Bee-inspired insights : Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction
(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
- Katipoğlu, Okan Mert ; Mohammadi, Babak LU and Keblouti, Mehdi
- organization
- publishing date
- 2024-08
- 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}}, }