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Water Resources Management

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16.05.2017

Modern Optimization Methods in Water Resources Planning, Engineering and Management

verfasst von: Gokmen Tayfur

Erschienen in: Water Resources Management | Ausgabe 10/2017

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Abstract

Mathematical (analytical, numerical and optimization) models are employed in many disciplines including the water resources planning, engineering and management. These models can vary from a simple black-box model to a sophisticated distributed physics-based model. Recently, development and employment of modern optimization methods (MOMs) have become popular in the area of mathematical modeling. This paper overviews the MOMs based on the evolutionary search which were developed over mostly the last 30 years. These methods have wide application in practice from finance to engineering and this paper focuses mostly on the applications in the area of water resources planning, engineering and management. Although there are numerous optimization algorithms, the paper outlines the ones that have been widely employed especially in the last three decades; such as the Genetic Algorithm (GA), Ant Colony (AC), Differential Evolution (DE), Particle Swarm (PS), Harmony Search (HS), Genetic Programming (GP), and Gene Expression Programming (GEP). The paper briefly introduces theoretical background of each algorithm and its applications and discusses the merits and, if any, shortcomings. The wide spectrum of applications include, but not limited to, flood control and mitigation, reservoir operation, irrigation, flood routing, river training, flow velocity, rainfall-runoff processes, sediment transport, groundwater management, water quality, hydropower, dispersion, and aquifers.

Vorheriger Artikel Informational Entropy: a Failure Tolerance and Reliability Surrogate for Water Distribution Networks

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Titel: Modern Optimization Methods in Water Resources Planning, Engineering and Management
verfasst von: Gokmen Tayfur
Publikationsdatum: 16.05.2017
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 10/2017
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-017-1694-6

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