LSTM-CM: a hybrid approach for natural drought prediction based on deep learning and climate models
- 3 January 2023
- journal article
- research article
- Published by Springer Nature in Stochastic Environmental Research and Risk Assessment
- Vol. 37 (6) , 2035-2051
Abstract
No abstract availableKeywords
Funding Information
- Sejong University Faculty program
This publication has 50 references indexed in Scilit:
- A review on climate‐model‐based seasonal hydrologic forecasting: physical understanding and system developmentWIREs Water, 2015
- Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern AustraliaAtmospheric Research, 2015
- Global Seasonal forecast system version 5 (GloSea5): a high‐resolution seasonal forecast systemQuarterly Journal of the Royal Meteorological Society, 2014
- Mitigating Socio-Economic-Environmental Impacts During Drought Periods by Optimizing the Conjunctive Management of Water ResourcesWater Resources Management, 2014
- Dynamic-Model-Based Seasonal Prediction of Meteorological Drought over the Contiguous United StatesJournal of Hydrometeorology, 2012
- Drought modeling – A reviewJournal of Hydrology, 2011
- The Application Assessment of Global Hydrologic Analysis Models on South KoreaJournal of Korea Water Resources Association, 2010
- Learning to Forget: Continual Prediction with LSTMNeural Computation, 2000
- Long Short-Term MemoryNeural Computation, 1997
- A simple hydrologically based model of land surface water and energy fluxes for general circulation modelsJournal of Geophysical Research: Atmospheres, 1994