A review of the artificial intelligence methods in groundwater level modeling
By Taher Rajaee, H. Ebrahimi, Vahid Nourani. Journal of Hydrology, 2019
373 citations recorded by the source corpus.
Abstract This study is a review to the special issue on artificial intelligence (AI) methods for groundwater level (GWL) modeling and forecasting, and presents a brief overview of the most popular AI techniques, along with the bibliographic reviews of the experiences of the authors over past years, and the reviewing and comparison of the obtained results. Accordingly, 67 journal papers published from 2001 to 2018 were reviewed in the terms of the features and abilities of the modeling approaches, input data consideration, prediction time steps, data division, etc. From the reviewed papers it can be concluded that despite some weaknesses, if the AI methods properly be developed, they can successfully be used to simulate and forecast the GWL time series in different aquifers. Since some of the stages of the AI modeling are based on the experience or trial-and-error procedures, it is useful to review them in the special application on GWL modeling. Many partial and general results were achieved from the reviewed papers, which can provide applicable guidelines for researchers who want to perform similar works in this field. Several new ideas in the related area of research are also presented in this study for developing innovative methods and for improving the quality of the modeling.
Subfields: Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/b79127b48a3bf7ddcf7b5553889f35e4ed9a819e
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In Environmental Engineering
Citations in the corpus
- A Review of the Artificial Neural Network Models for Water Quality Prediction (2020, background): “The increasing popularity of ANNs in the field of water resources [155] and environmental engineering [16] may be explained by the major advantage of the ANNs—that researchers can utilize them to model nonlinear and complex phenomena even if they do not fully understand the underlying mechanisms [15”