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Hydrogeology Journal

, Volume 23, Issue 6, pp 1129–1154 | Cite as

Review: Coastal groundwater optimization—advances, challenges, and practical solutions

  • Hamed KetabchiEmail author
  • Behzad Ataie-Ashtiani
Paper

Abstract

Decision models are essential tools for coastal groundwater management (CGM). A combined simulation-optimization framework is employed to develop these models. One of the main barriers in the widespread application of these models for real-world cases is their large computational burden. Recent advances in efficient computational approaches and robust optimization methods can crack this barrier. This study surveys the scientific basis of CGM to provide an overview on this subject and reviews the-state-of-the-art to clarify recent developments and to outline ideas for improving the computational performance. Key details are presented on the performance and choice of possible robust tools such as efficient evolutionary algorithms (EAs), surrogate models, and parallel processing techniques. Then, the potential challenges remaining in this context are scrutinized, demonstrating open fields for further research, which include issues related to advances in simulating and optimizing phases such as introducing new robust algorithms and considering multi-objective purposes, implementing novel and high-performance tools, considering global concerns (e.g. climate change impacts), enhancing the existing models to fit the real world, and taking into account the complexities of real-world applications (e.g. uncertainties in the modeling parameters, and data acquisition). Finally, the outcomes of the systematic review are applied to solve a real-world CGM problem in Iran, to quantitatively examine the performance of combined implementation of some of the suggested tools. It is revealed that the required computational time is considerably reduced by as much as three orders of magnitude when correct combinations of robust EAs, surrogate model, and parallelization technique are utilized.

Keywords

Coastal groundwater Optimization Evolutionary algorithms Iran Parallel processing 

Revue: Optimisation des aquifères côtiers—avancées, défis et solutions pratiques

Résumé

Des modèles d’aide à la décision sont des outils essentiels pour la gestion des eaux souterraines côtières (GESC). Un cadre combinant simulation et optimisation est utilisé pour développer ces modèles. Un des principaux obstacles dans l’application généralisée de ces modèles pour des cas réels est leur grande charge de calcul. Les avancées récentes dans les approches efficaces de calcul et les méthodes robustes d’optimisation peuvent faire sauter ces obstacles. Cette étude recense les bases scientifiques de la gestion des eaux souterraines côtières afin de fournir une revue du sujet et examine l’état de l’art afin de clarifier les récents développements et d’esquisser des idées pour l’amélioration des performances de calcul. Des détails clefs sont présentés sur la performance et le choix des outils robustes possibles, tels que les algorithmes d’efficacité évolutionnaires (AEE), les modèles de substitution, et les techniques de calcul en parallèle. Ensuite, les défis potentiels restant dans ce contexte sont examinés, afin de démontrer les champs ouverts pour de plus amples recherches, qui comprennent les questions liées aux progrès des phases de simulation et d’optimisation comme l’introduction de nouveaux algorithmes robustes et la considération d’approches multi-objectifs, la mise en œuvre de nouveaux outils à haute performance, le fait de surmonter des problèmes globaux (impacts du changement climatique par ex.), l’amélioration des modèles existants pour s’adapter au monde réel, et la prise en considération de la complexité des applications du monde réel (par exemple, les incertitudes dans les paramètres de modélisation, et l’acquisition des données). Finalement, les résultats de cette revue systématique sont appliqués pour résoudre un problème réel de GESC en Iran, afin d’examiner quantitativement la performance de la mise en œuvre combinée de certains outils proposés. Cela révèle que le temps de calcul nécessaire est considérablement réduit de plus de trois ordres de grandeur lorsque les combinaisons appropriées d’AEE, de modèle de substitution, et de technique de parallélisation sont utilisées.

Revisión: Optimización de las aguas subterráneas costeras—avances, desafíos y soluciones prácticas

Resumen

Los modelos de decisión son herramientas esenciales para el manejo de las aguas subterráneas costeras (CGM). Se emplea un marco de simulación y optimización combinado para desarrollar estos modelos. Uno de los principales obstáculos en la aplicación generalizada de estos modelos para los casos reales es su gran volumen de cálculo computacional. Los avances recientes en enfoques computacionales eficientes y métodos de optimización robustos pueden romper esta barrera. Este estudio examina la base científica de CGM para proporcionar una visión general sobre este tema y revisa el estado de la técnica para clarificar los desarrollos recientes y esbozar ideas para mejorar el rendimiento computacional. Se presentan los detalles claves sobre el rendimiento y la elección de las posibles herramientas robustas, tales como algoritmos eficientes evolutivos (EA), modelos sustitutos, y las técnicas de procesamiento en paralelo. Luego, se examinan los posibles problemas que subsisten en este contexto, demostrando campos abiertos para futuras investigaciones, que incluyen temas relacionados con los avances en la simulación y la optimización de las etapas, tales como la introducción de nuevos algoritmos robustos y considerando los propósitos múltiples objetivos, la implementación de herramientas nuevas y de alto rendimiento, la superación de los preocupaciones globales (por ejemplo, los impactos del cambio climático), la mejora de los modelos existentes para adaptarse a casos reales, teniendo en cuenta la complejidad de las aplicaciones del caso real (por ejemplo, las incertidumbres en los parámetros de modelado, y adquisición de datos). Por último, los resultados de la revisión sistemática se aplican para resolver un problema CGM de un caso real en Irán, examinando cuantitativamente el rendimiento de la aplicación combinada de algunas de las herramientas sugeridas. Se revela que el tiempo de cálculo requerido se reduce considerablemente en hasta tres órdenes de magnitud cuando se utilizan combinaciones correctas de EAs robustos, modelo sustituto y la técnica de paralelización.

