Articles | Volume 21, issue 12
https://doi.org/10.5194/nhess-21-3679-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-21-3679-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Dec 2021
Research article |
| 03 Dec 2021
| 03 Dec 2021
Applying machine learning for drought prediction in a perfect model framework using data from a large ensemble of climate simulations
Center for Digital Technology and Management, Munich, Germany
Department of Geography, Ludwig Maximilian University of Munich, Munich, Germany
Ralf Ludwig
Department of Geography, Ludwig Maximilian University of Munich, Munich, Germany
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- Applying Machine Learning for Threshold Selection in Drought Early Warning System H. Luo et al. https://doi.org/10.3390/cli10070097
- Wildfire risk in a changing climate: Evaluating fire weather indices and their global patterns with CMIP6 multi-model projections Y. He et al. https://doi.org/10.1016/j.wace.2025.100751
- Leveraging Artificial Intelligence to Address Climate Change T. Kumar et al. https://doi.org/10.38124/ijisrt/IJISRT24AUG020
- Integrating a hybrid machine learning model with climate projections for landslide susceptibility assessment M. Tumon et al. https://doi.org/10.1016/j.rineng.2025.108067
- Evaluating Performance of Multiple Machine Learning Models for Drought Monitoring: A Case Study of Typical Grassland in Inner Mongolia Y. Wang et al. https://doi.org/10.3390/land13060754
- Precipitation and vegetation transpiration variations dominate the dynamics of agricultural drought characteristics in China W. Guo et al. https://doi.org/10.1016/j.scitotenv.2023.165480
- Utilizing machine learning and CMIP6 projections for short-term agricultural drought monitoring in central Europe (1900–2100) S. Mohammed et al. https://doi.org/10.1016/j.jhydrol.2024.130968
- Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives S. Materia et al. https://doi.org/10.1002/wcc.914
- Evaluation of deep learning approaches for classification of drought stages using satellite imagery for Tharparker M. Raza et al. https://doi.org/10.33317/ssurj.450
- Unleashing the power of machine learning and remote sensing for robust seasonal drought monitoring: A stacking ensemble approach X. Xu et al. https://doi.org/10.1016/j.jhydrol.2024.131102
- Tracking shifts in European drought hotspots M. Merlo et al. https://doi.org/10.1088/1748-9326/ae4ca7
- Inter-seasonal connection of typical European heatwave patterns to soil moisture E. Felsche et al. https://doi.org/10.1038/s41612-023-00330-5
- Evaluation of Tropical Cyclone Disaster Loss Using Machine Learning Algorithms with an eXplainable Artificial Intelligence Approach S. Liu et al. https://doi.org/10.3390/su151612261
- Machine learning algorithms for the prediction of drought conditions in the Wami River sub-catchment, Tanzania C. Lalika et al. https://doi.org/10.1016/j.ejrh.2024.101794
- Data-driven seasonal climate predictions via variational inference and transformers L. Palma et al. https://doi.org/10.1038/s41612-026-01320-z
- Deep learning models for drought susceptibility mapping in Southeast Queensland, Australia F. Rezaie et al. https://doi.org/10.1007/s00477-024-02879-w
- Metabolomics-guided engineering of drought-resilient crops: Integrating multi-omics and AI for climate-smart agriculture C. Kaya https://doi.org/10.1016/j.plantsci.2026.113025
- Explainable machine learning for the prediction and assessment of complex drought impacts B. Zhang et al. https://doi.org/10.1016/j.scitotenv.2023.165509
- STAT-LSTM: A multivariate spatiotemporal feature aggregation model for SPEI-based drought prediction Y. Chen et al. https://doi.org/10.1007/s12145-025-01813-0
- Manifesting deep learning algorithms for developing drought vulnerability index in monsoon climate dominant region of West Bengal, India S. Saha et al. https://doi.org/10.1007/s00704-022-04300-4
- Revisiting the composite drought index for improving drought monitoring M. Faiz et al. https://doi.org/10.1016/j.jhydrol.2025.132707
- Preface: Recent advances in drought and water scarcity monitoring, modelling, and forecasting B. Bonaccorso et al. https://doi.org/10.5194/nhess-22-1857-2022
- A Contemporary Review on Deep Learning Models for Drought Prediction A. Gyaneshwar et al. https://doi.org/10.3390/su15076160
- Robust drought forecasting in Eastern Canada: Leveraging EMD-TVF and ensemble deep RVFL for SPEI index forecasting M. Karbasi et al. https://doi.org/10.1016/j.eswa.2024.124900
- Drought disaster monitoring and land use dynamics: identification of drought drivers using regression-based algorithms I. Orimoloye et al. https://doi.org/10.1007/s11069-022-05219-9
