刘 东,李 帅,付 强,刘春雷.基于KHA优化BP神经网络的地下水水质综合评价方法[J].农业机械学报,2018,49(9):275-284.
LIU Dong,LI Shuai,FU Qiang,LIU Chunlei.Comprehensive Evaluation Method of Groundwater Quality Based on BP Network Optimized by Krill Herd Algorithm[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(9):275-284.
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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   [下载全文]
Comprehensive Evaluation Method of Groundwater Quality Based on BP Network Optimized by Krill Herd Algorithm   [Download Pdf][in English]
投稿时间:2018-03-27  
DOI:10.6041/j.issn.1000-1298.2018.09.032
中文关键词:  地下水水质  建三江管理局  磷虾群算法  BP神经网络
基金项目: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)和黑龙江省水利科技项目(201319、201501、201503)
作者单位
刘 东 东北农业大学
农业部农业水资源高效利用重点实验室 
李 帅 东北农业大学 
付 强 东北农业大学
农业部农业水资源高效利用重点实验室 
刘春雷 东北农业大学 
中文摘要: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、CODMn、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,CODMn、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。
LIU Dong  LI Shuai  FU Qiang  LIU Chunlei
Northeast Agricultural University;Key Laboratory of Effective Utilization of Agricultural Water Resources, Ministry of Agriculture,Northeast Agricultural University,Northeast Agricultural University;Key Laboratory of Effective Utilization of Agricultural Water Resources, Ministry of Agriculture and Northeast Agricultural University
Key Words:groundwater quality  Jiansanjiang Administration  krill herd algorithm  BP neural network
Abstract: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, NH3N and NO-3N were the main factors caused groundwater pollution. Fe and Mn were local primary hazard but excessive amounts of CODMn, NH3 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.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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