Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/69663
Title: Comparative analysis of rainfall prediction models using machine learning in islands with complex orography: Tenerife island
Authors: Aguasca-Colomo, Ricardo 
Castellanos-Nieves, Dagoberto
Méndez, Máximo 
UNESCO Clasification: 250909 Predicción numérica meteorológica
120304 Inteligencia artificial
Keywords: Classification algorithms
Data processing
Machine learning
Computational methods
Predictive models, et al
Issue Date: 2019
Journal: Applied Sciences (Basel) 
Abstract: We present a comparative study between predictive monthly rainfall models for islands of complex orography using machine learning techniques. The models have been developed for the island of Tenerife (Canary Islands). Weather forecasting is influenced both by the local geographic characteristics as well as by the time horizon comprised. Accuracy of mid-term rainfall prediction on islands with complex orography is generally low when carried out with atmospheric models. Predictive models based on algorithms such as Random Forest or Extreme Gradient Boosting among others were analyzed. The predictors used in the models include weather predictors measured in two main meteorological stations, reanalysis predictors from the National Oceanic and Atmospheric Administration, and the global predictor North Atlantic Oscillation, all of them obtained over a period of time of more than four decades. When comparing the proposed models, we evaluated accuracy, kappa and interpretability of the model obtained, as well as the relevance of the predictors used. The results show that global predictors such as the North Atlantic Oscillation Index (NAO) have a very low influence, while the local Geopotential Height (GPH) predictor is relatively more important. Machine learning prediction models are a relevant proposition for predicting medium-term precipitation in similar geographical regions.
URI: http://hdl.handle.net/10553/69663
ISSN: 2076-3417
DOI: 10.3390/app9224931
Source: Applied Sciences (Basel) [ISSN 2076-3417], v. 9 (22), 4931
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