2021
DOI: 10.3390/rs13132468
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Machine Learning for Climate Precipitation Prediction Modeling over South America

Abstract: Many natural disasters in South America are linked to meteorological phenomena. Therefore, forecasting and monitoring climatic events are fundamental issues for society and various sectors of the economy. In the last decades, machine learning models have been developed to tackle different issues in society, but there is still a gap in applications to applied physics. Here, different machine learning models are evaluated for precipitation prediction over South America. Currently, numerical weather prediction mo… Show more

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Cited by 29 publications
(16 citation statements)
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References 32 publications
(35 reference statements)
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“…Given the topographic heterogeneity and diverse atmospheric systems acting on the continent, the systematic errors of the model may be associated with the misrepresentation of regional features, such as the Andean topography and the mesoscale/local scale phenomena as the formation of convective complexes, orographic rains, and breeze circulations. In this scenario, machine learning is a promising methodology, given that seasonal predictions of precipitation for SA with the neural networks technique have resulted in up to a 75% error reduction in estimates [240].…”
Section: Discussionmentioning
confidence: 99%
“…Given the topographic heterogeneity and diverse atmospheric systems acting on the continent, the systematic errors of the model may be associated with the misrepresentation of regional features, such as the Andean topography and the mesoscale/local scale phenomena as the formation of convective complexes, orographic rains, and breeze circulations. In this scenario, machine learning is a promising methodology, given that seasonal predictions of precipitation for SA with the neural networks technique have resulted in up to a 75% error reduction in estimates [240].…”
Section: Discussionmentioning
confidence: 99%
“…Application of statistical models is on the increase in a number of areas such as prediction of precipitation ( [12,[38][39][40][41][42], river flow ( [43][44][45][46][47]), and temperature ( [48,49], and [50]). Some recent studies on modelling water quality using statistical methods include Wadkar and Kote [51], Li et al [52], García-Ávila et al [29], and De Santi et al [53]).…”
Section: Discussionmentioning
confidence: 99%
“…There were four ML and one AI algorithm employed in this study. They are distributed random forest (DRF) [30], generalized linear model (GLM) [31], extreme gradient boosting (XGBoost) [32], generalized boosting machine (GBM) [33], and deep learning (DL) [34]- [36]. These algorithms were trained as binomial classifiers as characterized by the training datasets of each method.…”
Section: Modelmentioning
confidence: 99%