Application of artificial intelligence models for prediction of groundwater level fluctuations: case study (Tehran-Karaj alluvial aquifer)

• Published in: Environmental monitoring and assessment Vol. 194; no. 9; p. 619
• Main Authors: Vadiati, Meysam, Rajabi Yami, Zahra, Eskandari, Effat, Nakhaei, Mohammad, Kisi, Ozgur
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.09.2022; Springer Nature B.V
• Subjects: Adaptive systems; alluvial aquifer; Alluvial aquifers; Aquifer models; Aquifers; Artificial intelligence; Artificial neural networks; Atmospheric Protection/Air Quality Control/Air Pollution; Average flow; case studies; Correlation coefficient; Correlation coefficients; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental monitoring; Environmental science; Evapotranspiration; Fluctuations; Fuzzy logic; Geology; Groundwater; groundwater extraction; Groundwater levels; Groundwater table; Hydrogeology; Lead time; Mathematical models; Methods; Modelling; Momentum; Monitoring/Environmental Analysis; Neural networks; Numerical models; Precipitation; prediction; Predictions; Renewable resources; Resource management; River flow; Root-mean-square errors; Support vector machines; temperature; uncertainty; water management; Water resources; Water resources management; Water table; Water table fluctuations; Fuzzy logic; Groundwater; Artificial intelligence; Data driven-based methods

The nonlinear groundwater level fluctuations depend on the interaction of many factors such as evapotranspiration, precipitation, groundwater abstraction, and hydrogeological characteristics, making groundwater level prediction a complex task. Groundwater level changes are among the most critical is...