Abstract
In this study, a stochastic weather generator and a neuro-fuzzy network were developed to generate precipitation and air temperature (max-mean-min) on a daily basis for meteorological stations of the coastal area of the Basilicata region (Southern Italy). Several simulations were carried out to build an optimal model, whose efficiency was evaluated with the calculation of the root mean square error (RMSE) and the mean absolute error (MAE), which were both obtained comparing simulated and observed values. Subsequently, the developed neuro-fuzzy model was applied to generate other weather variables, such as relative humidity, solar radiation and wind velocity. The simulations showed the good performance of the neuro-fuzzy network in the data filling of the available time series. The evaluation of the performance was made by comparing the values of RMSE and MAE obtained with the neuro-fuzzy method developed in this study and literature methods such as multiple linear regressions and trend line.
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Acknowledgements
The study was carried out in the framework of the project Smart Basilicata in Smart Cities and Communities and Social Innovation” (MIUR n.84/Ric 2012, PON 2007–2013).
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Telesca, V., Caniani, D., Calace, S., Marotta, L., Mancini, I.M. (2017). Daily Temperature and Precipitation Prediction Using Neuro-Fuzzy Networks and Weather Generators. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10409. Springer, Cham. https://doi.org/10.1007/978-3-319-62407-5_31
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DOI: https://doi.org/10.1007/978-3-319-62407-5_31
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