Enhancing groundwater level prediction accuracy using interpolation techniques in deep learning models

Abdi, Erfan; Ali, Mumtaz; Santos, Celso Augusto Guimarães; Olusola, Adeyemi; Ghorbani, Mohammad Ali

doi:10.1016/j.gsd.2024.101213

Groundwater for Sustainable Development

2024

DOI: 10.1016/j.gsd.2024.101213

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Enhancing groundwater level prediction accuracy using interpolation techniques in deep learning models

Erfan Abdi,

Mumtaz Ali,

Celso Augusto Guimarães Santos

et al.

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2024

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Cited by 4 publications

References 63 publications

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Integrating an interpolation technique and AI models using Bayesian model averaging to enhance groundwater level monitoring

Wang,

Wang

2024

Earth Sci Inform

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Integrating an interpolation technique and AI models using Bayesian model averaging to enhance groundwater level monitoring

Wang,

Wang

2024

Earth Sci Inform

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Enhancing groundwater potential zone mapping with a hybrid analytical method: The case of semiarid basin

Zerouali,

Bailek,

Islam

et al. 2024

Groundwater for Sustainable Development

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Balancing Results from AI-Based Geostatistics versus Fuzzy Inference by Game Theory Bargaining to Improve a Groundwater Monitoring Network

Hashemi,

Peralta,

Yost

2024

MAKE

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An artificial intelligence-based geostatistical optimization algorithm was developed to upgrade a test Iranian aquifer’s existing groundwater monitoring network. For that aquifer, a preliminary study revealed that a Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) more accurately determined temporally average water table elevations than geostatistical kriging, spline, and inverse distance weighting. Because kriging is usually used in that area for water table estimation, the developed algorithm used MLP-ANN to guide kriging, and Genetic Algorithm (GA) to determine locations for new monitoring well location(s). For possible annual fiscal budgets allowing 1–12 new wells, 12 sets of optimal new well locations are reported. Each set has the locations of new wells that would minimize the squared difference between the time-averaged heads developed by kriging versus MLP-ANN. Also, to simultaneously consider local expertise, the algorithm used fuzzy inference to quantify an expert’s satisfaction with the number of new wells. Then, the algorithm used symmetric bargaining (Nash, Kalai–Smorodinsky, and area monotonic) to present an upgradation strategy that balanced professional judgment and heuristic optimization. In essence, the algorithm demonstrates the systematic application of relatively new computational practices to a common situation worldwide.

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Enhancing groundwater level prediction accuracy using interpolation techniques in deep learning models

Cited by 4 publications

References 63 publications

Integrating an interpolation technique and AI models using Bayesian model averaging to enhance groundwater level monitoring

Integrating an interpolation technique and AI models using Bayesian model averaging to enhance groundwater level monitoring

Enhancing groundwater potential zone mapping with a hybrid analytical method: The case of semiarid basin

Balancing Results from AI-Based Geostatistics versus Fuzzy Inference by Game Theory Bargaining to Improve a Groundwater Monitoring Network

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