Solving inverse problems of groundwater-pollution-source identification using a differential evolution algorithm

In this study, an accurate model was developed for solving problems of groundwater-pollution-source identification. In the developed model, the numerical simulations of flow and pollutant transport in groundwater were carried out using MODFLOW and MT3DMS software. The optimization processes were carried out using a differential evolution algorithm. The performance of the developed model was tested on two hypothetical aquifer models using real and noisy observation data. In the first model, the release histories of the pollution sources were determined assuming that the numbers, locations and active stress periods of the sources are known. In the second model, the release histories of the pollution sources were determined assuming that there is no information on the sources. The results obtained by the developed model were found to be better than those reported in literature.RésuméDans cette étude, un modèle précis a été développé pour résoudre les problèmes d’identification de la source de pollution des eaux souterraines. Dans le modèle développé, les simulations numériques d’écoulement et de transport de polluant dans les eaux souterraines ont été réalisées en utilisant les logiciels MODFLOW et MT3DMS et les processus d’optimisation ont été conduits au moyen d’un algorithme d’évolution différentielle. La performance du modèle développé a été testée sur deux modèles d’aquifères hypothétiques en utilisant des données d’observation réelles et bruitées. Dans le premier modèle, les historiques d’émission par les sources de pollution ont été déterminés en faisant l’hypothèse selon laquelle les nombres, les localisations et les périodes d’activité des sources sont connus. Dans le second modèle, les historiques d’émission par les sources de pollution ont été déterminés en faisant l’hypothèse qu’il n’y a pas d’information sur les sources. Les résultats obtenus au moyen du modèle développé sont meilleurs que ceux rapportés dans la littérature.ResumenEn este estudio, se desarrolló un modelo exacto para la resolución de problemas de identificación de fuentes de contaminación de agua subterránea. En el modelo desarrollado, las simulaciones numéricas de flujo y de transporte de contaminantes en el agua subterránea se llevaron a cabo usando los softwares MODFLOW y MT3DMS, y el proceso de optimización fue realizado utilizando un algoritmo diferencial de evolución. El rendimiento del modelo desarrollado se probó con dos modelos de acuíferos hipotéticos usando datos observacionales reales y ruidosos. En el primer modelo, las historias de los vertidos de las fuentes de contaminación se determinaron suponiendo conocidos los números, ubicaciones y períodos activos de las fuentes. En el segundo modelo, las historias de los vertidos de las fuentes de contaminación se determinaron suponiendo que no hay ninguna información sobre las fuentes. Se encontró que los resultados obtenidos por el modelo desarrollado eran mejores que los informados en la literatura.摘要在这项研究中,建立了准确模型来解决地下水污染源识别问题。在开发的模型中,采用MODFLOW和MT3DMS软件进行了地下水水流和污染物运移的数值模拟,并且采用差分进化算法进行了最优化处理。利用真实和众多的观测数据在两个假设含水层模型上对开发模型的性能进行了测试。第一个模型中,假定污染源的数量、位置和有效压力期已知,确定了污染源的释放历史。第二个模型中,假定没有污染源的任何信息,确定了污染源的释放历史。发现,所开发的模型获取的结果比文献记载的要好。ResumoNesse estudo, um modelo acurado foi desenvolvido para solucionar problemas de identificação de fontes de poluição das águas subterrâneas. No modelo desenvolvido, as simulações numéricas de fluxo e transporte de poluentes foram conduzidas usando os softwares MODFLOW e MT3DMS, e o processo de otimização foi conduzido usando um algoritmo de evolução diferencial. O desempenho do modelo desenvolvido foi testado em dois modelos de aquíferos hipotéticos usando dados de observações reais e com ruído. No primeiro modelo, os históricos de liberação de fontes de poluição foram determinados assumindo que os números, locais e períodos de estresse ativo das fontes são conhecidos. No segundo modelo, os históricos de liberação de fontes de poluição foram determinados assumindo que não existe informação sobre as fontes. Os resultados obtidos pelo modelo desenvolvido foram considerados melhores do que aqueles referenciados na literatura.ÖzetBu çalışmada, yeraltısuyu-kirlilik-kaynağı belirlenmesi ters problemlerinin çözümü için doğruluğu yüksek bir model geliştirilmiştir. Geliştirilen modelde, yeraltısuyunda akım ve kirletici taşınımı denklemlerinin sayısal simülasyonları MODFLOW ve MT3DMS yazılımları, optimizasyon işlemleri ise diferansiyel gelişim algoritması kullanılarak gerçekleştirilmiştir. Geliştirilen modelin performansı, iki adet kurgusal akifer modeli üzerinde gerçek ve hatalı gözlem verileri kullanılarak test edilmiştir. Birinci modelde, kaynakların yerleri ve sayılarının bilindiği varsayılarak kirletici kaynakların boşalım geçmişleri elde edilmiştir. İkinci modelde ise kaynaklarla ilgili herhangi bir bilgi olmadığı varsayılarak kirletici kaynakların boşalım geçmişleri belirlenmiştir. Geliştirilen modelden elde edilen sonuçların, literatürde verilen sonuçlardan daha iyi olduğu görülmüştür.

[1]  Ashu Jain,et al.  Identification of Unknown Groundwater Pollution Sources Using Artificial Neural Networks , 2004 .

