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Barzegar R, Fijani E, Asghari Moghaddam A, et al. Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models. Sci Total Environ. 2017;599:20-31doi: 10.1016/j.scitotenv.2017.04.189.
Barzegar, R., Fijani, E., Asghari Moghaddam, A., & Tziritis, E. (2017). Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models. The Science of the total environment, 59920-31. https://doi.org/10.1016/j.scitotenv.2017.04.189
Barzegar, Rahim, et al. "Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models." The Science of the total environment vol. 599 (2017): 20-31. doi: https://doi.org/10.1016/j.scitotenv.2017.04.189
Barzegar R, Fijani E, Asghari Moghaddam A, Tziritis E. Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models. Sci Total Environ. 2017 Dec 01;599:20-31. doi: 10.1016/j.scitotenv.2017.04.189. Epub 2017 Apr 29. PMID: 28463698.
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Sci Total Environ. 2017 Dec 01;599:20-31. doi: 10.1016/j.scitotenv.2017.04.189. Epub 2017 Apr 29.

Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models.

The Science of the total environment

Rahim Barzegar, Elham Fijani, Asghar Asghari Moghaddam, Evangelos Tziritis

Affiliations

  1. Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran. Electronic address: [email protected].
  2. School of Geology, College of Science, University of Tehran, Tehran, Iran.
  3. Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran.
  4. Hellenic Agricultural Organization, Soil and Water Resources Institute, 57400 Sindos, Greece.

PMID: 28463698 DOI: 10.1016/j.scitotenv.2017.04.189

Abstract

Accurate prediction of groundwater level (GWL) fluctuations can play an important role in water resources management. The aims of the research are to evaluate the performance of different hybrid wavelet-group method of data handling (WA-GMDH) and wavelet-extreme learning machine (WA-ELM) models and to combine different wavelet based models for forecasting the GWL for one, two and three months step-ahead in the Maragheh-Bonab plain, NW Iran, as a case study. The research used totally 367 monthly GWLs (m) datasets (Sep 1985-Mar 2016) which were split into two subsets; the first 312 datasets (85% of total) were used for model development (training) and the remaining 55 ones (15% of total) for model evaluation (testing). The stepwise selection was used to select appropriate lag times as the inputs of the proposed models. The performance criteria such as coefficient of determination (R

Copyright © 2017 Elsevier B.V. All rights reserved.

Keywords: ELM; Forecast; GMDH; Groundwater level; Iran; MODWT

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