A Comparative Study of Artificial Intelligence Models and A Statistical Method for Groundwater Level Prediction
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DOI: 10.1007/s11269-022-03070-y
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- Mehrdad Jeihouni & Ara Toomanian & Ali Mansourian, 2020. "Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management: a Novel Hybrid Use of Data Mining and GIS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 139-154, January.
- Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
- Partha Majumder & T.I. Eldho, 2020. "Artificial Neural Network and Grey Wolf Optimizer Based Surrogate Simulation-Optimization Model for Groundwater Remediation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 763-783, January.
- Ghorban Asgari & Ensieh Komijani & Abdolmotaleb Seid-Mohammadi & Mohammad Khazaei, 2021. "Assessment the Quality of Bottled Drinking Water Through Mamdani Fuzzy Water Quality Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5431-5452, December.
- Mojtaba Kadkhodazadeh & Saeed Farzin, 2021. "A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 3939-3968, September.
- L. Guneshwor & T. I. Eldho & A. Vinod Kumar, 2018. "Identification of Groundwater Contamination Sources Using Meshfree RPCM Simulation and Particle Swarm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1517-1538, March.
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Cited by:
- Bulent Haznedar & Huseyin Cagan Kilinc, 2022. "A Hybrid ANFIS-GA Approach for Estimation of Hydrological Time Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4819-4842, September.
- Yong Huang & Kehan Miao & Xiaoguang Liu & Yin Jiang, 2022. "The Hysteresis Response of Groundwater to Reservoir Water Level Changes in a Plain Reservoir Area," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4739-4763, September.
- Saeideh Samani & Meysam Vadiati & Farahnaz Azizi & Efat Zamani & Ozgur Kisi, 2022. "Groundwater Level Simulation Using Soft Computing Methods with Emphasis on Major Meteorological Components," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3627-3647, August.
- R. Sarma & S. K. Singh, 2022. "A Comparative Study of Data-driven Models for Groundwater Level Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2741-2756, June.
- Stephen Afrifa & Tao Zhang & Peter Appiahene & Vijayakumar Varadarajan, 2022. "Mathematical and Machine Learning Models for Groundwater Level Changes: A Systematic Review and Bibliographic Analysis," Future Internet, MDPI, vol. 14(9), pages 1-31, August.
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Keywords
Extreme Learning Machine; Least square Support Vector Machine; Multiple Linear Regression; Adaptive Neuro-Fuzzy Inference System; MLR; LSSVR; ELM; ANFIS; Water Parameters; Quantity Parameters;All these keywords.
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