- Tendencias de sequía extrema en Puebla: índices climáticos y socioeconómicos con implicaciones para la gestión del agua. M. Velasco Hernández et al. https://doi.org/10.22231/asyd.v21i1.1627
- Assessment of Multiple Machine Learning Models for Critical Mineral Exploration Data Quality in the Brazilian Shield H. Barbosa https://doi.org/10.3390/app16136658
- Drought index time series forecasting via three-in-one machine learning concept for the Euphrates basin L. Latifoğlu et al. https://doi.org/10.1007/s12145-024-01471-8
- Improving meteorological drought simulation in Iran using wavelet-enhanced deep learning models M. Heydarizad et al. https://doi.org/10.1007/s00704-025-05525-9
- Interpretability of compound drought-hot extreme index prediction model: a regional study in Iran M. Moghaddasi et al. https://doi.org/10.1007/s11356-025-36257-y
- Projection and assessment of future droughts in Iowa: developing a machine learning model and an interactive application I. Cintura & A. Arenas https://doi.org/10.3389/fenvs.2025.1564670
- Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling R. O'Loughlin et al. https://doi.org/10.5194/gmd-18-787-2025
- Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models S. Hauswirth et al. https://doi.org/10.3389/frwa.2023.1108108
- Compound hot–dry extremes in Senegal: trends and time of emergence in a warming world A. Seck et al. https://doi.org/10.1088/2515-7620/ae6f8d
- Explainable artificial intelligence in disaster risk management: Achievements and prospective futures S. Ghaffarian et al. https://doi.org/10.1016/j.ijdrr.2023.104123
- Explaining heatwaves with machine learning S. Buschow et al. https://doi.org/10.1002/qj.4642
- A XGBoost-based composite drought index combining multi-source remote sensing data for drought monitoring in China X. Huang et al. https://doi.org/10.1016/j.ejrh.2026.103623
- Artificial intelligence predicts normal summer monsoon rainfall for India in 2023 U. Narang et al. https://doi.org/10.1038/s41598-023-44284-3
- Unlocking drought in pistachio orchards: monitoring and forecasting using landsat time series and machine learning techniques N. Zarei et al. https://doi.org/10.1007/s13580-025-00689-9
- The Drought Regime in Southern Africa: A Systematic Review F. Chivangulula et al. https://doi.org/10.3390/cli11070147
- Could climate change exacerbate droughts in Bangladesh in the future? M. Rahman et al. https://doi.org/10.1016/j.jhydrol.2023.130096
41 citations as recorded by crossref.
- Applying Machine Learning for Threshold Selection in Drought Early Warning System H. Luo et al. https://doi.org/10.3390/cli10070097
- Wildfire risk in a changing climate: Evaluating fire weather indices and their global patterns with CMIP6 multi-model projections Y. He et al. https://doi.org/10.1016/j.wace.2025.100751
- Leveraging Artificial Intelligence to Address Climate Change T. Kumar et al. https://doi.org/10.38124/ijisrt/IJISRT24AUG020
- Integrating a hybrid machine learning model with climate projections for landslide susceptibility assessment M. Tumon et al. https://doi.org/10.1016/j.rineng.2025.108067
- Evaluating Performance of Multiple Machine Learning Models for Drought Monitoring: A Case Study of Typical Grassland in Inner Mongolia Y. Wang et al. https://doi.org/10.3390/land13060754
- Precipitation and vegetation transpiration variations dominate the dynamics of agricultural drought characteristics in China W. Guo et al. https://doi.org/10.1016/j.scitotenv.2023.165480
- Utilizing machine learning and CMIP6 projections for short-term agricultural drought monitoring in central Europe (1900–2100) S. Mohammed et al. https://doi.org/10.1016/j.jhydrol.2024.130968
- Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives S. Materia et al. https://doi.org/10.1002/wcc.914
- Evaluation of deep learning approaches for classification of drought stages using satellite imagery for Tharparker M. Raza et al. https://doi.org/10.33317/ssurj.450
- Unleashing the power of machine learning and remote sensing for robust seasonal drought monitoring: A stacking ensemble approach X. Xu et al. https://doi.org/10.1016/j.jhydrol.2024.131102
- Tracking shifts in European drought hotspots M. Merlo et al. https://doi.org/10.1088/1748-9326/ae4ca7
- Inter-seasonal connection of typical European heatwave patterns to soil moisture E. Felsche et al. https://doi.org/10.1038/s41612-023-00330-5
- Evaluation of Tropical Cyclone Disaster Loss Using Machine Learning Algorithms with an eXplainable Artificial Intelligence Approach S. Liu et al. https://doi.org/10.3390/su151612261
