Multi-stage hybridized online sequential extreme learning machine integrated with Markov Chain Monte Carlo copula-Bat algorithm for rainfall forecasting

To ameliorate agricultural impacts due to persistent drought-risks by promoting sustainable utilization and pre-planning of water resources, accurate rainfall forecasting models, addressing the dynamic nature of drought phenomenon, is crucial. In this paper, a multi-stage probabilistic machine learn...

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Vydané v:	Atmospheric research Ročník 213; s. 450 - 464
Hlavní autori:	Ali, Mumtaz, Deo, Ravinesh C., Downs, Nathan J., Maraseni, Tek
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 15.11.2018
Predmet:	algorithms And RF artificial intelligence autocorrelation Bat algorithm Copulas data collection decision support systems drought ELM Markov chain Markov chain Monte Carlo simulation OS-ELM Pakistan rain Rainfall forecasting semiarid zones water management water resources Pakistan Copulas Bat algorithm ELM OS-ELM And RF Markov chain Monte Carlo simulation Rainfall forecasting
ISSN:	0169-8095, 1873-2895
On-line prístup:	Získať plný text
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