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

Est�vez, Javier; Bellido-Jim�nez, Juan Antonio; Liu, Xiaodong; Garc�a-Mar�n, Amanda Pen�lope

Abstract

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.

Citation

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

• Journal Article Type: Article
• Acceptance Date: Jul 2, 2020
• Online Publication Date: Jul 4, 2020
• Publication Date: Jul 4, 2020
• Deposit Date: Aug 28, 2020
• Publicly Available Date: Aug 28, 2020
• Journal: Water
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 7
• Article Number: 1909
• DOI: https://doi.org/10.3390/w12071909
• Keywords: precipitation; forecasting; wavelet; neural networks models
• Public URL: http://researchrepository.napier.ac.uk/Output/2683533