Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.atmosres.2019.104720 |
Physical-empirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia | |
Pour, Sahar Hadi1,2; Abd Wahab, Ahmad Khairi1,2; Shahid, Shamsuddin1,2,3 | |
2020-03-01 | |
发表期刊 | ATMOSPHERIC RESEARCH |
ISSN | 0169-8095 |
EISSN | 1873-2895 |
出版年 | 2020 |
卷号 | 233 |
文章类型 | Article |
语种 | 英语 |
国家 | Malaysia |
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. |
英文关键词 | Extreme rainfall Climate forecasting Physical-empirical model Machine learning algorithm Recursive feature elimination |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000513180200017 |
WOS关键词 | SUPPORT VECTOR MACHINE ; CLIMATE-CHANGE ; SUMMER RAINFALL ; MONSOON RAINFALL ; NEURAL-NETWORKS ; EAST-COAST ; PRECIPITATION ; TEMPERATURE ; ENSEMBLE ; FORECAST |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/278809 |
专题 | 地球科学 |
作者单位 | 1.Univ Teknol Malaysia, Fac Engn, Sch Civil Engn, Johor Baharu 81310, Malaysia; 2.Univ Teknol Malaysia, Ctr Coastal & Ocean Engn COEI, Kuala Lumpur 54100, Malaysia; 3.Univ Malaysia Terengganu, Inst Oceanog & Environm INOS, Kuala Terengganu 21300, Terengganu, Malaysia |
推荐引用方式 GB/T 7714 | Pour, Sahar Hadi,Abd Wahab, Ahmad Khairi,Shahid, Shamsuddin. Physical-empirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia[J]. ATMOSPHERIC RESEARCH,2020,233. |
APA | Pour, Sahar Hadi,Abd Wahab, Ahmad Khairi,&Shahid, Shamsuddin.(2020).Physical-empirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia.ATMOSPHERIC RESEARCH,233. |
MLA | Pour, Sahar Hadi,et al."Physical-empirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia".ATMOSPHERIC RESEARCH 233(2020). |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论