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North China University of Water Resources and Electric Power (2021)

Study of Meteorological Drought Spatiotemporal Forecast Methods in China Based on Machine Learning

张棋

Titre : Study of Meteorological Drought Spatiotemporal Forecast Methods in China Based on Machine Learning

Auteur : 张棋

Grade : Master 2021

Université : North China University of Water Resources and Electric Power

Résumé partiel
As one of the most destructive disasters in nature,drought can cause great damage to agricultural production,socio-economic development,and human life safety.In the context of intensifying global climate change,the evolution pattern and prediction of meteorological droughts have become a hot spot and difficult issue in the field of climate change.It is of great theoretical and practical significance to clarify the evolution pattern,spatial and temporal distribution characteristics,and future development trend of droughts,especially for arid and semi-arid regions with water shortage,for the reasonable regional water resources allocation and prevention and mitigation of droughts.Although a large number of studies have focused on drought prediction,most of them have been conducted separately in two dimensions,spatial and temporal,while the characteristics and changes of drought are analyzed from the overall spatial and temporal scales in the future.Therefore,this study takes China as the study area,used the station and grid-point data continuously observed in the past 40 years to calculate several meteorological drought indices and comprehensively analyzes the evolution pattern of meteorological drought,quantitatively analyzes the applicability of different time series prediction algorithms to the drought indices,and deeply reveals the characteristics of the spatial and temporal distribution of meteorological drought in seven sub-regions of China,to provide an important theoretical basis for future drought warning.The

Mots clés : Meteorological drought ;Machine Learning ;Time series forecast ;Spatiotemporal pattern mining ;

Présentation (CNKI)

Page publiée le 9 mars 2022