arXiv · · Notable
This paper examines the relationship between COVID-19 spread and weather patterns across 89 cities in Saudi Arabia using machine learning. The study uses daily COVID-19 case reports from the Saudi Ministry of Health and historical weather data. The results indicate that temperature and wind speed have the strongest correlation with the spread of COVID-19, with a random forest model achieving the best performance.
COVID-19 · machine learning · weather · Saudi Arabia · random forest
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arXiv ·
This paper proposes a smart dome system for mosques that uses machine learning to automatically control dome ventilation based on weather conditions and outside temperatures. The system was tested on the Prophet Mosque in Saudi Arabia using K-Nearest Neighbors and Decision Tree algorithms. The Decision Tree algorithm achieved a higher accuracy of 98% compared to 95% for the k-NN algorithm.
arXiv ·
This study investigates the correlation between Google Trends data for COVID-19 symptoms and the actual number of COVID-19 cases in Saudi Arabia between March and October 2020. The researchers found that searches for "cough" and "sore throat" were most frequent, while "loss of smell", "loss of taste", and "diarrhea" showed the highest correlation with confirmed cases. The study concludes that Google searches can serve as a supplementary surveillance tool for monitoring the spread of COVID-19 in Saudi Arabia. Why it matters: The research demonstrates the potential of using readily available digital data to augment traditional surveillance methods for public health monitoring in the region.
KAUST ·
KAUST and K.A.CARE have partnered to study solar irradiation and atmospheric weather conditions in Saudi Arabia, leveraging K.A.CARE's Renewable Resources Atlas Project. The collaboration uses KAUST's Shaheen II supercomputer to simulate weather and atmospheric conditions from 2005-2018. The long-term goal is daily forecasting of weather and air quality across the Arabian Peninsula. Why it matters: This initiative will provide crucial data for renewable energy development and environmental monitoring in the region, supporting Saudi Arabia's sustainability goals.