[2]  George F. Pinder,et al.  Extension and field application of an integrated DNAPL source identification algorithm that utilizes stochastic modeling and a Kalman filter , 2011 .

[3]  M Tamer Ayvaz,et al.  A linked simulation-optimization model for solving the unknown groundwater pollution source identification problems. , 2010, Journal of contaminant hydrology.

[4]  Rainer Storn,et al.  Differential Evolution-A simple evolution strategy for fast optimization , 1997 .

[5]  Keith W. Hipel,et al.  Status quo analysis of the Flathead River conflict , 2004 .

[6]  George F. Pinder,et al.  Mass transport in flowing groundwater , 1973 .

[7]  Amvrossios C. Bagtzoglou,et al.  Mathematical Methods for Hydrologic Inversion: The Case of Pollution Source Identification , 2005 .

[8]  M. Janga Reddy,et al.  Multiobjective Differential Evolution with Application to Reservoir System Optimization , 2007 .

[9]  Om Prakash,et al.  Sequential optimal monitoring network design and iterative spatial estimation of pollutant concentration for identification of unknown groundwater pollution source locations , 2013, Environmental Monitoring and Assessment.

[10]  Hund-Der Yeh,et al.  Groundwater contaminant source identification by a hybrid heuristic approach , 2007 .

[11]  S. Gorelick,et al.  Identifying sources of groundwater pollution: An optimization approach , 1983 .

[12]  A. Bagtzoglou,et al.  State of the Art Report on Mathematical Methods for Groundwater Pollution Source Identification , 2001 .

[13]  Om Prakash,et al.  Characterization of Groundwater Pollution Sources with Unknown Release Time History , 2014 .

[14]  George F. Pinder,et al.  Optimal Search Strategy for the Definition of a DNAPL Source , 2003 .

[15]  A. Zanini,et al.  Contaminant source and release history identification in groundwater: a multi-step approach. , 2014, Journal of contaminant hydrology.

[16]  Lin Qiu,et al.  Estimation of Nonlinear Muskingum Model Parameter Using Differential Evolution , 2012 .

[17]  Slobodan P. Simonovic,et al.  Optimization of Water Distribution Network Design Using Differential Evolution , 2010 .

[18]  Arlen W. Harbaugh,et al.  A modular three-dimensional finite-difference ground-water flow model , 1984 .

[19]  Pei Wang,et al.  An almost-parameter-free harmony search algorithm for groundwater pollution source identification. , 2013, Water science and technology : a journal of the International Association on Water Pollution Research.

[20]  R. Freeze A stochastic‐conceptual analysis of one‐dimensional groundwater flow in nonuniform homogeneous media , 1975 .

[21]  George F. Pinder,et al.  Application of the Digital Computer for Aquifer Evaluation , 1968 .

[22]  D. Karaboga,et al.  A Simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution Algorithm , 2004 .

[23]  Bithin Datta,et al.  Three-Dimensional Groundwater Contamination Source Identification Using Adaptive Simulated Annealing , 2013 .

[24]  H. Karahan,et al.  Discussion of “Estimation of Nonlinear Muskingum Model Parameter Using Differential Evolution” by Dong-Mei Xu, Lin Qiu, and Shou-Yu Chen , 2013 .

[25]  I. Butera,et al.  Simultaneous identification of the pollutant release history and the source location in groundwater by means of a geostatistical approach , 2013, Stochastic Environmental Research and Risk Assessment.

[26]  Mustafa M. Aral,et al.  Identification of Contaminant Source Location and Release History in Aquifers , 2001 .

[27]  Hamid R. Ghafouri,et al.  OPTIMAL IDENTIFICATION OF GROUND-WATER POLLUTION SOURCES , 2007 .

[28]  Bithin Datta,et al.  Identification of Pollution Sources in Transient Groundwater Systems , 2000 .

[29]  Bithin Datta,et al.  Optimal Identification of Ground-Water Pollution Sources and Parameter Estimation , 2001 .

[30]  B. Datta,et al.  Identification of groundwater pollution sources using GA-based linked simulation optimization model , 2006 .

[31]  Ranji S. Ranjithan,et al.  A parallel evolutionary strategy based simulation–optimization approach for solving groundwater source identification problems , 2009 .

[32]  Mustafa M. Aral,et al.  Genetic Algorithms in Search of Groundwater Pollution Sources , 1996 .

[33]  E. C. Childs Dynamics of fluids in Porous Media , 1973 .

[34]  Brian J. Wagner,et al.  Simultaneous parameter estimation and contaminant source characterization for coupled groundwater flow and contaminant transport modelling , 1992 .

[35]  Bithin Datta,et al.  Optimal Monitoring Network and Ground-Water–Pollution Source Identification , 1997 .

[36]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[37]  P. Kitanidis,et al.  Estimation of historical groundwater contaminant distribution using the adjoint state method applied to geostatistical inverse modeling , 2004 .

[38]  G. Mahinthakumar,et al.  Hybrid Genetic Algorithm—Local Search Methods for Solving Groundwater Source Identification Inverse Problems , 2005 .

[39]  Arthur van Dam,et al.  MT3DMS, A Modular Three-Dimensional Multispecies Transport Model , 2011 .

[40]  C. R. Suribabu Differential evolution algorithm for optimal design of water distribution networks , 2010 .