ClimateLearn: A machine-learning approach for climate prediction using network measures
| Autor: | Marc Segond, Shilomo Havlin, Yang Wang, Henk A. Dijkstra, Ruggero Vasile, Markus Abel, Avi Gozolchiani, Qing Yi Feng, Armin Bunde |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
010504 meteorology & atmospheric sciences
Artificial neural network Computer science business.industry Genetic programming Complex network Machine learning computer.software_genre 01 natural sciences Toolbox Sea surface temperature 13. Climate action 0103 physical sciences Artificial intelligence Data mining 010306 general physics Symbolic regression business computer Lead time 0105 earth and related environmental sciences Network analysis |
| ISSN: | 1991-9603 |
| Popis: | We present the toolbox ClimateLearn to tackle problems in climate prediction using machine learning techniques and climate network analysis. The package allows basic operations of data mining, i.e. reading, merging, and cleaning data, and running machine learning algorithms such as multilayer artificial neural networks and symbolic regression with genetic programming. Because spatial temporal information on climate variability can be efficiently represented by complex network measures, such data are considered here as input to the machine-learning algorithms. As an example, the toolbox is applied to the prediction of the occurrence and the development of El Niño in the equatorial Pacific, first concentrating on the occurrence of El Niño events one year ahead and second on the evolution of sea surface temperature anomalies with a lead time of three months. |
| Databáze: | OpenAIRE |
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