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Article

Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment

1
Engineering Projects Area, Department of Rural Engineering, University of Córdoba, 14071 Córdoba, Spain
2
School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK
*
Author to whom correspondence should be addressed.
Water 2020, 12(7), 1909; https://doi.org/10.3390/w12071909
Received: 29 May 2020 / Revised: 27 June 2020 / Accepted: 2 July 2020 / Published: 4 July 2020
(This article belongs to the Section Water, Agriculture and Aquaculture)
Accurate forecast of hydrological data such as precipitation is critical in order to provide useful information for water resources management, playing a key role in different sectors. Traditional forecasting methods present many limitations due to the high-stochastic property of precipitation and its strong variability in time and space: not identifying non-linear dynamics or not solving the instability of local weather situations. In this work, several alternative models based on the combination of wavelet analysis (multiscalar decomposition) with artificial neural networks have been developed and evaluated at sixteen locations in Southern Spain (semiarid region of Andalusia), representative of different climatic and geographical conditions. Based on the capability of wavelets to describe non-linear signals, ten wavelet neural network models (WNN) have been applied to predict monthly precipitation by using short-term thermo-pluviometric time series. Overall, the forecasting results show differences between the ten models, although an effective performance (i.e., correlation coefficients ranged from 0.76 to 0.90 and Root Mean Square Error values ranged from 6.79 to 29.82 mm) was obtained at each of the locations assessed. The most appropriate input variables to obtain the best forecasts are analyzed, according to the geo-climatic characteristics of the sixteen sites studied. View Full-Text
Keywords: precipitation; forecasting; wavelet; neural networks models precipitation; forecasting; wavelet; neural networks models
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MDPI and ACS Style

Estévez, J.; Bellido-Jiménez, J.A.; Liu, X.; García-Marín, A.P. Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment. Water 2020, 12, 1909. https://doi.org/10.3390/w12071909

AMA Style

Estévez J, Bellido-Jiménez JA, Liu X, García-Marín AP. Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment. Water. 2020; 12(7):1909. https://doi.org/10.3390/w12071909

Chicago/Turabian Style

Estévez, Javier, Juan A. Bellido-Jiménez, Xiaodong Liu, and Amanda P. García-Marín 2020. "Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment" Water 12, no. 7: 1909. https://doi.org/10.3390/w12071909

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