希尔伯特-黄变换
人工神经网络
计算机科学
支持向量机
人工智能
时间序列
预测建模
数据挖掘
主成分分析
模式(计算机接口)
机器学习
计算机视觉
滤波器(信号处理)
操作系统
作者
Yu Xiang,Ling Gou,Lihua He,Shoulu Xia,Wenyong Wang
标识
DOI:10.1016/j.asoc.2018.09.018
摘要
Accurate and timely rainfall prediction is very important in hydrological modeling. Various prediction methods have been proposed in recent years. In this work, information regarding the short-to-long time variation inside original rainfall time series is explored using Ensemble Empirical Mode Decomposition (EEMD) based analysis on three rainfall datasets collected by meteorological stations located in Kunming, Lincang and Mengzi, Yunnan Province, China. Considering both with prediction accuracy and time efficiency, a novel combined model based on the information extracted with EEMD is then proposed in this paper. This model adopts various supervised learning methods for different components of input data, which employs Support Vector Regression (SVR) for short-period component prediction, while Artificial Neural Network (ANN) for long-period components prediction. Our research shows better performances than traditional methods that provides new thinking in rainfall prediction area.
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