the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
ClimateLearn: A machine-learning approach for climate prediction using network measures
Qing Yi Feng
Ruggero Vasile
Marc Segond
Avi Gozolchiani
Yang Wang
Markus Abel
Shilomo Havlin
Armin Bunde
Henk A. Dijkstra
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.
Qing Yi Feng et al.


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RC1: 'Comments for ClimateLearn manuscript', Anonymous Referee #1, 16 Mar 2016
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AC1: 'Point by point reply to reviewer #1', Qing Yi Feng, 17 Sep 2016
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AC1: 'Point by point reply to reviewer #1', Qing Yi Feng, 17 Sep 2016
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RC2: 'Review', Anonymous Referee #2, 21 Aug 2016
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AC2: 'Point by point reply to reviewer #2', Qing Yi Feng, 17 Sep 2016
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AC2: 'Point by point reply to reviewer #2', Qing Yi Feng, 17 Sep 2016


-
RC1: 'Comments for ClimateLearn manuscript', Anonymous Referee #1, 16 Mar 2016
-
AC1: 'Point by point reply to reviewer #1', Qing Yi Feng, 17 Sep 2016
-
AC1: 'Point by point reply to reviewer #1', Qing Yi Feng, 17 Sep 2016
-
RC2: 'Review', Anonymous Referee #2, 21 Aug 2016
-
AC2: 'Point by point reply to reviewer #2', Qing Yi Feng, 17 Sep 2016
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AC2: 'Point by point reply to reviewer #2', Qing Yi Feng, 17 Sep 2016
Qing Yi Feng et al.
Qing Yi Feng et al.
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Cited
9 citations as recorded by crossref.
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- Spatiotemporal Model Based on Deep Learning for ENSO Forecasts H. Geng & T. Wang 10.3390/atmos12070810
- The Prediction of ENSO Indexes Based on Time Series LSTM Model 福. 陈 10.12677/CCRL.2019.83032
- The Application of Machine Learning Techniques to Improve El Niño Prediction Skill H. Dijkstra et al. 10.3389/fphy.2019.00153
- A climate network perspective on the intertropical convergence zone F. Wolf et al. 10.5194/esd-12-353-2021
- Using network theory and machine learning to predict El Niño P. Nooteboom et al. 10.5194/esd-9-969-2018