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

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

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

综述:沿海地下水最优化—进展、挑战和实际解决方案

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

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

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.

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.

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.

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.

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

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.

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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.

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Published in the theme issue “Optimization for Groundwater Characterization and Management”

Appendix: Notation

Appendix: Notation

The following notation is used in this paper:

A j :

Surface area of the jth management zone

b w :

Pitch adjustment bandwidth for HS

C 1 :

Acceleration constant related to local best positions for PSO

C 2 :

Acceleration constant related to global best positions for PSO

c i,0 :

Salinity value of ith observation well at the start of the 50-year period

c i,50 :

Salinity value of ith observation well at the end of the 50-year period

C sc :

Penalty coefficient

E n :

Parallel efficiency

F DE :

Control parameter of differential variations for DE

F modified :

Modified objective function value of the management problem

N no. :

Number of generations with no improvement in the objective-function value

N tot :

Number of total generations from the beginning of computation

q SCE :

Number of simplexes for SCE

S n :

Speedup ratio

T nP :

Computational time of the parallel model with n threads

T S :

Computational time of the serial model

W j :

Net recharge rate of the jth management zone

α SCE :

Number of consecutive new solutions generated by the same simplexes for SCE

β SCE :

Number of evolutions of each complex before complexes are shuffled for SCE

ANN:

Artificial neural network

CACO:

Continuous ant colony optimization

CGM:

Coastal groundwater management

CR:

Crossover probability of DE

DE:

Differential evolution

EA:

Evolutionary algorithm

F :

Objective-function value of the management problem

GA:

Genetic algorithm

GP:

Genetic programming

HMCR:

Harmony consideration rate for HS

HS:

Harmony search

n :

Number of threads

NP:

Number of points in each complex for SCE

PAR:

Pitch adjustment rate for HS

Pen:

Penalty function

Pop:

EA’s population size

PSO:

Particle swarm optimization

SCE:

Shuffled complex evolution

SWI:

Seawater intrusion

TS n :

The time saving ratio

ω :

Inertia or momentum weight for PSO

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Ketabchi, H., Ataie-Ashtiani, B. Review: Coastal groundwater optimization—advances, challenges, and practical solutions. Hydrogeol J 23, 1129–1154 (2015). https://doi.org/10.1007/s10040-015-1254-1

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Keywords

  • Coastal groundwater
  • Optimization
  • Evolutionary algorithms
  • Iran
  • Parallel processing