Year: 2024
Type: conference-paper
Abstract: Climate change drives the environment to more extreme weather events. Increased air, land surface and canopy surface temperatures affect the industry of agriculture in different ways. Significant crop damages and losses are emerging and spreading throughout different regions, accompanied by water scarcity and imposed restrictions on farmers' water usage. The Eastern Mediterranean, Middle East, and North Africa (EMMENA) region is one of the most affected areas globally. The United States (US) developed a system for monitoring droughts in different counties and classifying them into six categories (i.e., no drought, abnormally dry, moderate drought, severe drought, extreme drought, and exceptional drought) based on the assigned drought score. To predict drought scores, Artificial Intelligence (AI) methodologies are applied to a dataset that combines meteorological variables from the NASA Langley Research Center with drought scores from the US drought monitor system. The main objective of this work is to propose a novel explainable ΑΙ technique based on unsupervised learning for drought severity predictions and raise the awareness for drought events in the wider EMMENA region.
DOI: 10.1117/12.3037320
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: bronze
Awards: ENFIELD: European Lighthouse to Manifest Trustworthy and Green AI, G3409612369, G4183413234, G434337789, G4937468798, Enhancing Earth Observation capabilities of the Eratosthenes Centre of Excellence on Disaster Risk Reduction through Artificial Intelligence, ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment
OpenAlex ID: W4402504615