Márquez-Grajales Aldo, Villegas-Vega Ramiro, Salas-Martínez Fernando, Acosta-Mesa Héctor-Gabriel, Mezura-Montes Efrén
INFOTEC Center for Research and Innovation in Information and Communication Technologies, Circuito Tecnopolo Sur, No 112, Fracc. Tecnopolo Pocitos, Aguascalientes, 20326, Aguascalientes, México.
Artificial Intelligence Research Institute, University of Veracruz, Campus Sur Paseo Lote II, Sección Segunda N° 112, Nuevo Xalapa, 91097, Xalapa, Veracruz, México.
MethodsX. 2024 Jun 12;13:102800. doi: 10.1016/j.mex.2024.102800. eCollection 2024 Dec.
is a complex phenomenon that impacts human activities and the environment. For this reason, predicting its behavior is crucial to mitigating such effects. Deep learning techniques are emerging as a powerful tool for this task. The main goal of this work is to review the state-of-the-art for characterizing the deep learning techniques used in the drought prediction task. The results suggest that the most widely used climate indexes were the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Regarding the multispectral index, the Normalized Difference Vegetation Index (NDVI) is the indicator most utilized. On the other hand, countries with a higher production of scientific knowledge in this area are located in Asia and Oceania; meanwhile, America and Africa are the regions with few publications. Concerning deep learning methods, the Long-Short Term Memory network (LSTM) is the algorithm most implemented for this task, either implemented canonically or together with other deep learning techniques (hybrid methods). In conclusion, this review reveals a need for more scientific knowledge about drought prediction using multispectral indices and deep learning techniques in America and Africa; therefore, it is an opportunity to characterize the phenomenon in developing countries.
是一个影响人类活动和环境的复杂现象。因此，预测其行为对于减轻此类影响至关重要。深度学习技术正成为完成这项任务的强大工具。这项工作的主要目标是回顾用于干旱预测任务的深度学习技术的最新进展。结果表明，使用最广泛的气候指数是标准化降水指数（SPI）和标准化降水蒸散指数（SPEI）。关于多光谱指数，归一化植被指数（NDVI）是使用最多的指标。另一方面，在该领域科学知识产出较高的国家位于亚洲和大洋洲；与此同时，美洲和非洲是出版物较少的地区。关于深度学习方法，长短期记忆网络（LSTM）是为此任务实施最多的算法，要么以标准方式实施，要么与其他深度学习技术（混合方法）一起实施。总之，本综述揭示了美洲和非洲在使用多光谱指数和深度学习技术进行干旱预测方面需要更多科学知识；因此，这是一个在发展中国家描述该现象的机会。