综述:沿海地下水最优化—进展、挑战和实际解决方案

摘要

决策模型是沿海地下水管理的主要工具。采用了组合的模拟—最优化框架来建立这些模型。现实情况下在广泛应用这些模型中一个主要障碍就是巨大的计算负担。高效计算处理和稳健最优化方法中的最新进展可解决这个障碍。这项研究调查了沿海地下水管理的科学基础,对沿海地下水管理进行了综述,阐明了最新进展,概述了改进计算性能的思路。对可能的稳健工具的性能和选择,如高效进化算法、替代模型和并行处理技术的关键细节进行了论述。另外,详细检查了在此背景下的潜在挑战。为进一步的研究展示了室外实验。这些研究包括模拟和最优化阶段诸如引入新的稳健算法、考虑多目标用途、采用新颖和高性能工具、克服全球性的困扰(例如气候变化影响)、增强现有的模型以适应现实情况以及充分考虑现实应用的复杂性(例如模拟参数和数据采集的不确定性)等阶段进展中的有关问题。最后,应用系统综述的成果来解决伊朗一个现实中的沿海地下水管理问题,定量检测了所提出的一些工具组合使用的性能。结果显示,利用稳健的进化算法、替代模型和并行化技术的正确组合,所需计算时间大大减少,减少了三个数量级.

نگاهی مروری بر بهینه سازی آب های زیرزمینی ساحلی—پیشرفت ها، چالش ها و راهکارهای عملی

چکیده

مدل های تصمیم گیری، ابزارهایی لازم برای مدیریت آب های زیرزمینی ساحلی هستند. روش تلفیقی شبیه سازی – بهینه سازی برای توسعه چنین مدل هایی می تواند استفاده شود. یکی از موانع اصلی به کارگیری گسترده این مدل ها در کاربردهای واقعی، زمان محاسباتی زیاد آنها است. پیشرفت های اخیر در رویکردهای محاسباتی کارآمد و روش های توانمند بهینه سازی قادر به رفع این مانع می باشند. این مطالعه به اصول علمی مدیریت آب های زیرزمینی ساحلی می پردازد تا نگاهی مروری بر این موضوع صورت گرفته و توسعه های اخیر در این زمینه مشخص گردد. همچنین به ایده هایی برای بهبود کارآیی زمان محاسباتی نیز پرداخته می شود. همچنین جزئیاتی مهم در زمینه کارآیی ابزارهایی کارآمد مانند الگوریتم های فراکاوشی توانمند، شبه مدل ها و تکنیک های پردازش موازی و نحوه انتخاب آنها ارائه می گردد. سپس به چالش های باقی مانده در این زمینه پرداخته می شود که به موضوع های تحقیقاتی بیشتر در این عرصه اشاره دارد. این موضوع ها مشتمل بر موارد زیر است: پیشرفت ها در زمینه مسائل مرتبط با شبیه سازی و بهینه سازی مانند معرفی الگوریتم های جدید توانمند و در نظر گرفتن مسائل چندهدفی، به کارگیری ابزارهایی جدید و با کارآیی فوق العاده؛ غلبه بر نگرانی های عمده (مانند آثار تغییرات اقلیم)، در نظر داشتن فرضیات منطبق بر واقعیت در مدل های موجود و ملحوظ نمودن پیچیدگی های لازم در کاربردهای واقعی (مانند عدم قطعیت در مشخصه های مدل و جمع آوری داده). در نهایت، نتایج این نگاه مروری نظام مند، در حل یک مسأله واقعی مدیریت آب های زیرزمینی ساحلی در ایران به کار گرفته می شود تا به صورت کمّی هم کارآیی به کارگیری ترکیبی برخی از راهکارهای پیشنهاد شده آزموده شود. مشاهده می شود که با به کارگیری ترکیبی صحیح الگوریتم های فراکاوشی توانمند، شبه مدل و تکنیک پردازش موازی، زمان مورد نیاز محاسباتی به طور قابل ملاحظه ای تا بیش از سه مرتبه نیز کاهش می یابد.

کلمات کلیدی: الگوریتم های فراکاوشی؛ ایران؛ بهینه سازی آب های زیرزمینی ساحلی؛ پردازش موازی؛ مدیریت آب های زیرزمینی

Revisão: Otimização das águas subterrâneas costeiras—avanços, desafios e soluções práticas

