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UNESCO-IHE Institute for Water Education, Delft (2021)

Machine learning methods for characterising and tracking spatiotemporal drought events : Case study : Central America Dry Corridor

Sánchez Hernández, Karel Aldrin

Titre : Machine learning methods for characterising and tracking spatiotemporal drought events : Case study : Central America Dry Corridor

Auteur : Sánchez Hernández, Karel Aldrin

Université de soutenance : UNESCO-IHE Institute for Water Education, Delft

Grade : Master of Science (MS) 2021

Résumé partiel
Drought is a natural, erratic phenomenon that has a widespread and significant impact on socioeconomic and environmental development. The early monitoring and evaluation of drought through forecasting models would allow the articulation of early control and mitigation strategies, thus achieving an optimal development in planning and preparation for climate change. Therefore, this research developed a methodology for spatiotemporal analysis of drought patterns using automatic learning tools in the dry corridor of Central America. To this end, some specific milestones were defined. These include : (i) To assess temporal and spatial meteorological and agricultural droughts events, (ii) Identifying and validate results of the spatiotemporal events using computer vision techniques and finally (iii) Implementing machine learning drought forecasting models. ERA 5 monthly land average dataset was used as input for index estimation, spatiotemporal analysis and forecasting models. The frequency of drought events was calculated using standardized SPI and SPEI indices for accumulation periods of 1,3,6,9. However, 3,6 allowed a more realistic analysis of the seasonal change conditions in the hydrological regime of the area and the identification of the existing teleconnection between drought events and scale propagation. Regarding the spatiotemporal dynamics, 97 drought events of greater extension were identified, which are generally originated in countries such as Guatemala, Nicaragua, and El Salvador between seasonal periods not longer than 7 months. Additionally, the suitability of automatic learning models such as SVR, ANN and deep learning such as LSTM for index forecasting (r2=0.80) and drought dynamics in a temporal window of 1 to 6 months ahead was verified with considerable performance.

Sujets  : machine learning drought Central America Dry Corridor spatiotemporal analysis forecasting models mitigation strategies

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Page publiée le 8 novembre 2022