Extreme Rainfall Indices Prediction with Atmospheric Parameters and Ocean–Atmospheric Teleconnections Using a Random Forest Model

Globally, extreme rainfall has intense impacts on ecosystems and human livelihoods. However, no effort has yet been made to forecast the extreme rainfall indices through machine learning techniques. In this paper, a new extreme rainfall indices forecasting model is proposed using a random forest (RF...

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Vydané v:	Journal of applied meteorology and climatology Ročník 61; číslo 6; s. 651 - 667
Hlavní autori:	Uddin, Md. Jalal, Li, Yubin, Tamim, Md. Yahya, Miah, Md. Babul, Ahmed, S. M. Shahriar
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Boston American Meteorological Society 01.06.2022
Predmet:	Air temperature Atmospheric forcing Convective available potential energy Dipoles El Nino El Nino phenomena El Nino-Southern Oscillation event Environmental impact Extreme weather Forecasting Heavy rainfall Lead time Livelihoods Machine learning Mathematical models Modelling Monthly rainfall North Atlantic Oscillation Ocean-atmosphere system Oceans Parameters Potential energy Rain Rainfall Rainfall forecasting Relative humidity Southern Oscillation Statistical analysis Teleconnections
ISSN:	1558-8424, 1558-8432
On-line prístup:	Získať plný text
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