Resumo

Modelos de decisão são ferramentas essenciais para a gestão das águas subterrâneas costeiras (GASC). Um arcabouço combinado de simulação-otimização é empregado para desenvolver esses modelos. Uma das principais barreiras na difusão da aplicação desses modelos para casos reais é o grande peso computacional. Avanços recentes em abordagens computacionais eficientes e métodos de otimização robustos podem quebrar essa barreira. Este estudo examina a base científica da GASC para fornecer uma perspectiva sobre o assunto e revisa o estado da arte para esclarecer os recentes desenvolvimentos e delinear ideias para melhorar o desempenho computacional. Os principais detalhes são apresentados no desempenho e na escolha de possíveis ferramentas robustas, tais como algoritmos evolutivos (AE) eficientes, modelos substitutos e técnicas de processamento paralelo. Assim, neste contexto, são analisados os potenciais desafios remanescentes, demonstrando campos abertos para pesquisas futuras, que incluem questões relacionadas aos avanços na simulação e otimização de fases, como a introdução de novos algoritmos robustos e considerando propósitos multiobjectivos, a implementação de ferramentas novas e de alto desempenho, superando preocupações globais (por exemplo, impactos das mudanças climáticas), aprimorando modelos existentes para se ajustarem ao mundo real, e levando em consideração as complexidades de aplicações no mundo real (por exemplo, as incertezas nos parâmetros de modelagem e aquisição de dados). Finalmente, os resultados da revisão sistemática são aplicados para resolver um problema real de GASC no Irã, a fim de analisar quantitativamente o desempenho da implementação combinada de algumas das ferramentas sugeridas. É mostrado que o tempo computacional necessário é reduzido consideravelmente em até três ordens de grandeza quando combinações corretas de AE robustos, modelo substituto e técnica de paralelização são utilizadas.

Notes

Acknowledgements

The authors acknowledge the Iran Kish Free Zone Organization, which provided the data for the real-case study described in this paper. The authors appreciate the constructive comments of two anonymous reviewers and associate editor Dr. George Kourakos, who helped to improve the final paper.

