristics and causes of groundwater quality in Kaifeng City,the hydrochemical characteristics of sub-aquifers were systematically studied based on the water quality test data of 31 groundwater exploitation wells with depths of 600-1 600 min Kaifeng City.The hybrid genetic algorithm and BP neural network method was used to evaluate water quality,and the distribution law of water quality characteristics was analyzed from two aspects of burial depth and water-rich.The results show that the groundwater quality of 600-800msub-aquifer is the best in Kaifeng City,followed by 800-1 400 m,and 1 400-1 600 mis the worst.With the increase of burial depth,water quality becomes worse,water richness becomes stronger,and water quality becomes better.It can be seen that the hybrid genetic algorithm and BP neural network method can objectively describe the comprehensive situation of groundwater quality,avoid the subjective influence of human,and make the evaluation results more reasonable.
引文
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[2]Xinyi Wang,Li Zhao,Xiaoman Liu,et al.Temperature Effect on the Transport of Nitrate and Ammonium Ions in a Loose-pore Geothermal Reservoir[J].Journal of Geochemical Exploration,2013,124(1):59-66.
[3]鹿海员,谢新民,郭克贞,等.基于水资源优化配置的地下水可开采量研究[J].水利学报,2013,44(10):1 182-1 188.
[4]周淼,李维刚,易灵.四种水质评价方法的特点分析与比较研究[J].环境科学与管理,2016,41(12):173-177.
[5]庞园,张明珠,庞志研,等.广花盆地地下水水质评价及变化趋势分析[J].水电能源科学,2017,35(11):40-43.
[6]田帅,刘国东,倪福全.BP与GIS耦合的地下水水质综合分析评价[J].水电能源科学,2012,30(2):38-40,119.
[7]HJ/T164-2004,地下水环境监测技术规范[S].北京:中国环境科学出版社,2005.
[8]GB/T14848-1993,地下水质量标准[S].北京:中国标准出版社,2008.
[9]金菊良,周玉良,魏一鸣.基于遗传程序设计的水质评价模型[J].水电能源科学,2004,22(2):1-5.
[10]GB5749-2006,生活饮用水卫生标准[S].北京:中国标准出版社,2006.
[11]金菊良,洪天求,王文圣.基于熵和FAHP的水资源可持续利用模糊综合评价模型[J].水力发电学报,2007,26(4):22-28.
[12]郭庆春,何振芳,李力,等.BP神经网络在渭河水环境质量评价中的应用[J].水土保持通报,2011,31(4):112-115.