The performance of the wavelet Halt-Winters hybrid model (WHW) in groundwater level forecasting of Urmieh coastal aquifer

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

MIRARABI A.; NASSERY H.R.; NAKHAEI M.; ALIJANI F.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Iranian Journal of Geology Year:2019 | Volume:13 | Issue:49 Page(s): 37-48

Download Full-Text

Persian Verion

View:

763

Download:

Cites:

Information Journal Paper

Title

The performance of the wavelet Halt-Winters hybrid model (WHW) in groundwater level forecasting of Urmieh coastal aquifer

Author(s)

MIRARABI A. | NASSERY H.R. | NAKHAEI M. | ALIJANI F. | Issue Writer Certificate

Keywords

Halt-WintersQ1

Wavelet transformQ2

Neural networkQ2

Support vector regressionQ1

GroundwaterQ2

Abstract

For management and planning valuable Groundwater resources, it is very important to predict Groundwater level and have a correct understanding about aquifer changes. In this paper for the first time, the wavelet Halt-Winters hybrid models (WHW) were used and tested for Groundwater forecasting. A monthly data set of 16 years consisting of Groundwater level fluctuations was used in two observation wells of Urmieh coastal aquifer. In the WHW, the dataset was converted into several sub-dataset with different time scales. Then, the sub-series were used in the HW model as inputs. Subsequently, the performance of the WHW model was compared with ARIMA, HW, and SARIMA as linear models and Neural network models (ANN) and Support vector regression (SVR) as nonlinear models. The results showed that the NSE and RMSE values of the WHW model were upgraded up to 30% and 60% respectively, in comparison with linear models. The WHW hybrid model also has the same performance compared to nonlinear models. This research reflects that if there are multiple seasonal fluctuations in the Groundwater time series, the performance of the WHW model compared with linear models will be more accurate.

Cites

No record.

References

No record.

Cite

APA: Copy

MIRARABI, A., NASSERY, H.R., NAKHAEI, M., & ALIJANI, F.. (2019). The performance of the wavelet Halt-Winters hybrid model (WHW) in groundwater level forecasting of Urmieh coastal aquifer. IRANIAN JOURNAL OF GEOLOGY, 13(49 ), 37-48. SID. https://sid.ir/paper/129455/en

Vancouver: Copy

MIRARABI A., NASSERY H.R., NAKHAEI M., ALIJANI F.. The performance of the wavelet Halt-Winters hybrid model (WHW) in groundwater level forecasting of Urmieh coastal aquifer. IRANIAN JOURNAL OF GEOLOGY[Internet]. 2019;13(49 ):37-48. Available from: https://sid.ir/paper/129455/en

IEEE: Copy

A. MIRARABI, H.R. NASSERY, M. NAKHAEI, and F. ALIJANI, “The performance of the wavelet Halt-Winters hybrid model (WHW) in groundwater level forecasting of Urmieh coastal aquifer,” IRANIAN JOURNAL OF GEOLOGY, vol. 13, no. 49 , pp. 37–48, 2019, [Online]. Available: https://sid.ir/paper/129455/en

Related Journal Papers