References

  1. Abd-Elhamid FF, Javadi AA (2011) A cost-effective method to control seawater intrusion in coastal aquifers. Water Resour Manag 25:2755–2780Google Scholar
  2. Afshar MH (2007) Partially constrained ant colony optimization algorithm for the solution of constrained optimization problems: application to storm water network design. Adv Water Resour 30(4):954–965Google Scholar
  3. Afshar MH, Ketabchi H, Rasa E (2006) Elitist continuous ant colony optimization algorithm: application to reservoir operation problems. Inter J Civil Eng 4(4):274–285Google Scholar
  4. Amritkar A, Tafti D, Liu R, Kufrin R, Chapman B (2012) Open MP parallelism for fluid and fluid-particulate systems. Parallel Comput 38:501–517Google Scholar
  5. Arndt O, Barth T, Freisleben B, Grauer M (2005) Approximating a finite element model by neural network prediction for facility optimization in groundwater engineering. Eur J Oper Res 166(3):769–781Google Scholar
  6. Arsenault R, Poulin A, Côté P, Brissette F (2014) A comparison of stochastic optimization algorithms in hydrological model calibration. J Hydrol Eng-ASCE 19(7):1374–1384Google Scholar
  7. Ataie-Ashtiani B, Ketabchi H (2011) Elitist continuous ant colony optimization algorithm for optimal management of coastal aquifers. Water Resour Manag 25:165–190Google Scholar
  8. Ataie-Ashtiani B, Rajabi MM, Ketabchi H (2013a) Inverse modeling for freshwater lens in small islands: Kish Island, Persian Gulf. Hydrol Process 27:2759–2773Google Scholar
  9. Ataie-Ashtiani B, Werner AD, Simmons CT, Morgan LK, Lu C (2013b) How important is the impact of land-surface inundation on seawater intrusion caused by sea-level rise? Hydrogeol J 21(7):1673–1677Google Scholar
  10. Ataie-Ashtiani B, Ketabchi H, Rajabi MM (2014) Optimal management of freshwater lens in a small island using surrogate models and evolutionary algorithms. J Hydrol Eng-ASCE 19(2):339–354Google Scholar
  11. Ayvaz MT (2009) Application of harmony search algorithm to the solution of groundwater management models. Adv Water Resour 32:916–924Google Scholar
  12. Ayvaz MT, Karahan H (2008) A simulation/optimization model for the identification of unknown groundwater well locations and pumping rates. J Hydrol 357(1–2):76–92Google Scholar
  13. Banerjee P, Singh VS, Chatttopadhyay K, Chandra PC, Singh B (2011) Artificial neural network model as a potential alternative for groundwater salinity forecasting. J Hydrol 398(3):212–220Google Scholar
  14. Baú DA (2012) Planning of groundwater supply systems subject to uncertainty using stochastic flow reduced models and multi-objective evolutionary optimization. Water Resour Manag 26(9):2513–2536Google Scholar
  15. Bear J, Cheng AHD, Sorek S, Quazarand D, Herrera I (1999) Seawater intrusion in coastal aquifers: concepts, methods and practices. In: Theory and application of transport in porous media. Kluwer, Dordrecht, The NetherlandsGoogle Scholar
  16. Beaudoin A, De Dreuzy JR, Erhel J, Mustapha H (2006) Parallel simulations of underground flow in porous and fractured media parallel computing. In: Joubert G, Nagel W, Peters F, Plata O, Tirado P, Zapata E (eds) Parallel computing: current and future issues of high-end computing. Proceedings of the International Conference ParCo 2005, NIC Series 33, Malaga, Spain, September 2005, John von Neumann Institute for Computing, Julich, Germany, pp 391–398Google Scholar
  17. Bhattacharjya RK, Datta B (2005) Optimal management of coastal aquifers using linked simulation optimization approach. Water Resour Manag 19:295–320Google Scholar
  18. Bhattacharjya RK, Datta B (2009) ANN-GA-based model for multiple objective management of coastal aquifers. J Water Res Plan Manage-ASCE 1355:314–322Google Scholar
  19. Bhattacharjya RK, Datta B, Satish MG (2007) Artificial neural networks approximation of density dependent saltwater intrusion process in coastal aquifers. J Hydrol Eng-ASCE 12(3):273–282Google Scholar
  20. Blanning RW (1975) The construction and implementation of metamodels. Simulation 24(6):177–184Google Scholar
  21. Canot É, de Dieuleveult C, Erhel J (2006) A parallel software for a saltwater intrusion problem. In: Parallel computing: current and future issues of high-end computing. Proceedings of the International Conference ParCo 2005, Malaga, Spain, September 2005, NIC Series 33, John von Neumann Institute for Computing, Julich, Germany, pp 399–406Google Scholar
  22. Cardoso MF, Salcedo RL, Feyo de Azevedo S (1996) The simplex-simulated annealing approach to continuous non-linear optimization. Comput Chem Eng 20(9):1065–1080Google Scholar
  23. Cheng AHD, Halhal D, Naji A, Ouazar D (2000) Pumping optimization in saltwater-intruded coastal aquifers. Water Resour Res 36(8):2155–2166Google Scholar
  24. Cheng T, Mo Z, Shao J (2014) Accelerating groundwater flow simulation in MODFLOW using JASMIN-based parallel computing. Groundwater 52(2):194–205Google Scholar
  25. Civicioglu P, Besdok E (2013) A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif Intell Rev 39(4):315–346Google Scholar
  26. Commer M, Kowalsky MB, Doetsch J, Newman GA, Finsterle S (2014) MPiTOUGH2: a parallel parameter estimation framework for hydrological and hydrogeophysical applications. Comput Geosci 65:127–135Google Scholar
  27. Coumou D, Mattha S, Geiger S, Driesner T (2008) A parallel FE-FV scheme to solve fluid flow in complex geologic media. Comput Geosci 34:1697–1707Google Scholar
  28. D’Ambrosio D, Spataro W, Iovine G (2006) Parallel genetic algorithms for optimizing cellular automata models of natural complex phenomena: an application to debris flows. Comput Geosci 32(7):861–875Google Scholar
  29. Das A, Datta B (1999) Development of multi-objective management models for coastal aquifers. J Water Res Plan Manage-ASCE 125(2):76–87Google Scholar
