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基于KHA优化BP神经网络的地下水水质综合评价方法 被引量:21

Comprehensive Evaluation Method of Groundwater Quality Based on BP Network Optimized by Krill Herd Algorithm
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摘要 为提高区域地下水水质评价精度,将磷虾群算法(Krill herd algorithm,KHA)引入到BP神经网络连接权值与阈值的优化过程中,构建了KHA-BP地下水水质综合评价模型。以黑龙江省农垦建三江管理局为研究对象,运用所建模型对其下辖15个农场进行地下水水质综合评价,并对造成地下水水质污染的主要原因进行辨识。为验证本文所建模型的适用性,引入区分度法与序号总和理论分别分析了KHA-BP模型、PSO-BP模型以及BP模型的可靠性与稳定性。结果表明:各农场地下水水质良好,且存在一定的空间分布规律,I类水质主要集中在管理局西南位置,Ⅱ类水质主要集中在北部和南部,Ⅲ类水质主要分布于中东部和中西部。Fe、Mn、CODMn、NH_3-N以及NO-3-N是造成地下水水质污染的主要因素。其中Fe、Mn是当地原生危害,CODMn、NH_3-N、NO-3-N含量超标主要与大量施用化肥、农药有关。KHA-BP模型的区分度为1.107 0,Spearman等级相关系数为0.928 6,与PSO-BP模型、BP模型相比优势明显。研究成果可为粮食生产核心区的地下水资源科学管理及水生态文明建设提供科学依据。 A new BP network model was developed to improve the accuracy and assess the groundwater quality. For this purpose,the krill herd algorithm(KHA) was established with the optimization process of the connection weights and thresholds of the BP neural network. Totally 15 farms were selected to evaluate the groundwater quality and identify the main causes of groundwater pollution in Jiansanjiang Administration. In addition,to verify the applicability of the model,the distinction degree method and the theory of serial number summation were used to analyze the reliability and stability of KHA-BP model, PSO-BP model and BP model, respectively. The results exhibited a good agreement of groundwater quality in each farm and there was a certain spatial distribution pattern such as the water quality of grade I was mainly concentrated in the southwest position,grade Ⅱ was distributed in the north and south,while the grade Ⅲ was located in the mid-west and mid-east of the administration. Fe,Mn,CODMn,NH3-N and NO-3-N were the main factors caused groundwater pollution. Fe and Mn were local primary hazard but excessive amounts of CODMn,NH3-N and NO-3^-N were mainly related to use of a large number of fertilizers and pesticides. The distinction degree of KHA-BP was 1. 107 0 and Spearman 's rank coefficient was 0. 928 6,which was better than those of PSO-BP and BP. In conclusion,this research could provide a scientific basis for the comprehensive management of groundwater resources and construction of water ecological civilization in the core areas of food production.
作者 刘东 李帅 付强 刘春雷 LIU Dong;LI Shuai;FU Qiang,;LIU Chunlei(College of Water Conservancy and Civil Engineering,Northeast Agricultural University,Harbin 150030,China;Key Laboratory of Effective Utilization of Agricultural Water Resources,Ministry of Agriculture,Harbin 150030,China)
出处 《农业机械学报》 EI CAS CSCD 北大核心 2018年第9期275-284,共10页 Transactions of the Chinese Society for Agricultural Machinery
基金 国家自然科学基金项目(51579044 41071053 51479032) 国家重点研发计划项目(2017YFC0406002) 黑龙江省自然科学基金项目(E2017007) 黑龙江省水利科技项目(201319 201501 201503)
关键词 地下水水质 建三江管理局 磷虾群算法 BP神经网络 groundwater quality Jiansanjiang Administration krill herd algorithm BP neural network
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