Articles | Volume 21, issue 12
Nat. Hazards Earth Syst. Sci., 21, 3679–3691, 2021

Special issue: Recent advances in drought and water scarcity monitoring,...

Nat. Hazards Earth Syst. Sci., 21, 3679–3691, 2021
Research article
03 Dec 2021
Research article | 03 Dec 2021

Applying machine learning for drought prediction in a perfect model framework using data from a large ensemble of climate simulations

Elizaveta Felsche and Ralf Ludwig

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Cited articles

Barnston, A. G. and Livezey, R. E.: Classification, Seasonality and Persistence of Low-Frequency Atmospheric Circulation Patterns, Mon. Weather Rev., 115, 1083–1126,<1083:CSAPOL>2.0.CO;2, 1987. a
Belayneh, A., Adamowski, J., Khalil, B., and Quilty, J.: Coupling machine learning methods with wavelet transforms and the bootstrap and boosting ensemble approaches for drought prediction, Atmos. Res., 172–173, 37–47,, 2016. a, b
Biesiada, J. and Duch, W.: Feature Selection for High-Dimensional Data – A Pearson Redundancy Based Filter, in: Computer Recognition Systems 2, edited by: Kurzynski, M., Puchala, E., Wozniak, M., and Zolnierek, A., Springer, Berlin, Heidelberg, 242–249, 2007. a
Bishop, C. M.: Pattern Recognition and Machine Learning (Information Science and Statistics), 1st edn., Springer, Berlin, Heidelberg, 2007. a
Bonaccorso, B., Cancelliere, A., and Rossi, G.: Probabilistic forecasting of drought class transitions in Sicily (Italy) using Standardized Precipitation Index and North Atlantic Oscillation Index, J. Hydrol., 526, 136–150,, 2015. a, b, c, d
Short summary
This study applies artificial neural networks to predict drought occurrence in Munich and Lisbon, with a lead time of 1 month. An analysis of the variables that have the highest impact on the prediction is performed. The study shows that the North Atlantic Oscillation index and air pressure 1 month before the event have the highest importance for the prediction. Moreover, it shows that seasonality strongly influences the goodness of prediction for the Lisbon domain.
Final-revised paper