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A systematic review of predictor screening methods for downscaling of numerical climate models

文献类型：期刊论文

作者Baghanam, Aida Hosseini5; Nourani, Vahid3,4,5; Bejani, Mohammad5; Pourali, Hadi5; Kantoush, Sameh Ahmed2; Zhang, Yongqiang1
刊名EARTH-SCIENCE REVIEWS
出版日期2024-06-01
卷号253页码:32
关键词Predictor screening (PS) Feature selection (FS) Feature extraction (FE) Climate modeling Climate change GCMs
ISSN号0012-8252
DOI10.1016/j.earscirev.2024.104773
英文摘要Effective selection of climate predictors is a fundamental aspect of climate modeling research. Predictor Screening (PS) plays a crucial role in identifying regional climate drivers, reducing noise, expediting convergence, and minimizing time consumption, ultimately leading to the development of robust models. This review delves into the complex landscape of PS techniques within the context of Numerical Climate Modeling (NCM), with a specific focus on their applicability across various Koppen climate classifications and PS model structures. The analysis revealed substantial variations in the performance of PS methods, shedding light on their ability to capture -and prioritize predictors related to precipitation and temperature within distinct climate contexts. Furthermore, the provided methods have been categorized into two subsections: Feature Selection (FS) and Feature Extraction (FE), with FS encompassing filter, wrapper, embedded, and ensemble/hybrid techniques, and FE covering Linear Feature Extraction (LFE), Time-Domain Analysis (TDA), deep learning, and clustering methods. The initial compilation of papers, acquired through a keyword search on Scopus, consisted of 3650 documents. Following a meticulous evaluation process, 206 papers were identified as fitting for inclusion in the literature review, covering the time frame from 1974 to November 3, 2023. In conclusion, the results provide a detailed understanding of the strengths and limitations of each approach, establishing a hierarchy of effectiveness contingent upon the specific climate context. Additionally, insights into promising avenues for future research in this field are offered. This review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) standard as its foundation.
WOS关键词EXTREME LEARNING-MACHINE ; NONLINEAR DIMENSIONALITY REDUCTION ; MULTIPLE LINEAR-REGRESSION ; MULTISITE DAILY RAINFALL ; DAILY PRECIPITATION ; NEURAL-NETWORKS ; RIVER-BASIN ; SURFACE-TEMPERATURE ; GENETIC ALGORITHM ; WAVELET TRANSFORM
资助项目Iran National Science Foundation through Iran-China (INSF-NSFC) joint projects[4021444]
WOS研究方向Geology
语种英语
WOS记录号WOS:001230448800001
出版者ELSEVIER
资助机构Iran National Science Foundation through Iran-China (INSF-NSFC) joint projects
源URL[http://ir.igsnrr.ac.cn/handle/311030/205546]  
专题陆地水循环及地表过程院重点实验室_外文论文
通讯作者Nourani, Vahid
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
2.Kyoto Univ, Water Resources Res Ctr, Disaster Prevent Res Inst, Uji 6110011, Japan
3.Charles Darwin Univ, Coll Engn IT & Environm, Darwin, NT 0909, Australia
4.Near East Univ, Fac Civil & Environm Engn, Dept Civil Engn, Via Mersin 10, TR-99138 Nicosia, Turkiye
5.Univ Tabriz, Fac Civil Engn, Ctr Excellence Hydroinformat, Tabriz 51666 16471, Iran
推荐引用方式
GB/T 7714		 Baghanam, Aida Hosseini,Nourani, Vahid,Bejani, Mohammad,et al. A systematic review of predictor screening methods for downscaling of numerical climate models[J]. EARTH-SCIENCE REVIEWS,2024,253:32.
APA Baghanam, Aida Hosseini,Nourani, Vahid,Bejani, Mohammad,Pourali, Hadi,Kantoush, Sameh Ahmed,&Zhang, Yongqiang.(2024).A systematic review of predictor screening methods for downscaling of numerical climate models.EARTH-SCIENCE REVIEWS,253,32.
MLA Baghanam, Aida Hosseini,et al."A systematic review of predictor screening methods for downscaling of numerical climate models".EARTH-SCIENCE REVIEWS 253(2024):32.

入库方式： OAI收割

来源：地理科学与资源研究所

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