Modeling of groundwater level using artificial intelligence techniques: a case study of Reyhanli region in Turkey
Open accessAbstract
Determination of the change in groundwater level in terms of planning and managing resources is important.In this study, the groundwater level of Reyhanlı region in Turkey was predicted using multi-linear regression (MLR), adaptive neural fuzzy inference system (ANFIS), Radial basis neural network (RBNN), support vector machines with radial basis functions (SVM-RBF) and support vector machines with poly kernels (SVM-PK) methods.Models were carried out using 192 data of monthly ground water level, monthly total precipitation and monthly average temperature values measured for 16 years between 2000 and 2015.Comparisons revealed that the SVM-RBF and SVM-PK models had the most accuracy in the groundwater level prediction.
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ML techniques such as ANN, SVM, FL, ANFIS, LR, and KNN have been applied to predict stream flow and examine water quality parameters.
“ML techniques, such as ANN, SVM, FL, ANFIS (an ANN-based on FL), LR, and Key-Nearest Neighbors (KNN), have been applied to predict stream flow and examine water quality parameters (e.g., Ref. [161,162]).”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [161]
ML models are used to predict stream flow and examine water quality parameters.
“ML models are used to predict stream flow and examine water quality parameters (Demirci et al., 2019).”
From Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda (2020) · cites this paper as Demirci et al., 2019
In energy research, ANN, expert systems, pattern recognition, and fuzzy logic models are the main focus areas.
“In energy research, ANN, expert systems, pattern recognition, and fuzzy logic models are the main focus areas (Demirci et al., 2019; Tyralis et al., 2019).”
From Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda (2020) · cites this paper as Demirci et al., 2019
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Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda
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- Authors
- DEMIRCI, M., ÜNEŞ, F., & KÖRLÜ, S.
- Affiliations
- İskenderun Technical University (TR)
- ORCID
- Venue
- Applied Ecology and Environmental Research
- Publisher
- ALOKI Ltd
- Published
- 2019
- Volume
- 17
- Issue
- 2
- Pages
- 2651-2663
- ISSN
- 1589-1623 (ISSN-L)
- 1785-0037
- OpenAlex
- W2922089711
- Cited by
- 39
- References
- 45
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 2 · 2023: 8 · 2022: 5 · 2021: 3 · 2020: 2 · 2019: 2
- FWCI
- 1.27
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Applied Ecology and Environmental Research (publishedVersion, cc-by)
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Physical Sciences › Earth and Planetary Sciences › Geochemistry and Petrology › Groundwater and Isotope Geochemistry
- Physical Sciences › Engineering › Ocean Engineering › Water resources management and optimization
- Keywords
- groundwater level prediction, artificial intelligence, adaptive neuro-fuzzy inference system, radial basis function neural network, support vector machine, multi-linear regression, precipitation, air temperature, groundwater management
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