Abstract
The study investigates accuracy of two machine learning methods, neuro-fuzzy system with grid partition (ANFIS-GP) and multivariate adaptive regression spline (MARS) in prediction of 1-day- to 6-day-ahead groundwater levels (GWLs) using data from two wells, USA. The outcomes indicate that the ANFIS-GP provides inferior results compared to regression-based simple MARS method. The MARS method which is much simpler than the ANFIS-GP is recommended for short-term GWL prediction.
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Kisi, O., Sanikhani, H. (2021). Modeling Short-Term Groundwater-Level Fluctuations Using Multivariate Adaptive Regression Spline. In: Abrunhosa, M., Chambel, A., Peppoloni, S., Chaminé, H.I. (eds) Advances in Geoethics and Groundwater Management : Theory and Practice for a Sustainable Development. Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-59320-9_41
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DOI: https://doi.org/10.1007/978-3-030-59320-9_41
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