Geospatial modelling of drought patterns in Oman: GIS-based and machine learning approach

Drought is one of the most devastating natural disasters, and its consequences affect various human and environmental aspects. In both arid and semi-arid regions, the impact of drought poses direct threats to livelihoods and socio-economic activities. For drought mitigation purposes, spatially accur...

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Veröffentlicht in:	Modeling earth systems and environment Jg. 10; H. 3; S. 3411 - 3431
1. Verfasser:	Mansour, Shawky
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.06.2024 Springer Nature B.V
Schlagworte:	Air temperature Algorithms Arid zones Artificial neural networks Biodiversity Chemistry and Earth Sciences Climate change Climate system Computer Science Dependent variables Disasters Drought Earth and Environmental Science Earth Sciences Earth System Sciences Economic activities Ecosystems Emergency preparedness Environment Environmental aspects Environmental impact Evapotranspiration Food production Geographic information systems Geographical information systems Hydroelectric power Independent variables Information systems Livelihoods Machine learning Math. Appl. in Environmental Science Mathematical Applications in the Physical Sciences Mitigation Natural disasters Neural networks Original Article Physics Plant cover Precipitation Rainy season Relative humidity Remote sensing Semi arid areas Semiarid zones Socioeconomic aspects Statistics for Engineering Variables Vegetation cover Water management Water resources Water resources management Water supply Wet season Wind speed Oman GIS Artificial neural network Forecasting Drought risk patterns Oman
ISSN:	2363-6203, 2363-6211
Online-Zugang:	Volltext
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