A hybrid approach based on simulation, optimization, and estimation of conjunctive use of surface water and groundwater resources

Azar, Naser Arya; Kayhomayoon, Zahra; Milan, Sami Ghordoyee; Sanayei, HamedReza Zarif; Berndtsson, Ronny; Nematollahi, Zahra

doi:10.1007/s11356-022-19762-2

Cited by 14 publications

(5 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of their model was comparable with the performance of two first-level models of the present study (MSA-ANN and SOS-ANN), but its performance is far from the performance of the second-level model of SOS-MSA-ANN. Furthermore, two hybrid metaheuristics models of FA-GMDH-LS-SVM and WOA-GMDH-LS-SVM proposed by Arya Azar et al 10 with water supply of 87.1% and 84.4%, could not provide significant results in meeting the demands of the studied area. Therefore, among the six implemented methods for the conjunctive use of water resources systems, the SOS-MSA-ANN was the superior model in terms of water supply.…”

Section: Discussionmentioning

confidence: 89%

“…Based on the results, the water level of the aquifer optimally decreased during the study period. Arya Azar et al 10 integrated the whale optimization algorithm and the firefly algorithm with the group method of data handling and least squares support vector machine to optimally allocate surface and groundwater resources in Marvdasht, south of Iran. The results indicated that the groundwater level increased by about 0.4 and 0.55 m using the whale optimization algorithm and firefly algorithm, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An artificial intelligence-based model for optimal conjunctive operation of surface and groundwater resources

Akbarifard,

Madadi,

Zounemat-Kermani

2024

Nat Commun

View full text Add to dashboard Cite

A hybrid simulation-optimization model is proposed for the optimal conjunctive operation of surface and groundwater resources. This second-level model is created by finding and combining the best aspects of two resilient metaheuristics, the moth swarm algorithm and the symbiotic organization search algorithm, and then connecting the resulting algorithm to an artificial neural network simulator. For assessment of the developed model efficiency, its results are compared with two first-level simulation-optimization models. The comparisons reveal that the operation policies obtained by the developed second-level model can reliably supply more than 99% of the total demands in the study regions, indicating its superior efficiency compared to the two other first-level models. In addition, the highest sustainability index in the study regions belongs to the proposed model. Comparing the results of this research with those of other recent studies confirm the supremacy of the developed second-level model over several previously developed models.

show abstract

Section: Discussionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

An artificial intelligence-based model for optimal conjunctive operation of surface and groundwater resources

Akbarifard,

Madadi,

Zounemat-Kermani

2024

Nat Commun

View full text Add to dashboard Cite

show abstract

“…In this method, which is cost efficient and needs less processing time, the simulator model is approximated to create an interface between the simulation and optimization models, and the approximated model is then used for optimization [25]. This approximation can be performed using various methods [14]: artificial neural networks (ANN) [25,26], fuzzy linear regression [27], regression models [14,28], kernel extreme learning machines (KELM) [29], SVM [30], kriging-KELM-SVM [24], response matrix method [1], and genetic programming (GP) and multigene genetic programming (MGGP) [31]. Owing to the efficient performance of the genetic algorithm (GA) in optimization problems, in this current study, an approach is developed which comprises a simulation model and gene expression programming (GEP) for the simulation-optimization process of aquifer exploitation under artificial recharge.…”

Section: Literature Reviewmentioning

confidence: 99%

Determining the Optimal Aquifer Exploitation under Artificial Recharge using the Combination of Numerical Models and Particle Swarm Optimization

et al. 2023

View full text Add to dashboard Cite

Determining optimal exploitation from aquifers is always a major challenge, especially for aquifers facing a drop in their groundwater level. In aquifers with artificial recharge, more complex algorithms are required to determine the optimal exploitation amount. Therefore, in this study, the optimal amount of harvest from the exploitation wells has been determined using a combined simulation–optimization model considering the artificial recharge in Yasouj aquifer in Iran. The model is based on a combination of MODFLOW code and gene expression programming (GEP) simulator tool to simulate the aquifer and particle swarm optimization (PSO) to maximize the total exploitation from the aquifer. The simulation results showed that the artificial recharge was ineffective in maximum exploitation from the aquifer. As a result, considering several constraints, including the maximum pumping rate from the aquifer and the permissible drop in the groundwater level, the maximum exploitation from the aquifer was defined as the objective function. The optimization results showed that the optimal exploitation rate is equal to 8.84 million cubic meters (MCM) per year, and only 74% of the water from artificial recharge can be used based on this amount. Additionally, the most appropriate locations to exploit this amount of water are the northwest and east of the aquifer. According to the findings, it is suggested to ban exploitation from the central and southern parts of the aquifer due to the low groundwater level. The results of the sensitivity analysis show that the reduction in the maximum exploitation rate along with a 50% drop in the groundwater level play an effective role in decreasing the optimal exploitation amount.

show abstract

“…GMDH has been extensively applied in various areas of hydrological studies, such as river flow prediction, management, and soil and sediment (Lin et al 2020;Khodakhah et al 2022;Jaafari et al 2022;Mulashani et al 2022;Nadiri et al 2022). In addition, various studies have been performed on groundwater level prediction exploiting the GMDH approach (Moghaddam et al 2021;Arya Azar et al 2022;Tao et al 2022). LSSVM has been implemented for predicting GWL, and reported that this method improves the accuracy compared to ANN in predicting GWL (Miraki et al 2019;Guzman et al 2019;Khedri et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Qanat Discharge Prediction Using a Comparative Analysis of Machine Learning Methods

Samani,

Vadiati,

Kisi

et al. 2024

Preprint

View full text Add to dashboard Cite

The Qanat (also known as kariz) is one of the significant water resources in many arid and semiarid regions. The present research aims to use machine learning techniques for Qanat discharge (QD) prediction and find a practical model that predicts QD well. Gene expression programming (GEP), artificial neural network (ANN), group method of data handling (GMDH), least-square support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS), are employed to predict one-, two-, and five-months time step ahead QD in an unconfined aquifer. QD for one, two, and three lag-times (QDt−1, QDt−2, QDt−3), QD for adjacent Qanat, the main meteorological components (Tt, ETt, Pt) and GWL for one, two, and three lag-times are utilized as input dataset to accomplish accurate QD prediction. The GMDH model, according to its best results, had promising accuracy in predicting multi-step ahead monthly QD, followed by the LSSVM, ANFIS, ANN and GEP, respectively.

show abstract

A hybrid approach based on simulation, optimization, and estimation of conjunctive use of surface water and groundwater resources

Cited by 14 publications

References 44 publications

An artificial intelligence-based model for optimal conjunctive operation of surface and groundwater resources

An artificial intelligence-based model for optimal conjunctive operation of surface and groundwater resources

Determining the Optimal Aquifer Exploitation under Artificial Recharge using the Combination of Numerical Models and Particle Swarm Optimization

Qanat Discharge Prediction Using a Comparative Analysis of Machine Learning Methods

Contact Info

Product

Resources

About