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Bee-inspired insights : Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction

Katipoğlu, Okan Mert ; Mohammadi, Babak LU orcid and Keblouti, Mehdi (2024) In Environmental Monitoring and Assessment 196(8).
Abstract

Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Therefore, it is necessary to model groundwater level (GWL) fluctuations to monitor and predict groundwater storage. Artificial intelligence-based models in water resource management have become prevalent due to their proven success in hydrological studies. This study proposed a hybrid model that combines the artificial neural network (ANN) and the artificial bee colony optimization (ABC) algorithm, along with the ensemble empirical mode decomposition (EEMD) and the local mean decomposition (LMD)... (More)

Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Therefore, it is necessary to model groundwater level (GWL) fluctuations to monitor and predict groundwater storage. Artificial intelligence-based models in water resource management have become prevalent due to their proven success in hydrological studies. This study proposed a hybrid model that combines the artificial neural network (ANN) and the artificial bee colony optimization (ABC) algorithm, along with the ensemble empirical mode decomposition (EEMD) and the local mean decomposition (LMD) techniques, to model groundwater levels in Erzurum province, Türkiye. GWL estimation results were evaluated with mean square error (MSE), coefficient of determination (R2), and residual sum of squares (RSS) and visually with violin, scatter, and time series plot. The study results indicated that the EEMD-ABC-ANN hybrid model was superior to other models in estimating GWL, with R2 values ranging from 0.91 to 0.99 and MSE values ranging from 0.004 to 0.07. It has also been revealed that promising GWL predictions can be made with previous GWL data.

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author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Artificial bee colony, Artificial neural network, Data pre-processing, Groundwater level, Hydroinformatics, Optimization
in
Environmental Monitoring and Assessment
volume
196
issue
8
article number
724
publisher
Springer
external identifiers
  • pmid:38990407
  • scopus:85198138371
ISSN
0167-6369
DOI
10.1007/s10661-024-12838-1
language
English
LU publication?
yes
id
f9142118-d233-43c4-b42c-708ce47d4677
date added to LUP
2024-09-24 14:33:52
date last changed
2024-10-08 18:27:24
@article{f9142118-d233-43c4-b42c-708ce47d4677,
  abstract     = {{<p>Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Therefore, it is necessary to model groundwater level (GWL) fluctuations to monitor and predict groundwater storage. Artificial intelligence-based models in water resource management have become prevalent due to their proven success in hydrological studies. This study proposed a hybrid model that combines the artificial neural network (ANN) and the artificial bee colony optimization (ABC) algorithm, along with the ensemble empirical mode decomposition (EEMD) and the local mean decomposition (LMD) techniques, to model groundwater levels in Erzurum province, Türkiye. GWL estimation results were evaluated with mean square error (MSE), coefficient of determination (R<sup>2</sup>), and residual sum of squares (RSS) and visually with violin, scatter, and time series plot. The study results indicated that the EEMD-ABC-ANN hybrid model was superior to other models in estimating GWL, with R<sup>2</sup> values ranging from 0.91 to 0.99 and MSE values ranging from 0.004 to 0.07. It has also been revealed that promising GWL predictions can be made with previous GWL data.</p>}},
  author       = {{Katipoğlu, Okan Mert and Mohammadi, Babak and Keblouti, Mehdi}},
  issn         = {{0167-6369}},
  keywords     = {{Artificial bee colony; Artificial neural network; Data pre-processing; Groundwater level; Hydroinformatics; Optimization}},
  language     = {{eng}},
  number       = {{8}},
  publisher    = {{Springer}},
  series       = {{Environmental Monitoring and Assessment}},
  title        = {{Bee-inspired insights : Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction}},
  url          = {{http://dx.doi.org/10.1007/s10661-024-12838-1}},
  doi          = {{10.1007/s10661-024-12838-1}},
  volume       = {{196}},
  year         = {{2024}},
}