Predicting Groundwater Levels in Ogallala Aquifer Wells Using Hierarchical Cluster Analysis and Artificial Neural Networks
Publication: Journal of Hydrologic Engineering
Volume 28, Issue 3
Abstract
The Ogallala Aquifer, located in the Central Plains of the United States, is essential for agricultural irrigation and public water supply. Indiscriminate pumping from the aquifer has caused several negative impacts, such as deterioration of water quality and depletion of groundwater levels, which urgently demand better management. This paper applies hierarchical cluster analysis (HCA) and artificial neural networks (ANNs) for predicting annual groundwater levels in 403 wells of the Ogallala Aquifer. First, the methodology employed HCA to cluster homogeneous wells based on the time series of groundwater levels. Then, the study calibrated an ANN model for each cluster (composed of one or more wells) using previous annual values of groundwater levels as input. The HCA results showed a particular pattern in the spatial distribution of the 30 found clusters, revealing that the Ogallala Aquifer holds higher groundwater levels in the western part, which gradually decrease, advancing to the east. The ANN models provided proper predictions even for wells outside of the calibration data set. This investigation concludes that the integration of HCA and ANN enabled single models to accurately forecast annual groundwater levels for sets of wells in the Ogallala Aquifer.
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Data Availability Statement
The data that support the findings of this study are available from the corresponding author, CAGS, upon reasonable request.
Acknowledgments
This study was also financed in part by the Brazilian Agency for the Improvement of Higher Education (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–CAPES)–Fund Code 001, the National Council for Scientific and Technological Development, Brazil–CNPq (Grant Nos. 313358/2021-4, 309330/2021-1, and 420031/2021-9), and the Federal University of Paraíba.
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Journal of Hydrologic Engineering
Volume 28 • Issue 3 • March 2023
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© 2022 American Society of Civil Engineers.
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Received: May 31, 2022
Accepted: Oct 14, 2022
Published online: Dec 21, 2022
Published in print: Mar 1, 2023
Discussion open until: May 21, 2023
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