Comparative Analysis of Rainfall Prediction Models Using Machine Learning in Islands with Complex Orography: Tenerife Island
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...
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ftmdpi:oai:mdpi.com:/2076-3417/9/22/4931/ 2023-08-20T04:08:17+02:00 Comparative Analysis of Rainfall Prediction Models Using Machine Learning in Islands with Complex Orography: Tenerife Island Ricardo Aguasca-Colomo Dagoberto Castellanos-Nieves Máximo Méndez agris 2019-11-16 application/pdf https://doi.org/10.3390/app9224931 EN eng Multidisciplinary Digital Publishing Institute Environmental Sciences https://dx.doi.org/10.3390/app9224931 https://creativecommons.org/licenses/by/4.0/ Applied Sciences; Volume 9; Issue 22; Pages: 4931 classification algorithms data processing machine learning computational methods predictive models rainfall forecasting extreme gradient boosting (XGBoost) random forest (rf) Text 2019 ftmdpi https://doi.org/10.3390/app9224931 2023-07-31T22:48:08Z 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. Text North Atlantic North Atlantic oscillation MDPI Open Access Publishing Applied Sciences 9 22 4931 |
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MDPI Open Access Publishing |
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English |
topic |
classification algorithms data processing machine learning computational methods predictive models rainfall forecasting extreme gradient boosting (XGBoost) random forest (rf) |
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classification algorithms data processing machine learning computational methods predictive models rainfall forecasting extreme gradient boosting (XGBoost) random forest (rf) Ricardo Aguasca-Colomo Dagoberto Castellanos-Nieves Máximo Méndez Comparative Analysis of Rainfall Prediction Models Using Machine Learning in Islands with Complex Orography: Tenerife Island |
topic_facet |
classification algorithms data processing machine learning computational methods predictive models rainfall forecasting extreme gradient boosting (XGBoost) random forest (rf) |
description |
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. |
format |
Text |
author |
Ricardo Aguasca-Colomo Dagoberto Castellanos-Nieves Máximo Méndez |
author_facet |
Ricardo Aguasca-Colomo Dagoberto Castellanos-Nieves Máximo Méndez |
author_sort |
Ricardo Aguasca-Colomo |
title |
Comparative Analysis of Rainfall Prediction Models Using Machine Learning in Islands with Complex Orography: Tenerife Island |
title_short |
Comparative Analysis of Rainfall Prediction Models Using Machine Learning in Islands with Complex Orography: Tenerife Island |
title_full |
Comparative Analysis of Rainfall Prediction Models Using Machine Learning in Islands with Complex Orography: Tenerife Island |
title_fullStr |
Comparative Analysis of Rainfall Prediction Models Using Machine Learning in Islands with Complex Orography: Tenerife Island |
title_full_unstemmed |
Comparative Analysis of Rainfall Prediction Models Using Machine Learning in Islands with Complex Orography: Tenerife Island |
title_sort |
comparative analysis of rainfall prediction models using machine learning in islands with complex orography: tenerife island |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2019 |
url |
https://doi.org/10.3390/app9224931 |
op_coverage |
agris |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Applied Sciences; Volume 9; Issue 22; Pages: 4931 |
op_relation |
Environmental Sciences https://dx.doi.org/10.3390/app9224931 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/app9224931 |
container_title |
Applied Sciences |
container_volume |
9 |
container_issue |
22 |
container_start_page |
4931 |
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1774720464487186432 |