Spatiotemporal Comparative Analysis of Dry/Wet Phenomenon of the Rainy Period Using Artificial Neural Networks and Markov Chains
Author:
Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Geography, Planning and Development
Link
https://link.springer.com/content/pdf/10.1007/s12524-023-01720-1.pdf
Reference43 articles.
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2. AghaKouchak, A. (2015). A multivariate approach for persistence-based drought prediction: Application to the 2010–2011 East Africa drought. Journal of Hydrology, 526, 127–135. https://doi.org/10.1016/j.jhydrol.2014.09.063.
3. AghaKouchak, A., & Nakhjiri, N. (2012). A near real-time satellite-based global drought climate data record. Environmental Research Letters, 7(4), 044037. https://doi.org/10.1088/1748-9326/7/4/044037
4. Altunkaynak, A., & Jalilzadnezamabad, A. (2021). Extended lead time accurate forecasting of palmer drought severity index using hybrid wavelet-fuzzy and machine learning techniques. Journal of Hydrology, 601, 126619. https://doi.org/10.1016/j.jhydrol.2021.126619.
5. Bayissa, Y. A. (2018). Developing an impact-based combined drought index for monitoring crop yield anomalies in the Upper Blue Nile Basin, Ethiopia, 1st edn. London: CRC Press. https://doi.org/10.1201/9780429399510
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