Application of particle swarm optimization to water management: an introduction and overview

Jahandideh-Tehrani, Mahsa; Bozorg‐Haddad, Omid; Loáiciga, Hugo A.

doi:10.1007/s10661-020-8228-z

Cited by 79 publications

(33 citation statements)

References 110 publications

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“…Various algorithms have their own strengths in solving optimization problems, but the application of PSO in practical applications is more flexible. Its advantages lie in its simple implementation and powerful functions, and it is feasible in terms of the parameter optimization of mathematical methods for groundwater quality evaluation [26,27].…”

Section: ) Comparison Of Pso and Other Optimization Algorithmsmentioning

confidence: 99%

Groundwater Quality Evaluation Based on PCA-PSO-SVM Machine Learning in Xinzhou City, China

Ni¹,

Yao²,

Song³

et al. 2022

Pol. J. Environ. Stud.

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The scientific evaluation of water quality change trends and pollution characteristics is of great significance to improving the current situation of water resources. The particle swarm optimization support vector machine based on principal component analysis (PCA-PSO-SVM) was used to conduct a comprehensive evaluation of groundwater quality in Xinzhou city, and the results were compared with those of a variety of traditional water quality evaluation methods. The evaluation results show that the water quality evaluation model based on PCA-PSO-SVM is more comprehensive and objective. The overall groundwater quality situation in Xinzhou city is generally good. The upper pore water quality is mainly class II, and the lower karst water is mainly class III. The water quality shows significant spatial differences. Compared with the traditional water quality evaluation method, the improved SVM algorithm compensates for the defects of the traditional method. The model structure is stable, and the accuracy and calculation efficiency are high, which makes the method worthy of promotion and application. The research results can provide a scientific basis and reference value for the water quality evaluation of groundwater-related projects.

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Section: ) Comparison Of Pso and Other Optimization Algorithmsmentioning

confidence: 99%

Groundwater Quality Evaluation Based on PCA-PSO-SVM Machine Learning in Xinzhou City, China

Ni¹,

Yao²,

Song³

et al. 2022

Pol. J. Environ. Stud.

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“…Bio-inspired techniques have been mainly employed as path planners and monitoring algorithms for water resources [15]. Bio-inspired techniques based on Swarm Intelligence (SI) have the advantage of working with several agents/particles simultaneously.…”

Section: Related Workmentioning

confidence: 99%

An Informative Path Planner for a Swarm of ASVs Based on an Enhanced PSO with Gaussian Surrogate Model Components Intended for Water Monitoring Applications

2021

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Controlling the water quality of water supplies has always been a critical challenge, and water resource monitoring has become a need in recent years. Manual monitoring is not recommended in the case of large water surfaces for a variety of reasons, including expense and time consumption. In the last few years, researchers have proposed the use of autonomous vehicles for monitoring tasks. Fleets or swarms of vehicles can be deployed to conduct water resource explorations by using path planning techniques to guide the movements of each vehicle. The main idea of this work is the development of a monitoring system for Ypacarai Lake, where a fleet of autonomous surface vehicles will be guided by an improved particle swarm optimization based on the Gaussian process as a surrogate model. The purpose of using the surrogate model is to model water quality parameter behavior and to guide the movements of the vehicles toward areas where samples have not yet been collected; these areas are considered areas with high uncertainty or unexplored areas and areas with high contamination levels of the lake. The results show that the proposed approach, namely the enhanced GP-based PSO, balances appropriately the exploration and exploitation of the surface of Ypacarai Lake. In addition, the proposed approach has been compared with other techniques like the original particle swarm optimization and the particle swarm optimization with Gaussian process uncertainty component in a simulated Ypacarai Lake environment. The obtained results demonstrate the superiority of the proposed enhanced GP-based PSO in terms of mean square error with respect to the other techniques.

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“…As hydraulic connections and compensatory relationships between the various reservoirs are very complicated in a cascade system, joint operation process of cascade reservoirs is a complex high-dimensional constrained optimization problem which is hard to get the globally optimal solution. Numerous classical methods, such as linear programming (Lee et al 2008;Kang et al 2017), nonlinear programming (Catalao et al 2010;Jothiprakash and Arunkumar 2014), dynamic programming (DP) (Ji et al 2014;Rani et al 2020;Zhao et al 2017) and DP's variants (Opan 2011;Feng et al 2017;Cheng et al 2012) and intelligent algorithms (Jahandideh-Tehrani et al 2020;Shaikh 2020), have been applied to joint operation of cascade reservoirs. In contrast to other methods, DP and its variants have been used more extensively in engineering practice because they can easily handle non-linear as well as non-differentiable factors and are able to produce satisfactory solutions in most cases.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Joint Operation of Cascade Reservoirs Using Enhanced Progressive Optimality Algorithm and Dynamic Programming Hybrid Approach

Jia¹

2021

Water Resour Manage

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Dynamic programming (DP) is one of the most classical methods adopted for reservoir operation. It reduces the computational efforts of complex high-dimensional problems by piecewise dimensionality reduction and provides the global optimums of the problems, but it suffers the "curse of dimensionality".Progressive optimality algorithm (POA) has been used repeatedly in reservoir operation studies during last decades because it alleviates the "curse of dimensionality" of DP and has good convergence and extensive applicability.Nonetheless, POA encounters two difficulties in multi-reservoir operation applications. One is the transfer interrupt problem that makes the search procedure hard to achieve free allocation of water between two nonadjacent stages, and the latter is the dimensionality problem that leads to a low convergence rate. In order to overcome these deficiencies, this paper makes some enhancements to POA and proposes a hybrid approach combining the enhanced POA and DP (EPOA-DP) for long-term operation of cascade reservoir systems. In EPOA-DP, EPOA is employed 2 to improve the quality of the solutions and DP is used to reduce the computational effort of the two-stage problem solution. The proposed approach was tested using a real world four-reservoir cascade system and a ten-reservoir benchmark test example, and the results demonstrate that it outperforms POA both in computational time and quality of the solution.

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Application of particle swarm optimization to water management: an introduction and overview

Cited by 79 publications

References 110 publications

Groundwater Quality Evaluation Based on PCA-PSO-SVM Machine Learning in Xinzhou City, China

Groundwater Quality Evaluation Based on PCA-PSO-SVM Machine Learning in Xinzhou City, China

An Informative Path Planner for a Swarm of ASVs Based on an Enhanced PSO with Gaussian Surrogate Model Components Intended for Water Monitoring Applications

Long-Term Joint Operation of Cascade Reservoirs Using Enhanced Progressive Optimality Algorithm and Dynamic Programming Hybrid Approach

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