Comparison of four machine learning algorithms for their applicability in satellite-based optical rainfall retrievals

Atmospheric Research

Volumn 169, Issue , 2016, Pages 424-433

  Meyer, Hanna ^a   Kühnlein, Meike ^a   Appelhans, Tim ^a   Nauss, Thomas ^a  

^a PHILIPPS UNIVERSITY MARBURG   (Germany)

Author keywords

Machine learning; MSG SEVIRI; Rainfall area; Rainfall rate; Rainfall retrieval

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPLEX NETWORKS; DECISION TREES; INTERACTIVE COMPUTER SYSTEMS; LEARNING SYSTEMS; OPTIMIZATION; RAIN; REAL TIME SYSTEMS; REMOTE SENSING; SATELLITES; SUPPORT VECTOR MACHINES;

CLOUD TOP HEIGHTS; CLOUD-TOP TEMPERATURES; COMPUTATIONAL SPEED; HIGH PROBABILITY; HIGH-DIMENSIONAL; MSG SEVIRI; PREDICTOR VARIABLES; RAINFALL RATES;

LEARNING ALGORITHMS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; CLIMATE PREDICTION; DATA SET; MACHINE LEARNING; PROXY CLIMATE RECORD; RAINFALL; REAL TIME; REMOTE SENSING; SUPPORT VECTOR MACHINE;

EID: 84954105449     PISSN: 01698095     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.atmosres.2015.09.021     Document Type: Article

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