2021
DOI: 10.21203/rs.3.rs-779973/v1
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Improving Hybrid Models For Precipitation Forecasting By Combining Nonlinear Machine Learning Methods

Abstract: Precipitation forecast, especially on monthly and annual scales, is a key for optimal water resources management and planning, especially in semiarid climates with scarce water. The traditional hybrid models, in which two statistical models are used to separate and simulate linear and nonlinear components of precipitation time series, are still unable to provide accurate precipitation forecasts. This research aims to improve hybrid forecast models by combining one linear model and three nonlinear models with t… Show more

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Cited by 2 publications
(1 citation statement)
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“…Accurate rainfall prediction is a challenge when using a singular model, so the introduction of a hybrid that combines two models has the potential to deliver performance that exceeds the capabilities of each composing model (Parviz et al 2021). One example of time series forecasting is hybridising least square support vector machines (LSSVM) with GMDH.…”
Section: Hybridising Gmdhmentioning
confidence: 99%
“…Accurate rainfall prediction is a challenge when using a singular model, so the introduction of a hybrid that combines two models has the potential to deliver performance that exceeds the capabilities of each composing model (Parviz et al 2021). One example of time series forecasting is hybridising least square support vector machines (LSSVM) with GMDH.…”
Section: Hybridising Gmdhmentioning
confidence: 99%