Estimation of groundwater recharge using simulation-optimization model and cascade forward ANN at East Nile Delta aquifer, Egypt

Abd-Elmaboud, Mahmoud E.; Abdel-Gawad, Hossam A. A.; El-Alfy, Kassem Salah; Ezzeldin, Mohsen

doi:10.1016/j.ejrh.2021.100784

Cited by 16 publications

(6 citation statements)

References 37 publications

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“…The performance of the ANNs method was evaluated using the root mean squared error (RMSE), mean squared error (MSE), and correlation coefficient (R 2 ). These evaluation metrics are widely used to measure the accuracy of ANN models in various studies [39,40]. The errors obtained from the evaluation process were presented as equations, providing a better understanding of the method's accuracy in identifying acceptable recharge regions ( 4)- (6).…”

Section: Resultsmentioning

confidence: 99%

Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin

Zaresefat

Derakhshani

Nikpeyman

et al. 2023

Water

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A water supply is vital for preserving usual human living standards, industrial development, and agricultural growth. Scarce water supplies and unplanned urbanization are the primary impediments to results in dry environments. Locating suitable sites for artificial groundwater recharge (AGR) could be a strategic priority for countries to recharge groundwater. Recent advances in machine learning (ML) techniques provide valuable tools for producing an AGR site suitability map (AGRSSM). This research developed an ML algorithm to identify the most appropriate location for AGR in Iranshahr, one of the major districts in the East of Iran characterized by severe drought and excessive groundwater consumption. The area’s undue reliance on groundwater resources has resulted in aquifer depletion and socioeconomic problems. Nine digitized and georeferenced data layers have been considered for preparing the AGRSSM, including precipitation, slope, geology, unsaturated zone thickness, land use, distance from the main rivers, precipitation, water quality, and transmissivity of soil. The developed AGRSSM was trained and validated using 1000 randomly selected points across the study area with an accuracy of 97%. By comparing the results of the proposed sites with those of other methods, it was discovered that the artificial intelligence method could accurately determine artificial recharge sites. In summary, this study uses a novel approach to identify optimal AGR sites using machine learning algorithms. Our findings have practical implications for policymakers and water resource managers looking to address the problem of groundwater depletion in Iranshahr and other regions facing similar challenges. Future research in this area could explore the applicability of our approach to other regions and examine the potential economic benefits of using AGR to recharge groundwater.

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Section: Resultsmentioning

confidence: 99%

Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin

Zaresefat

Derakhshani

Nikpeyman

et al. 2023

Water

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“…In this case, the signals fed to the ANN input are also fed to the hidden layer of the developed ANN topology to form the outcome 10 . The advantage of such a step is that the proposed approach considers non-linear connections between the inputs and output and the linear component 11 . A similar method was applied in 9 .…”

Section: State-of-the-artsmentioning

confidence: 99%

An interpretable ensemble structure with a non-iterative training algorithm to improve the predictive accuracy of healthcare data analysis

Izonin,

Tkachenko,

Yemets

et al. 2024

Sci Rep

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The modern development of healthcare is characterized by a set of large volumes of tabular data for monitoring and diagnosing the patient's condition. In addition, modern methods of data engineering allow the synthesizing of a large number of features from an image or signals, which are presented in tabular form. The possibility of high-precision and high-speed processing of such large volumes of medical data requires the use of artificial intelligence tools. A linear machine learning model cannot accurately analyze such data, and traditional bagging, boosting, or stacking ensembles typically require significant computing power and time to implement. In this paper, the authors proposed a method for the analysis of large sets of medical data, based on a designed linear ensemble method with a non-iterative learning algorithm. The basic node of the new ensemble is an extended-input SGTM neural-like structure, which provides high-speed data processing at each level of the ensemble. Increasing prediction accuracy is ensured by dividing the large dataset into parts, the analysis of which is carried out in each node of the ensemble structure and taking into account the output signal from the previous level of the ensemble as an additional attribute on the next one. Such a design of a new ensemble structure provides both a significant increase in the prediction accuracy for large sets of medical data analysis and a significant reduction in the duration of the training procedure. Experimental studies on a large medical dataset, as well as a comparison with existing machine learning methods, confirmed the high efficiency of using the developed ensemble structure when solving the prediction task.

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“…Abd Elmabood et al [51] developed an indirect S-O method formed of the unstructured version of MODFLOW and PS to calibrate the numerical model of the Quaternary Aquifer which lies in the east of the Nile Delta, Egypt. The statistical indices reported elsewhere [52]- [54] showed the high accuracy of the model to get the minimum difference between the observed and calculated hydraulic heads.…”

Section: Applications Of S-o To Groundwater Managementmentioning

confidence: 99%

Simulation-Optimization Modeling Techniques for Groundwater Management and Sustainability: A Critical Review

Saqr

Ibrahim

Fujii

et al. 2022

AEF

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Recently, groundwater resources have been subjected to negative impacts from various natural and anthropogenic factors worldwide. Hence, great efforts have been conducted in the literature to find the best management strategies for protection against groundwater quantity shortage and aquifer material contamination. Simulation-optimization (S-O) modeling has been considered one of the most feasible options for groundwater management, providing high accuracy in understanding the complex real-world water issues. This review study aims at clarifying the concepts, techniques, and stepwise methodologies of both simulation models and optimization algorithms, in addition to the applications of the integrated S-O approach in solving groundwater problems. Besides, the linkages between applications of the S-O framework and sustainable development goals (SDGs) from quantity and quality perspectives are illustrated to show the expected beneficial outcomes for various socio-economic and environmental issues in different fields. Based on this review, conclusions are drawn that may be useful for future studies related to groundwater conservation, management, and planning.

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Estimation of groundwater recharge using simulation-optimization model and cascade forward ANN at East Nile Delta aquifer, Egypt

Cited by 16 publications

References 37 publications

Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin

Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin

An interpretable ensemble structure with a non-iterative training algorithm to improve the predictive accuracy of healthcare data analysis

Simulation-Optimization Modeling Techniques for Groundwater Management and Sustainability: A Critical Review

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