基于DRASTIC-LU参数和数据驱动模型的地下水硝酸盐脆弱性分区  

Groundwater Vulnerability Zoning to Nitrate Based on DRASTIC-LU Parameters and Data-driven Models Seyed Ahmad Eslaminezhad1,Mobin

作　　者：Seyed Ahmad Eslaminezhad Mobin Eftekhari Mohammad Akbari Seyed Ahmad Eslaminezhad;Mobin Eftekhari;Mohammad Akbari(Department of Surveying and Geomatics Engineering,College of Engineering,University of Tehran,Tehran 1417466191,Iran;Department of Water and Hydraulic Structures Engineering,Mashhad Branch of Islamic Azad University,Mashhad 9187147587,Iran;Department of Civil Engineering,University of Birjand,Birjand 9717434765,Iran)

机构地区：[1]德黑兰大学工程学院测绘与地理信息工程系,伊朗德黑兰1417466191 [2]伊斯兰阿扎德大学马什哈德分校土木工程系,伊朗马什哈德9187147587 [3]比尔詹德大学土木工程系,伊朗比尔詹德9717434765

出　　处：《北京工业大学学报》2021年第12期1338-1359,共22页Journal of Beijing University of Technology

摘　　要：从开采、管理和控制不同地区的污染的角度,评估地下水脆弱性以确定这些资源的优先次序是重要的.研究的目的是基于DRASTIC-LU参数以及空间和非空间数据驱动的方法来估算Birjand平原含水层的地下水(硝酸盐质量浓度)脆弱性.研究提出新的组合方法来确定(Birjand平原含水层)地下水脆弱性分区中合适的DRASTICLU参数,即将具有指数和双平方核的地理加权回归(geographically weighted regression,GWR)和人工神经网络(artificial neural network,ANN)与二进制粒子群优化算法(binary particle swarm optimization,BPSO)相结合.计算结果为:对于ANN、指数核GWR和双平方核GWR的适应度函数(1-R^(2))的最佳值分别为0.1060、0.0745和0.0065,这表明双平方核的兼容性比其他方法更高.研究表明DRASTIC-LU参数对研究区域的硝酸盐质量浓度估计的地下水脆弱性有显著影响.Assessment of groundwater vulnerability to prioritize these resources from the perspective of exploitation,management,and control of pollution in different areas is important.The purpose of this study was to estimate groundwater vulnerability(nitrate concentration)of the Birjand plain aquifer based on DRASTIC-LU parameters and spatial and non-spatial data-driven methods.The novelty of this study is to present new combination approaches to determine the effective DRASTIC-LU parameters in groundwater vulnerability zoning(Birjand plain aquifer).In this regard,geographically weighted regression(GWR)with exponential and bi-square kernels and artificial neural network(ANN)were combined with a binary particle swarm optimization algorithm(BPSO).The best value of the fitness function(1-R2)for ANN,GWR with exponential kernel,and GWR with bi-square kernel was obtained 0.1060,0.0745,and 0.0065,respectively,which indicates higher compatibility of the bi-square kernel than other methods.It was also found that the DRASTIC-LU parameters have a significant effect on the rate of groundwater vulnerability and estimation of the nitrate concentration in the study area.

关 键 词：地下水脆弱性 DRASTIC-LU参数 地理加权回归 人工神经网络 二进制粒子群优化算法 数据驱动模型 

分 类 号：P208[天文地球—地图制图学与地理信息工程]