Document Type

Article

Journal/Book Title/Conference

Machine Learning & Knowledge Extraction

Author ORCID Identifier

Masoumeh Hashemi https://orcid.org/0000-0001-8649-5208

Volume

6

Issue

3

Publisher

MDPI AG

Publication Date

8-9-2024

Journal Article Version

Version of Record

First Page

1871

Last Page

1893

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Abstract

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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