Drought Prediction Using Recurrent Neural Networks and Long Short-Term Memory Model
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-5443-6_8
Reference15 articles.
1. Dakshin, D., Rupesh, V. R., & Praveen Kumar, S. (2019). Water hazard prediction using machine learning. International Journal of Innovative Technology and Exploring Engineering Regular Issue, 9(1), 1451–1457. https://doi.org/10.35940/ijitee.a4245.119119
2. Elsafi, S. H. (2014). Artificial neural networks (ANNs) for flood forecasting at Dongola Station in the River Nile Sudan. Alexandria Engineering Journal, 53(3), 655–662. https://doi.org/10.1016/j.aej.2014.06.010
3. Dikshit, A., Pradhan, B., Alamri, A. M. (2021). Long lead time drought forecasting using lagged climate variables and a stacked long short-term memory model. Science of The Total Environment 755(Part 2), 142638. https://doi.org/10.1016/j.scitotenv.2020.142638
4. Abbot, J., & Marohasy, J. (2014). Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks. Atmospheric Research, 138, 166–178. https://doi.org/10.1016/j.atmosres.2013.11.002
5. Aghakouchak, A., Farahmand, A., Melton, F. S., Teixeira, J., Anderson, M. C., Wardlow, B. D., & Hain, C. R. (2015). Remote sensing of drought: Progress, challenges and opportunities. Reviews of Geophysics, 53(2), 452–480. https://doi.org/10.1002/2014rg000456
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