  30. Das A, Datta B (2000) Optimization based solution of density dependent seawater intrusion in coastal aquifers. J Hydrol Eng-ASCE 5(1):82–89Google Scholar
  31. Dhar A, Datta B (2009) Saltwater intrusion management of coastal aquifers-I: linked simulation-optimization. J Hydrol Eng-ASCE 14(12):1263–1272Google Scholar
  32. Doherty J (2005) PEST: model independent parameter estimation, user manual, 5th edn. Watermark, Brisbane, AustraliaGoogle Scholar
  33. Dong Y, Li G, Xu H (2013) Distributed parallel computing in stochastic modeling of groundwater systems. Groundwater 51(2):293–297Google Scholar
  34. Dorigo M (1992) Optimization, learning and natural algorithms. PhD Thesis, Politecnico di Milano, Milan, ItalyGoogle Scholar
  35. Drees and Sommer AB (2004) Kish Island Integrated Management Plan: Kish Island. Iran Kish Free Zone Organization, Kish Island, Iran; Drees and Sommer, Stuttgart, GermanyGoogle Scholar
  36. Duan Q, Sorooshian S, Gupta VK (1992) Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour Res 28(4):1015–1031Google Scholar
  37. Duan Q, Gupta VK, Sorooshian S (1993) A shuffled complex evolution approach for effective and efficient global minimization. J Optim Theory Appl 76(3):501–521Google Scholar
  38. Elbeltagi E, Hegazy T, Grierson D (2005) Comparison among five evolutionary-based optimization algorithms. Adv Eng Inform 19(1):43–53Google Scholar
  39. Elçi A, Ayvaz TM (2014) Differential-evolution algorithm based optimization for the site selection of groundwater production wells with the consideration of the vulnerability concept. J Hydrol 511:736–749Google Scholar
  40. Emch PG, Yeh WWG (1998) Management model for conjunctive use of coastal surface water and groundwater. J Water Res Plan Manage-ASCE 124:129–139Google Scholar
  41. Erhel J, de Dreuzy JR, Beaudoin A, Bresciani E, Tromeur-Dervout D (2009) A parallel scientific software for heterogeneous hydrogeoloy. In: Parallel Computational Fluid Dynamics 2007: implementations and experiences on large scale and grid computing (Lecture notes in computational science and engineering). Springer, Berlin, pp 39–48Google Scholar
  42. Falkland A (1991) Hydrology and water resources of small islands: a practical guide—a contribution to the International Hydrological Programme. UNESCO, ParisGoogle Scholar
  43. Ferreira da Silva JF, Haie N (2007) Optimal locations of groundwater extractions in coastal aquifers. Water Resour Manag 21:1299–1311Google Scholar
  44. Finney BA, Samsuhadi A, Willis R (1992) Quasi-three-dimensional optimization of Jakarta Basin. J Water Res Plan Manage-ASCE 118(1):18–31Google Scholar
  45. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, New YorkGoogle Scholar
  46. Gaur S, Chahar BR, Graillot D (2011) Analytic elements method and particle swarm optimization based simulation-optimization model for groundwater management. J Hydrol 402(3):217–227Google Scholar
  47. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68Google Scholar
  48. Gerritsen MG, Löf H, Thiele MR (2009) Parallel implementations of streamline simulators. Comput Geosci 13(1):135–149Google Scholar
  49. Ghassemi F, Alam K, Howard K (2000) Freshwater lenses and practical limitations of their three-dimensional simulation. Hydrogeol J 8:521–537Google Scholar
  50. Giroux B, Larouche B (2013) Task-parallel implementation of 3D shortest path raytracing for geophysical applications. Comput Geosci 54:130–141Google Scholar
  51. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, BostonGoogle Scholar
  52. Guan J, Kentel E, Aral MM (2008) Genetic algorithm for constrained optimization models and its application in groundwater resources management. J Water Res Plan Manage-ASCE 134(1):64–72Google Scholar
  53. Guo Y, Ko J (2013) Cost optimization for a large-scale hybrid central cooling plant with multiple energy sources under a complex electricity cost structure. HVAC&R Res 19(6):754–763Google Scholar
  54. He K, Zheng L, Dong S, Tang L, Wu J, Zheng C (2007) PGO: a parallel computing platform for global optimization based on genetic algorithm. Comput Geosci 33:357–366Google Scholar
  55. Hemker T, Fowler KR, Farthing MW, von Stryk O (2008) A mixed-integer simulation-based optimization approach with surrogate functions in water resources management. Optim Eng 9(4):341–360Google Scholar
  56. Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Ann Arbor, MIGoogle Scholar
  57. Huang C, Mayer AS (1997) Pump-and-treat optimization using well locations and pumping rates as decision variables. Water Resour Res 33(5):1001–1012Google Scholar
  58. Hunt RJ, Doherty J, Tonkin MJ (2007) Are models too simple? Arguments for increased parameterization. Groundwater 45(3):254–262Google Scholar
  59. Hwang HT, Park YJ, Sudicky EA, Forsyth PA (2014) A parallel computational framework to solve flow and transport in integrated surface–subsurface hydrologic systems. Environ Model Softw 61:39–58Google Scholar
  60. Javadi AA, Abd-Elhamid HF, Farmani R (2012) A simulation-optimization model to control seawater intrusion in coastal aquifers using abstraction/recharge wells. Int J Numer Anal Meth Geomech 3616:1757–1779Google Scholar
  61. Javadi AA, Hussain M, Sherif M, Farmani R (2015) Multi-objective optimization of different management scenarios to control seawater intrusion in coastal aquifers. Water Resour Manage. doi: 10.1007/s11269-015-0914-1 Google Scholar
  62. Jin HQ, Jespersen D, Mehrotra P, Biswas R, Huang L, Chapman B (2011) High performance computing using MPI and OpenMP on multi-core parallel systems. Parallel Comput 37:562–575Google Scholar
  63. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-TR06, Computer Engineering, Erciyes University, Kayseri, TurkeyGoogle Scholar
  64. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132Google Scholar
