ClimateLearn: A machine-learning approach for climate prediction using network measures
Qing Yi Feng1, Ruggero Vasile2,3, Marc Segond4, Avi Gozolchiani5, Yang Wang5, Markus Abel3, Shilomo Havlin5, Armin Bunde6, and Henk A. Dijkstra11Institute for Marine and Atmospheric research Utrecht, Utrecht University, The Netherlands 2UP Transfer, Potsdam, Germany 3Ambrosys, Potsdam, Germany 4European Centre for Soft Computing, Mieres, Spain 5Bar-Ilan University, Isreal 6University of Giessen, Germany
Received: 10 Dec 2015 – Accepted for review: 10 Feb 2016 – Discussion started: 11 Feb 2016
Abstract. 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.
Feng, Q. Y., Vasile, R., Segond, M., Gozolchiani, A., Wang, Y., Abel, M., Havlin, S., Bunde, A., and Dijkstra, H. A.: ClimateLearn: A machine-learning approach for climate prediction using network measures, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2015-273, 2016.