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Environmental Earth Sciences

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01.08.2017 | Original Article

Combined gamma and M-test-based ANN and ARIMA models for groundwater fluctuation forecasting in semiarid regions

verfasst von: Bahram Choubin, Arash Malekian

Erschienen in: Environmental Earth Sciences | Ausgabe 15/2017

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Abstract

The shortage of surface water in arid and semiarid regions has led to the more use of the groundwater resources. In these areas, the groundwater is essential for activities such as water supply and irrigation. One of the most important stages in sustainable yield of groundwater resources is awareness of groundwater level. In this study, we have applied artificial neural networks (ANN) and autoregressive integrated moving average (ARIMA) models for groundwater level forecasting to 4 months ahead in Shiraz basin, southwestern Iran. Time series analysis was conducted according to the Box–Jenkins method. Meanwhile, gamma and M-test were considered for determining the optimal input combination and length of training and testing data in the ANN model. The results indicated that performance of multilayer perceptron neural network (4, 14, 1) and ARIMA (2, 1, 2) is satisfactory in the groundwater level forecasting for one month ahead. The performance comparison shows that the ARIMA model performs appreciably better than the ANN.

Vorheriger Artikel Validation of TRMM 3B42 V6 for estimation of mean annual rainfall over ungauged area in semiarid climate

Nächster Artikel Temporal stability of soil water storage and its influencing factors on a forestland hillslope during the rainy season in China’s Loess Plateau

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Titel: Combined gamma and M-test-based ANN and ARIMA models for groundwater fluctuation forecasting in semiarid regions
verfasst von: Bahram Choubin
Arash Malekian
Publikationsdatum: 01.08.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Environmental Earth Sciences / Ausgabe 15/2017
Print ISSN: 1866-6280
Elektronische ISSN: 1866-6299
DOI: https://doi.org/10.1007/s12665-017-6870-8

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