  65. Karterakis SM, Karatzas GP, Nikolos IK, Papadopoulou MP (2007) Application of linear programming and differential evolutionary optimization methodologies for the solution of coastal subsurface water management problems subject to environmental criteria. J Hydrol 342:270–282Google Scholar
  66. Katsifarakis KL, Petala Z (2006) Combining genetic algorithms and boundary elements to optimize coastal aquifers management. J Hydrol 327:200–207Google Scholar
  67. Katsifarakis KL, Tselepidou K (2009) Pumping cost minimization in aquifers with regional flow and two zones of different transmissivities. J Hydrol 377(1–2):106–111Google Scholar
  68. Kaveh A, Talatahari S (2009) Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Comput Struct 87(5):267–283Google Scholar
  69. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Piscataway, NJ, Nov/Dec 1995, pp 1942–1948Google Scholar
  70. Kerrou J, Renard P (2010) A numerical analysis of dimensionality and heterogeneity effects on advective dispersive seawater intrusion processes. Hydrogeol J 18(1):55–72Google Scholar
  71. Ketabchi H, Ataie-Ashtiani B (2015) Evolutionary algorithms for the optimal management of coastal groundwater: a comparative study toward future challenges. J Hydrol 520:193–213Google Scholar
  72. Ketabchi H, Mahmoodzadeh D, Ataie-Ashtiani B, Werner AD, Simmons CT (2014) Sea-level rise impact on fresh groundwater lenses in two-layer small islands. Hydrol Process 28:5938–5953Google Scholar
  73. Khadem M, Afshar MH (2014) A hybridized GA with LP-LP model for the management of confined groundwater. Groundwater. doi: 10.1111/gwat.12234 Google Scholar
  74. Khan IA (1982) A model for managing irrigated agriculture. Water Resour Bull 18:81–87Google Scholar
  75. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680Google Scholar
  76. Kish Free Zone Organization (2006) General annual reports. Kish Free Zone Organization, Kish Island, IranGoogle Scholar
  77. Kollat JB, Reed PM (2006) Comparing state-of-the-art evolutionary multi-objective algorithms for long-term groundwater monitoring design. Adv Water Resour 29(6):792–807Google Scholar
  78. Kollet SJ, Maxwell RM (2006) Integrated surface-groundwater flow modeling: a free-surface overland flow boundary condition in a parallel groundwater flow model. Adv Water Resour 29(7):945–958Google Scholar
  79. Kollet SJ, Maxwell RM, Woodward CS, Smith S, Vanderborght J, Vereecken H, Simmer C (2010) Proof of concept of regional scale hydrologic simulations at hydrologic resolution utilizing massively parallel computer resources. Water Resour Res 46(4):W04201Google Scholar
  80. Kourakos G, Harter T (2014) Parallel simulation of groundwater non-point source pollution using algebraic multigrid preconditioners. Computat Geosci 18(5):851–867Google Scholar
  81. Kourakos G, Mantoglou A (2009) Pumping optimization of coastal aquifers based on evolutionary algorithms and surrogate modular neural network models. Adv Water Resour 32:507–521Google Scholar
  82. Kourakos G, Mantoglou A (2011) Simulation and multi-objective management of coastal aquifers in semi-arid regions. Water Resour Manag 25:1063–1074Google Scholar
  83. Kourakos G, Mantoglou A (2013) Development of a multi-objective optimization algorithm using surrogate models for coastal aquifer management. J Hydrol 479:13–23Google Scholar
  84. Leandro J, Chen AS, Schumann A (2014) A 2D parallel diffusive wave model for floodplain inundation with variable time step (P-DWave). J Hydrol 517:250–259Google Scholar
  85. Li T, Wang G, Chen J, Wang H (2011) Dynamic parallelization of hydrological model simulations. Environ Modell Softw 26(12):1736–1746Google Scholar
  86. Li X, Wei J, Li T, Wang G, Yeh WWG (2014) A parallel dynamic programming algorithm for multi-reservoir system optimization. Adv Water Resour 67:1–15Google Scholar
  87. Liu D, Chen X, Lou Z (2010) A model for the optimal allocation of water resources in a saltwater intrusion area: a case study in Pearl River Delta in China. Water Resour Manag 24:63–81Google Scholar
  88. Ma H, Simon D, Fei M, Chen Z (2013) On the equivalences and differences of evolutionary algorithms. Eng Appl Artif Intel 26(10):2397–2407Google Scholar
  89. Madadgar M, Afshar A (2009) An improved continuous ant algorithm for optimization of water resources problems. Water Resour Manage 23:2119–2139Google Scholar
  90. Mahmoodzadeh D, Ketabchi H, Ataie-Ashtiani B, Simmons CT (2014) Conceptualization of a fresh groundwater lens influenced by climate change: a modeling study of an arid-region island in the Persian Gulf, Iran. J Hydrol 519:399–413Google Scholar
  91. Maier HR, Kapelan Z, Kasprzyk J, Kollat J, Matott LS, Cunha MC, Dandy GC, Gibbs MS, Keedwell E, Marchi A, Ostfeld A, Savic D, Solomatine DP, Vrugt JA, Zecchin AC, Minsker BS, Barbour EJ, Kuczera G, Pasha F, Castelletti A, Giuliani M, Reed PM (2014) Evolutionary algorithms and other metaheuristics in water resources: current status, research challenges and future directions. Environ Modell Softw 62:271–299Google Scholar
  92. Mantoglou A (2003) Pumping management of coastal aquifers using analytical models of saltwater intrusion. Water Resour Res 39(12):1335Google Scholar
  93. Mantoglou A, Papantoniou M (2008) Optimal design of pumping networks in coastal aquifers using sharp interface models. J Hydrol 361:52–63Google Scholar
  94. Mantoglou A, Papantoniou M, Giannoulopoulos P (2004) Management of coastal aquifers based on nonlinear optimization and evolutionary algorithms. J Hydrol 97:209–228Google Scholar
  95. Maris V, Wannamaker PE (2010) Parallelizing a 3D finite difference MT inversion algorithm on a multicore PC using OpenMP. Comput Geosci 36(10):1384–1387Google Scholar
  96. Matott LS, Tolson BA, Asadzadeh M (2012) A benchmarking framework for simulation-based optimization of environmental models. Environ Modell Softw 35:19–30Google Scholar
