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
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.
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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
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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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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
-
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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Cited
11 citations as recorded by crossref.
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- Forecasting the El Niño type well before the spring predictability barrier J. Ludescher et al. 10.1038/s41612-023-00519-8
- Deep Residual Convolutional Neural Network Combining Dropout and Transfer Learning for ENSO Forecasting J. Hu et al. 10.1029/2021GL093531
- Network-based forecasting of climate phenomena J. Ludescher et al. 10.1073/pnas.1922872118
- Unravelling the spatial diversity of Indian precipitation teleconnections via a non-linear multi-scale approach J. Kurths et al. 10.5194/npg-26-251-2019
- 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
- Evaluation of the real-time El Niño forecasts by the climate network approach between 2011 and present A. Bunde et al. 10.1007/s00704-024-05035-0
- Spatial-temporal transformer network for multi-year ENSO prediction D. Song et al. 10.3389/fmars.2023.1143499
- Using network theory and machine learning to predict El Niño P. Nooteboom et al. 10.5194/esd-9-969-2018