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Approaches to optimal aquifer management and intelligent control in a multiresolutional decision support system

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Abstract

Despite remarkable new developments in stochastic hydrology and adaptations of advanced methods from operations research, stochastic control, and artificial intelligence, solutions of complex real-world problems in hydrogeology have been quite limited. The main reason is the ultimate reliance on first-principle models that lead to complex, distributed-parameter partial differential equations (PDE) on a given scale. While the addition of uncertainty, and hence, stochasticity or randomness has increased insight and highlighted important relationships between uncertainty, reliability, risk, and their effect on the cost function, it has also (a) introduced additional complexity that results in prohibitive computer power even for just a single uncertain/random parameter; and (b) led to the recognition in our inability to assess the full uncertainty even when including all uncertain parameters. A paradigm shift is introduced: an adaptation of new methods of intelligent control that will relax the dependency on rigid, computer-intensive, stochastic PDE, and will shift the emphasis to a goal-oriented, flexible, adaptive, multiresolutional decision support system (MRDS) with strong unsupervised learning (oriented towards anticipation rather than prediction) and highly efficient optimization capability, which could provide the needed solutions of real-world aquifer management problems. The article highlights the links between past developments and future optimization/planning/control of hydrogeologic systems.

Résumé

Malgré de remarquables nouveaux développements en hydrologie stochastique ainsi que de remarquables adaptations de méthodes avancées pour les opérations de recherche, le contrôle stochastique, et l’intelligence artificielle, solutions pour les problèmes complexes en hydrogéologie sont restées assez limitées. La principale raison est l’ultime confiance en les modèles qui conduisent à des équations partielles complexes aux paramètres distribués (PDE) à une échelle donnée. Alors que l’accumulation d’incertitudes et, par conséquent, la stockasticité ou l’aléat a augmenté la perspicacité et a mis en lumière d’importantes relations entre l’incertitude, la fiabilité, le risque, et leur effet sur les coûts de fonctionnement, il a également (a) introduit une complexité additionnelle qui résulte dans un pouvoir prohibitif des moyens de calcul informatique même pour une simple estimation de l’incertitude; et (b) a conduit a une reconnaissance de notre manque d’aptitude à maîtriser l’incertitude totale même en introduisant tous les paramètres connus de l’incertitude. La représentation du changement est introduit: une adaptation de nouvelles méthodes de contrôle intelligent qui va relâcher la dépendance à la rigidité des algorithmes, aux calculs informatiques intensifs, à la PDE stockastique, et qui modifiera l’emphase entre les MRDS—systèmes interactifs d’aide à la décision de multiresolutionelle (flexibles, adaptables et orientables selon les objectifs)—avec un fort apprentissage non (orienté vers l’anticipation plutôt que la prédiction), et une capacité d’optimisation efficiente très élevée, qui pourrait apporter le besoin de solutions pour la modélisation des problèmes de management des aquifères réalistes. Cet article met en lumière les liens entre les développements passés et les futurs moyens d’optimisation, de gestion et de contrôle des systèmes hydrogéologiques.

Resumen

A pesar de nuevos avances notables en hidrología estocástica y las adaptaciones de métodos avanzados de investigación de operaciones, control estocástico, e inteligencia artificial, las soluciones de problemas complejos del mundo real en hidrogeología han sido bastante limitadas. La principal razón es la dependencia definitiva en modelos de primer-principio que conducen a ecuaciones parciales diferencias de parámetro distribuido complejas (PDE) a una escala dada. Mientras que la adición de incertidumbre, y por lo tanto, estocasticidad o aleatoriedad ha incrementado la profundidad y resaltado relaciones importantes entre la incertidumbre, confiabilidad, riesgo, y su efecto en la función de costo, la adición también ha permitido (a) introducir complejidad adicional que resulta en potencia computacional excesiva aún para un solo parámetro incierto/aleatorio; y (b) llevar a reconocer nuestra discapacidad para evaluar la incertidumbre completa aún cuando se incluyen todos los parámetros inciertos. Se introduce un cambio paradigmático: una adaptación de nuevos métodos de control de inteligencia que relajará la dependencia en PDE estocásticas, rígidas y de uso computacional intensivo, cambiando el énfasis hacia un sistema de apoyo de decisiones de propósitos múltiples (MRDS) adaptivo, flexible, y orientado a objetivos con fuerte aprendizaje sin supervisión (orientado a la anticipación más que a la predicción) con fuerte capacidad de optimización eficiente, lo cual podría aportar las soluciones necesarias a los problemas de manejo reales con los acuíferos. El artículo resalta los vínculos entre desarrollos pasados y control/planificación/optimización futura de sistemas hidrogeológicos.

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Notes

  1. First-order Taylor series expansion of perturbations about the mean (parameters and state variables).

  2. Optimization cannot exist without a representation, which is a model/simulator based on characterization.

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Acknowledgement

We would like to acknowledge Dr. Cliff Voss (Executive Editor), Mr. Perry Olcott (Mananging Editor), and the three reviewers, particularly Dr. Peter Kitanidis, for their excellent and constructive comments; as well as several individuals who helped us bridge among disciplines, particularly, Mr. Tom Anderson (RMOTC), Dr. Xian-Juan Wen (ChevronTexaco), Dr. Dong Zhang (U. of Oklahoma), Dr. S.P. Neumann (U. of Arozina), and Dr. Larry Lake (U. of Texas).

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  1. MRDS Inc., 5900 W. 25th Avenue, Kennewick, WA, 99338, USA

    Shlomo Orr

  2. ECE Department, Drexel University, 3141 Chestnut Street, Philadelphia, PA, 19104, USA

    Alexander M. Meystel

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  1. Shlomo Orr
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  2. Alexander M. Meystel
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Correspondence to Shlomo Orr.

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Orr, S., Meystel, A.M. Approaches to optimal aquifer management and intelligent control in a multiresolutional decision support system. Hydrogeol J 13, 223–246 (2005). https://doi.org/10.1007/s10040-004-0424-3

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  • DOI: https://doi.org/10.1007/s10040-004-0424-3

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