Modelling groundwater level variations by learning from multiple models using fuzzy logic-Reference-Cited by-同舟云学术

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Modelling groundwater level variations by learning from multiple models using fuzzy logic

Published:2019-01-25 Issue:2 Volume:64 Page:210-226
ISSN:0262-6667
Container-title:Hydrological Sciences Journal
language:en
Short-container-title:Hydrological Sciences Journal

Author:

Nadiri Ata Allah¹²,Naderi Keyvan³,Khatibi Rahman⁴,Gharekhani Maryam¹

Affiliation:

1. Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran

2. Institute of Environment, University of Tabriz, East Azerbaijan, Iran

3. Formerly of Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran

4. GTEV-ReX Limited, Swindon, UK

Funder

University of Tabriz

Publisher

Informa UK Limited

Subject

Water Science and Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/02626667.2018.1554940

Reference45 articles.

1. Water level forecasting through fuzzy logic and artificial neural network approaches

2. Artificial intelligence modeling to evaluate field performance of photocatalytic asphalt pavement for ambient air purification

3. Optimization of DRASTIC method by artificial neural network, nitrate vulnerability index, and composite DRASTIC models to assess groundwater vulnerability for unconfined aquifer of Shiraz Plain, Iran

4. A committee machine with empirical formulas for permeability prediction

5. Fuzzy clustering analysis for optimizing fuzzy membership functions

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