Subject Areas :
Mohammad Nakhaei 1 , Farshad Alijani 2 , Ali Mirarabi 3 , HamidReza Nasseri 4
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Keywords: Halt-Winters Wavelet transform Neural network Support vector regression Groundwater.,
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.
شرکت آب منطقهای آذربایجان غربی، 1393. گزارش توجیهی ممنوعیت دشت ارومیه، دفتر مطالعات پایه.
- نورانی، و.، شرقی، الف. و نجفی، ح.، 1397. مدلسازی پدیدههای هیدروکلیماتولوژیکی با استفاده از مدل ترکیبی موجک-هالت وینترز، 14، شماره 1، 59-70.
-Box, G.E., Jenkins, G.M., Reinsel, G.C. and Ljung, G.M., 2015. Time series analysis: Forecasting and control (5th ED). John Wiley and Sons, 680.
-Dąbrowska, D., Sołtysiak, M. and Waligóra, J., 2015. Short term forecasting of the chloride content in the mineral waters of the Ustroń Health Resort using SARIMA and Holt-Winters models. Environmental and Socio-economic Studies, 3, 57-65.
-Kecman, V., 2005. Support Vector Machines: An Introduction, Theory and Applications. Springer-Verlag, New York, 4, 1-48.
-Mallat, S.G., 1998. A wavelet tour of signal processing. Second ed. Academic Press. SanDiego.
-Moosavi, V., Vafakhah, M., Shirmohammadi B. and Behnia, N., 2013. Awavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods. Water Recourses Management, 27, 1301–1321.
-Nakhaei, M. and Saberi, A., 2012. A combined Wavelet- Artificial Neural Network model and its application to the prediction of groundwater level fluctuations. Journal Geopersia, 2, 77-91.
-Sang, Y.F., 2012. A practical guide to discrete wavelet decomposition of hydrologic time series. Water Recourses Management, 26, 3345–3365.
-Sudheer, G. and Suseelatha, A., 2015. Short term load forecasting using wavelet transform combined with Holt–Winters and weighted nearest neighbor models. International Journal of Electrical Power and Energy Systems, 64, 340-346.
-Winters, P.R., 1960. Forecasting sales by exponentially weighted moving averages. Management Science, 6, 324-342.
-Yang, Q., Wang, Y., Zhang, J. and Delgado, J., 2017. A comparative study of shallow groundwater level simulation with three time series models in a coastal aquifer of south china. Applied Water Science, 7, 689-698.