E
Elham Fijani
Researcher at University of Tehran
Publications - 22
Citations - 953
Elham Fijani is an academic researcher from University of Tehran. The author has contributed to research in topics: Aquifer & Groundwater. The author has an hindex of 11, co-authored 20 publications receiving 678 citations. Previous affiliations of Elham Fijani include University of Tabriz & Louisiana State University.
Papers
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Journal ArticleDOI
Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models.
TL;DR: The wavelet based models improved the performances of GMDH and ELM models for multi-step-ahead GWL forecasting and provided the best performances for GWL forecasts in comparison with single WA-neural network-based models.
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Optimization of DRASTIC method by supervised committee machine artificial intelligence to assess groundwater vulnerability for Maragheh–Bonab plain aquifer, Iran
Elham Fijani,Elham Fijani,Ata Allah Nadiri,Ata Allah Nadiri,Asghar Asghari Moghaddam,Frank T.-C. Tsai,Barnali M. Dixon +6 more
TL;DR: The SCMAI model is an effective model to improve the DRASTIC method for groundwater vulnerability assessment for the Maragheh–Bonab plain aquifer in Iran and provides a confident estimate of the pollution risk.
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Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters.
Elham Fijani,Rahim Barzegar,Rahim Barzegar,Ravinesh C. Deo,Evangelos Tziritis,Konstantinos Skordas +5 more
TL;DR: The developed methodology demonstrates the robustness of the two-phase VMD-CEEMDAN-ELM model in identifying and analyzing critical water quality parameters with a limited set of model construction data over daily horizons, and thus, to actively support environmental monitoring tasks, especially in case of high-frequency, and relatively complex, real-time datasets.
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Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms.
TL;DR: The newly designed multi-model ensemble-based approach can be considered as a pragmatic step for mapping groundwater contamination risks of multiple aquifer systems with multi- model techniques, yielding the high accuracy of the ANN committee-based model.
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Distribution of fluoride in groundwater of Maku area, northwest of Iran
TL;DR: In this article, the authors investigated the possible causes of high fluoride concentrations in groundwater in Maku area, in the north of West Azarbaijan province, northwest of Iran.