  97. Maxwell RM (2013) A terrain-following grid transform and pre-conditioner for parallel, large-scale, integrated hydrologic modeling. Adv Water Resour 53:109–117Google Scholar
  98. Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inf 1(4):355–366Google Scholar
  99. Mirghani BY, Mahinthakumar KG, Tryby ME, Ranjithan RS, Zechman EM (2009) A parallel evolutionary strategy based simulation-optimization approach for solving groundwater source identification problems. Adv Water Resour 32:1373–1385Google Scholar
  100. Mustapha H, Ghorayeb A, Mustapha KA (2010) Underground flow simulations using parallel finite element method. Comput Geosci 36(2):161–166Google Scholar
  101. Neal JC, Fewtrell TJ, Trigg M (2009) Parallelisation of storage cell flood models using OpenMP. Environ Modell Softw 24:872–877Google Scholar
  102. Neal JC, Fewtrell TJ, Bates PD, Wright NG (2010) A comparison of three parallelisation methods for 2D flood inundation models. Environ Modell Softw 25(4):398–411Google Scholar
  103. Nelder JA, Mead R (1965) A Simplex method for function minimization. Comput J 7:308–313Google Scholar
  104. Nguyen AT, Reiter S, Rigo P (2014) A review on simulation-based optimization methods applied to building performance analysis. Appl Energ 113:1043–1058Google Scholar
  105. Nicklow J, Reed PM, Savic D, Dessalegne T, Harrell L, Chan-Hilton A, Karamouz M, Minsker B, Ostfeld A, Singh A, Zechman E (2010) State of the art for genetic algorithm and beyond in water resources planning and management. J Water Resour Plan Manage-ASCE 136(4):412–432Google Scholar
  106. O’donncha F, Ragnoli E, Suits F (2014) Parallelisation study of a three-dimensional environmental flow model. Comput Geosci 64:96–103Google Scholar
  107. Ojha R, Ramadas M, Govindaraju RS (2015) Current and future challenges in groundwater: I. modeling and management of resources. J Hydrol Eng-ASCE 20(1); CID: A4014007Google Scholar
  108. Papadopoulou MP, Nikolos IK, Karatzas GP (2010) Computational benefits using artificial intelligent methodologies for the solution of an environmental design problem: saltwater intrusion. Water Sci Technol 62(7):1479–1490Google Scholar
  109. Parhami B (2002) Introduction to parallel processing, algorithms and architectures, University of California, Santa Barbara, CA; Kluwer, Dordrecht, The NetherlandsGoogle Scholar
  110. Park CH, Aral MM (2004) Multi-objective optimization of pumping rates and well placement in coastal aquifers. J Hydrol 290:80–99Google Scholar
  111. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22:52–67Google Scholar
  112. Pedemonte M, Nesmachnow S, Cancela H (2011) A survey on parallel ant colony optimization. Appl Soft Comput 11(8):5181–5197Google Scholar
  113. Pham DT, Castellani M (2014) Benchmarking and comparison of nature-inspired population-based continuous optimization algorithms. Soft Comput 18(5):871–903Google Scholar
  114. Pourtakdoust SH, Nobahari H (2004) An extension of ant colony system to continuous optimization problems. In: Ant colony optimization and swarm intelligence. Springer, Heidelberg, Germany, pp 294–301Google Scholar
  115. Qahman KH, Larabi A, Quazar D, Naji A, Cheng AHD (2005) Optimal and sustainable extraction of groundwater in coastal aquifers. Stoch Environ Res Risk Assess 19:99–110Google Scholar
  116. Qahman K, Larabi A, Ouazar D, Naji A, Cheng AHD (2009) Optimal extraction of groundwater in Gaza coastal aquifer. J Water Resour Protect 4:249–259Google Scholar
  117. Qin XS, Huang GH, He L (2009) Simulation and optimization technologies for petroleum waste management and remediation process control. J Environ Manage 90(1):54–76Google Scholar
  118. Rajabi MM, Ataie-Ashtiani B (2014) Sampling efficiency in Monte Carlo based uncertainty propagation strategies: application in seawater intrusion simulations. Adv Water Resour 67:46–64Google Scholar
  119. Rajabi MM, Ataie-Ashtiani B, Simmons CT (2015) Polynomial chaos expansions for uncertainty propagation and moment independent sensitivity analysis of seawater intrusion simulations. J Hydrol 520:101–122Google Scholar
  120. Rao SS (2009) Engineering optimization: theory and practice. Wiley, Chichester, UK, 813 ppGoogle Scholar
  121. Rao SVN, Thandaveswara BS, Bhallamudi SM, Srivivasulu V (2003) Optimal groundwater management in deltaic regions using simulated annealing and neural networks. Water Resour Manage 17:409–428Google Scholar
  122. Rao SVN, Sreenivasulu V, Bhallamudi SM, Thandaveswara BS, Sudheer KP (2004) Planning groundwater development in coastal aquifers. Hydrolog Sci J 49:155–170Google Scholar
  123. Razavi S, Tolson BA, Burn DH (2012) Review of surrogate modeling in water resources. Water Resour Res 48(7):W07401Google Scholar
  124. Rechenberg I (1965) Cybernetic solution path of an experimental problem, library translation no. 1122. Ministry of Aviation, Royal Aircraft Establishment, Farnborough, Hants, UKGoogle Scholar
  125. Reed PM, Kollat JB (2013) Visual analytics clarify the scalability and effectiveness of massively parallel many-objective optimization: a groundwater monitoring design example. Adv Water Resour 56:1–13Google Scholar
  126. Reed PM, Hadka D, Herman JD, Kasprzyk JR, Kollat JB (2013) Evolutionary multi-objective optimization in water resources: the past, present, and future. Adv Water Resour 51:438–456Google Scholar
  127. Riegels N, Pulido-Velazquez M, Doulgeris C, Sturm V, Jensen R, Møller F, Bauer-Gottwein P (2013) Systems analysis approach to the design of efficient water pricing policies under the EU water framework directive. J Water Res Plan Manage-ASCE 139(5):574–582Google Scholar
  128. Sayeed M, Mahinthakumar GK (2005) Efficient parallel implementation of hybrid optimization approaches for solving groundwater inverse problems. J Comput Civil Eng-ASCE 19(4):329–340Google Scholar
