Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall
Abstract
One of the most challenging tasks in rainfall prediction is designing a reliable computational methodology owing the random and stochastic characteristics of time-series. In this study, the potential of five different data-driven models including Multilayer Perceptron (MLP), Least Square Support Vector Machine (LSSVM), Neuro-fuzzy, Hammerstein-Weiner (HW) and Autoregressive Integrated Moving Average (ARIMA) were employed for multi-station (Hien,...
Paper Details
Title
Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall
Published Date
Nov 30, 2019
Journal
Volume
33
Issue
15
Pages
5067 - 5087
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