- Machine learning algorithms for the prediction of drought conditions in the Wami River sub-catchment, Tanzania C. Lalika et al. https://doi.org/10.1016/j.ejrh.2024.101794
- Data-driven seasonal climate predictions via variational inference and transformers L. Palma et al. https://doi.org/10.1038/s41612-026-01320-z
- Deep learning models for drought susceptibility mapping in Southeast Queensland, Australia F. Rezaie et al. https://doi.org/10.1007/s00477-024-02879-w
- Metabolomics-guided engineering of drought-resilient crops: Integrating multi-omics and AI for climate-smart agriculture C. Kaya https://doi.org/10.1016/j.plantsci.2026.113025
- Explainable machine learning for the prediction and assessment of complex drought impacts B. Zhang et al. https://doi.org/10.1016/j.scitotenv.2023.165509
- STAT-LSTM: A multivariate spatiotemporal feature aggregation model for SPEI-based drought prediction Y. Chen et al. https://doi.org/10.1007/s12145-025-01813-0
- Manifesting deep learning algorithms for developing drought vulnerability index in monsoon climate dominant region of West Bengal, India S. Saha et al. https://doi.org/10.1007/s00704-022-04300-4
- Revisiting the composite drought index for improving drought monitoring M. Faiz et al. https://doi.org/10.1016/j.jhydrol.2025.132707
- Preface: Recent advances in drought and water scarcity monitoring, modelling, and forecasting B. Bonaccorso et al. https://doi.org/10.5194/nhess-22-1857-2022
- A Contemporary Review on Deep Learning Models for Drought Prediction A. Gyaneshwar et al. https://doi.org/10.3390/su15076160
- Robust drought forecasting in Eastern Canada: Leveraging EMD-TVF and ensemble deep RVFL for SPEI index forecasting M. Karbasi et al. https://doi.org/10.1016/j.eswa.2024.124900
- Drought disaster monitoring and land use dynamics: identification of drought drivers using regression-based algorithms I. Orimoloye et al. https://doi.org/10.1007/s11069-022-05219-9
- Tendencias de sequía extrema en Puebla: índices climáticos y socioeconómicos con implicaciones para la gestión del agua. M. Velasco Hernández et al. https://doi.org/10.22231/asyd.v21i1.1627
- Assessment of Multiple Machine Learning Models for Critical Mineral Exploration Data Quality in the Brazilian Shield H. Barbosa https://doi.org/10.3390/app16136658
- Drought index time series forecasting via three-in-one machine learning concept for the Euphrates basin L. Latifoğlu et al. https://doi.org/10.1007/s12145-024-01471-8
- Improving meteorological drought simulation in Iran using wavelet-enhanced deep learning models M. Heydarizad et al. https://doi.org/10.1007/s00704-025-05525-9
- Interpretability of compound drought-hot extreme index prediction model: a regional study in Iran M. Moghaddasi et al. https://doi.org/10.1007/s11356-025-36257-y
- Projection and assessment of future droughts in Iowa: developing a machine learning model and an interactive application I. Cintura & A. Arenas https://doi.org/10.3389/fenvs.2025.1564670
- Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling R. O'Loughlin et al. https://doi.org/10.5194/gmd-18-787-2025
- Simulating hydrological extremes for different warming levels–combining large scale climate ensembles with local observation based machine learning models S. Hauswirth et al. https://doi.org/10.3389/frwa.2023.1108108
- Compound hot–dry extremes in Senegal: trends and time of emergence in a warming world A. Seck et al. https://doi.org/10.1088/2515-7620/ae6f8d
- Explainable artificial intelligence in disaster risk management: Achievements and prospective futures S. Ghaffarian et al. https://doi.org/10.1016/j.ijdrr.2023.104123
- Explaining heatwaves with machine learning S. Buschow et al. https://doi.org/10.1002/qj.4642
- A XGBoost-based composite drought index combining multi-source remote sensing data for drought monitoring in China X. Huang et al. https://doi.org/10.1016/j.ejrh.2026.103623
- Artificial intelligence predicts normal summer monsoon rainfall for India in 2023 U. Narang et al. https://doi.org/10.1038/s41598-023-44284-3
- Unlocking drought in pistachio orchards: monitoring and forecasting using landsat time series and machine learning techniques N. Zarei et al. https://doi.org/10.1007/s13580-025-00689-9
- The Drought Regime in Southern Africa: A Systematic Review F. Chivangulula et al. https://doi.org/10.3390/cli11070147
- Could climate change exacerbate droughts in Bangladesh in the future? M. Rahman et al. https://doi.org/10.1016/j.jhydrol.2023.130096
Saved (final revised paper)
Latest update: 25 Aug 2026
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.
This study applies artificial neural networks to predict drought occurrence in Munich and...
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