  129. Sedki A, Ouazar D (2011) Simulation-optimization modeling for sustainable groundwater development: a Moroccan coastal aquifer case study. Water Resour Manage 25:2855–2875Google Scholar
  130. Shamir U, Bear J, Gamiliel A (1984) Optimal annual operation of a coastal aquifer. Water Resour Res 20(4):435–444Google Scholar
  131. Simmons CT (2005) Variable density groundwater flow: from current challenges to future possibilities. Hydrogeol J 13(1):116–119Google Scholar
  132. Singh A (2012) An overview of the optimization modeling applications. J Hydrol 466:167–182Google Scholar
  133. Singh A (2014) Optimization modeling for seawater intrusion management. J Hydrol 508:43–52Google Scholar
  134. Singh A (2015) Managing the environmental problem of seawater intrusion in coastal aquifers through simulation-optimization modeling. Ecol Indic 48:498–504Google Scholar
  135. Sitanggang KI, Lynett P (2005) Parallel computation of a highly nonlinear Boussinesq equation model through domain decomposition. Int J Numer Meth Fl 49(1):57–74Google Scholar
  136. Sophocleous M (2010) Review: groundwater management practices, challenges, and innovations in the High Plains aquifer, USA: lessons and recommended actions. Hydrogeol J 18(3):559–575Google Scholar
  137. Sreekanth J, Datta B (2010) Multi-objective management of saltwater intrusion in coastal aquifers using genetic programming and modular neural network based surrogate models. J Hydrol 393(3):245–256Google Scholar
  138. Sreekanth J, Datta B (2011a) Comparative evaluation of genetic programming and neural network as potential surrogate models for coastal aquifer management. Water Resour Manage 25(13):3201–3218Google Scholar
  139. Sreekanth J, Datta B (2011b) Coupled simulation‐optimization model for coastal aquifer management using genetic programming‐based ensemble surrogate models and multiple‐realization optimization. Water Resour Res 47(4):W04516Google Scholar
  140. Sreekanth J, Datta B (2014) Stochastic and robust multi-objective optimal management of pumping from coastal aquifers under parameter uncertainty. Water Resour Manage 28(7):2005–2019Google Scholar
  141. Stigter TY, Nunes JP, Pisani B, Fakir Y, Hugman R, Li Y, Tome S, Ribeiro L, Samper J, Oliveira R, Monteiro JP, Silva A, Tavares PCF, Shapouri M, Cancela da Fonseca L, El Himer H (2014) Comparative assessment of climate change and its impacts on three coastal aquifers in the Mediterranean. Reg Environ Change 14(1):41–56Google Scholar
  142. Storn R, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11:341–359Google Scholar
  143. Tang Y, Reed PM, Kollat JB (2007) Parallelization strategies for rapid and robust evolutionary multiobjective optimization in water resources applications. Adv Water Res 30(3):335–353Google Scholar
  144. Tang G, D’Azevedo EF, Zhang F, Parker JC, Watson DB, Jardine PM (2010) Application of a hybrid MPI/OpenMP approach for parallel groundwater model calibration using multi-core computers. Comput Geosci 36(11):1451–1460Google Scholar
  145. Van Genuchten MT (1980) A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci Soc Am J 44(5):892–898Google Scholar
  146. Voss CI, Provost AM (2010) SUTRA: a model for saturated-unsaturated, variable-density groundwater flow with solute or energy transport. US Geol Surv Water Resour Invest Rep 02-4231Google Scholar
  147. Wang C, Duan Q, Gong W, Ye A, Di Z, Miao C (2014) An evaluation of adaptive surrogate modeling based optimization with two benchmark problems. Environ Modell Softw 60:167–179Google Scholar
  148. Watson IA, Crouch RS, Bastian P, Oswald SE (2005) Advantages of using adaptive remeshing and parallel processing for modeling biodegradation in groundwater. Adv Water Resour 28:1143–1158Google Scholar
  149. Werner AD, Bakker M, Post VE, Vandenbohede A, Lu C, Ataie-Ashtiani B, Simmons CT, Barry DA (2013) Seawater intrusion processes, investigation and management: recent advances and future challenges. Adv Water Resour 51:3–26Google Scholar
  150. Wheeler MF, Peszyńska M (2002) Computational engineering and science methodologies for modeling and simulation of subsurface applications. Adv Water Resour 25(8):1147–1173Google Scholar
  151. White I, Falkland T (2010) Management of freshwater lenses on small Pacific islands. Hydrogeol J 18(1):227–246Google Scholar
  152. Willis R, Finney B (1988) Planning model for optimal control of saltwater intrusion. J Water Res Plan Manag ASCE 114:333–347Google Scholar
  153. Yan S, Minsker B (2006) Optimal groundwater remediation design using an Adaptive Neural Network Genetic Algorithm. Water Resour Res 42(5):W05407Google Scholar
  154. Yan S, Minsker B (2011) Applying dynamic surrogate models in noisy genetic algorithms to optimize groundwater remediation designs. J Water Resour Plan Manage ASCE 137(3):284–292Google Scholar
  155. Yang XS, Deb S (2010) Engineering optimization by cuckoo search. Int J Math Model Numer Optim 1(4):330–343Google Scholar
  156. Yang Y, Wu J, Sun X, Wu J, Zheng C (2013) Development and application of a master-slave parallel hybrid multi-objective evolutionary algorithm for groundwater remediation design. Environ Earth Sci 70(6):2481–2494Google Scholar
  157. Yu D (2010) Parallelization of a two-dimensional flood inundation model based on domain decomposition. Environ Modell Softw 25:935–945Google Scholar
  158. Zhang K, Wu YS, Bodvarsson GS (2003) Massively parallel computing simulation of fluid flow in the unsaturated zone of Yucca Mountain, Nevada. J Contam Hydrol 62:381–399Google Scholar
  159. Zhang S, Xia Z, Yuan R, Jiang X (2014) Parallel computation of a dam-break flow model using OpenMP on a multi-core computer. J Hydrol 512:126–133Google Scholar

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© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Civil EngineeringSharif University of TechnologyTehranIran

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