气候变化领域集成服务门户(CCPortal): Physical-empirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia

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DOI	10.1016/j.atmosres.2019.104720
	Physical-empirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia
	Pour S.H.; Wahab A.K.A.; Shahid S.
发表日期	2020
ISSN	0169-8095
卷号	233
英文摘要	Reliable prediction of rainfall extremes is vital for disaster management, particularly in the context of increasing rainfall extremes due to global climate change. Physical-empirical models have been developed in this study using three widely used Machine Learning (ML) methods namely, Support Vector Machines (SVM), Random Forests (RF), Bayesian Artificial Neural Networks (BANN) for the prediction of rainfall and rainfall related extremes during Northeast Monsoon (NEM) in Peninsular Malaysia from synoptic predictors. The gridded daily rainfall data of Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) was used to estimate four rainfall indices namely, rainfall amount, average rainfall intensity, days having >95-th percentile rainfall, and total number of dry days in Peninsular Malaysia during NEM for the period 1951–2015. The National Centers for Environmental Prediction (NCEP) reanalysis sea level pressure (SLP) data was used for the prediction of rainfall indices with different lead periods. The recursive feature elimination (RFE) method was used to select the SLP at different NCEP grid points which were found significantly correlated with NEM rainfall indices. The results showed superior performance of BANN among the ML models with normalised root mean square error of 0.04–0.14, Nash-Sutcliff Efficiency of 0.98–1.0, and modified agreement index of 0.97–0.99 and Kling-Gupta efficient index 0.65–0.96 for one-month lead period prediction. The 95% confidence interval (CI) band for BANN was found narrower than the other ML models. Almost all the forecasted values by BANN were also found with 95% CI, and therefore, the p-factor and the r-factor for BANN in predicting rainfall indices were found in the range of 0.95–1.0 and 0.25–0.49 respectively. Application of BANN in prediction of rainfall indices with higher lead time was also found excellent. The synoptic pattern revealed that SLP over the north of South China Sea is the major driver of NEM rainfall and rainfall extremes in Peninsular Malaysia. © 2019 Elsevier B.V.
英文关键词	Climate forecasting; Extreme rainfall; Machine learning algorithm; Physical-empirical model; Recursive feature elimination
学科领域	Bayesian networks; Climate change; Climate models; Data integration; Decision trees; Dielectric properties; Disaster prevention; Disasters; Forecasting; Learning algorithms; Learning systems; Machine learning; Mean square error; Neural networks; Sea level; Support vector machines; Water resources; Bayesian artificial neural networks; Climate forecasting; Empirical model; Extreme rainfall; Global climate changes; National centers for environmental predictions; Recursive feature elimination; Root mean square errors; Rain; algorithm; empirical analysis; extreme event; forecasting method; machine learning; physicochemical property; precipitation intensity; prediction; seasonal variation; Malaysia; West Malaysia
语种	英语
scopus关键词	Bayesian networks; Climate change; Climate models; Data integration; Decision trees; Dielectric properties; Disaster prevention; Disasters; Forecasting; Learning algorithms; Learning systems; Machine learning; Mean square error; Neural networks; Sea level; Support vector machines; Water resources; Bayesian artificial neural networks; Climate forecasting; Empirical model; Extreme rainfall; Global climate changes; National centers for environmental predictions; Recursive feature elimination; Root mean square errors; Rain; algorithm; empirical analysis; extreme event; forecasting method; machine learning; physicochemical property; precipitation intensity; prediction; seasonal variation; Malaysia; West Malaysia
来源期刊	Atmospheric Research
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/120545
作者单位	School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, 81310, Malaysia; Centre for Coastal and Ocean Engineering (COEI), Universiti Teknologi Malaysia (UTM), Kuala Lumpur, 54100, Malaysia; Institute of Oceanography and Environment (INOS), University Malaysia Terengganu (UMT), Kuala Terengganu, Terengganu 21300, Malaysia
推荐引用方式 GB/T 7714	Pour S.H.,Wahab A.K.A.,Shahid S.. Physical-empirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia[J],2020,233.
APA	Pour S.H.,Wahab A.K.A.,&Shahid S..(2020).Physical-empirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia.Atmospheric Research,233.
MLA	Pour S.H.,et al."Physical-empirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia".Atmospheric Research 233(2020).