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Employing Earth Observations And Artificial Intelligence To Address Key Global Environmental Challenges In Service Of The Sdgs, Wenzhao Li
Computational and Data Sciences (PhD) Dissertations
Earth Observation (EO) data provides the capability to integrate data from multiple sources and helps to produce more relevant, frequent, and accurate information about complex processes. EO, empowered by methodologies from Artificial Intelligence (AI), supports various aspects of the UN’s Sustainable Development Goals (SDGs). This dissertation presents author’s major studies using EO to fill in knowledge gaps and develop methodologies and cloud-based applications in selected SDGs, including SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), SDG 14 (Life below Water) and SDG 15 (Life on Land). For SDG 6, the study focuses on spatiotemporal water recharge …
Aerosols Size Distribution Characteristics And Role Of Precipitation During Dust Storm Formation Over Saudi Arabia, Ashraf Farahat, Hesham El-Askary, A. Umran Dogan
Aerosols Size Distribution Characteristics And Role Of Precipitation During Dust Storm Formation Over Saudi Arabia, Ashraf Farahat, Hesham El-Askary, A. Umran Dogan
Mathematics, Physics, and Computer Science Faculty Articles and Research
Kingdom of Saudi Arabia and the Gulf region are frequently exposed to major dust storms and anthropogenic emissions from rapidly growing industrial activities that affect aerosols optical and physical characteristics. This paper integrates observations from space-borne sensors namely MODIS and CALIPSO, together with AERONET ground observations to examine eight years aerosols characteristics during the (March–May) season of 2003 to 2010 over Saudi Arabia. Aerosol analysis from the interdependent data assessment show comparable aerosols characteristics over the eight year period with higher aerosols mean optical depths over enhanced dust load region, (46–50°E, 25–29°N), during March–May of 2009 and 2010. The mean …