Integration of group method of data handling (GMDH) algorithm and population-based metaheuristic algorithms for spatial prediction of potential groundwater

Author:

Amiri-Doumari Sahar,Karimipour Ahmadreza,Nayebpour Seyed Nader,Hatamiafkoueieh Javad

Publisher

Springer Science and Business Media LLC

Subject

Earth-Surface Processes,Geology,Pollution,Soil Science,Water Science and Technology,Environmental Chemistry,Global and Planetary Change

Reference80 articles.

1. Abdel-Aal RE, Elhadidy MA, Shaahid S (2009) Modeling and forecasting the mean hourly wind speed time series using GMDH-based abductive networks. Renew Energy 34:1686–1699

2. Al-Fugara AK, Ahmadlou M, Al-Shabeeb AR, AlAyyash S, Al-Amoush H, Al-Adamat R (2020a) Spatial mapping of groundwater springs potentiality using grid search-based and genetic algorithm-based support vector regression. Geocarto Int 37:1–20

3. Al-Fugara AK, Ahmadlou M, Shatnawi R, AlAyyash S, Al-Adamat R, Al-Shabeeb AA-R, Soni S (2020b) Novel hybrid models combining meta-heuristic algorithms with support vector regression (SVR) for groundwater potential mapping. Geocarto Int 37:1–20

4. Arabameri A, Pal SC, Rezaie F, Nalivan OA, Chowdhuri I, Saha A, Lee S, Moayedi H (2021) Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques. J Hydrol 36:100848

5. Çadraku HS (2021) Groundwater quality assessment for irrigation: case study in the Blinaja river basin Kosovo. Civil Eng J 7(9):1515–1528

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