Locating monitoring wells in groundwater systems using embedded optimization and simulation models

被引:40
作者
Bashi-Azghadi, Seyyed Nasser [2 ]
Kerachian, Reza [1 ]
机构
[1] Univ Tehran, Sch Civil Engn, Ctr Excellence Engn & Management Infrastruct, Tehran, Iran
[2] Univ Tehran, Sch Civil Engn, Tehran, Iran
关键词
Groundwater monitoring; Pollution source identification; Probabilistic Support Vector Machines (PSVMs); Embedded optimization and simulation models; SUPPORT VECTOR MACHINES; POLLUTION SOURCES; IDENTIFICATION; NETWORK; REGION;
D O I
10.1016/j.scitotenv.2010.02.004
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In this paper, a new methodology is proposed for optimally locating monitoring wells in groundwater systems in order to identify an unknown pollution source using monitoring data The methodology is comprised of two different single and multi-objective optimization models, a Monte Carlo analysis, MODI-TOW. MT3O groundwater quantity and quality simulation models and a Probabilistic Support Vector Machine (PSVM) The single-object we optimization model, which uses the results of the Monte Carlo analysis and maximizes the reliability of contamination detection, provides the initial location of monitoring wells. The objective functions of the multi-objective optimization model are minimizing the monitoring cost, i e the number of monitoring wells, maximizing the reliability of contamination detection and maximizing the probability of detecting an unknown pollution source The PSVMs are calibrated and verified using the results of the single-objective optimization model and the Monte Carlo analysis Then, the PSVMs are linked with the multi-objective optimization model, which maximizes both the reliability of contamination detection and probability of detecting an unknown pollution source To evaluate the efficiency and applicability of the proposed methodology, it is applied to Tehran Refinery in Iran (C) 2010 Elsevier B V All rights reserved
引用
收藏
页码:2189 / 2198
页数:10
相关论文
共 32 条
[1]  
Aral M. M., 1996, NATO ASI SER, V2, P347
[2]   Support vectors-based groundwater head observation networks design [J].
Asefa, T ;
Kemblowski, MW ;
Urroz, G ;
McKee, M ;
Khalil, A .
WATER RESOURCES RESEARCH, 2004, 40 (11) :W1150901-W1150914
[3]   Support vector machines (SVMs) for monitoring network design [J].
Asefa, T ;
Kemblowski, M ;
Urroz, G ;
McKee, M .
GROUND WATER, 2005, 43 (03) :413-422
[4]   State of the art report on mathematical methods for groundwater pollution source identification [J].
Atmadja, J ;
Bagtzoglou, AC .
ENVIRONMENTAL FORENSICS, 2001, 2 (03) :205-214
[5]   Convergence assessment of numerical Monte Carlo simulations in groundwater hydrology [J].
Ballio, F ;
Guadagnini, A .
WATER RESOURCES RESEARCH, 2004, 40 (04) :W046031-W046035
[6]   A Conflict-Resolution Model for the Conjunctive Use of Surface and Groundwater Resources that Considers Water-Quality Issues: A Case Study [J].
Bazargan-Lari, Mohammad Reza ;
Kerachian, Reza ;
Mansoori, Abbas .
ENVIRONMENTAL MANAGEMENT, 2009, 43 (03) :470-482
[7]   Groundwater recharge modelling using the Monte Carlo technique, Manawatu region, New Zealand [J].
Bekesi, G ;
McConchie, J .
JOURNAL OF HYDROLOGY, 1999, 224 (3-4) :137-148
[8]   LIBSVM: A Library for Support Vector Machines [J].
Chang, Chih-Chung ;
Lin, Chih-Jen .
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2011, 2 (03)
[9]  
Cherkassky V, 1997, IEEE Trans Neural Netw, V8, P1564, DOI 10.1109/TNN.1997.641482
[10]   USING GENETIC ALGORITHMS TO SOLVE A MULTIOBJECTIVE GROUNDWATER MONITORING PROBLEM [J].
CIENIAWSKI, SE ;
EHEART, JW ;
RANJITHAN, S .
WATER RESOURCES RESEARCH, 1995, 31 (02) :399-409