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 ·
A novel wind speed forecasting (WSF) framework is proposed combining Wavelet Packet Decomposition (WPD), Seasonal Adjustment Method (SAM), and Bidirectional Long Short-term Memory (BiLSTM). The SAM method eliminates the seasonal component of the decomposed subseries generated by WPD to reduce forecasting complexity. The model was tested on five years of hourly wind speed observations acquired from the Dumat Al-Jandal wind farm in Al-Jouf, Saudi Arabia, achieving high forecasting accuracy.
arXiv ·
This paper proposes a machine learning method for early detection and classification of date fruit diseases, which are economically important to countries like Saudi Arabia. The method uses a hybrid feature extraction approach combining L*a*b color features, statistical features, and Discrete Wavelet Transform (DWT) texture features. Experiments using a dataset of 871 images achieved the highest average accuracy using Random Forest (RF), Multilayer Perceptron (MLP), Naïve Bayes (NB), and Fuzzy Decision Trees (FDT) classifiers.
arXiv ·
Researchers developed a two-stage AI pipeline to predict desalination performance efficiency losses due to climate factors in the UAE, achieving 98% accuracy. The model forecasts aerosol optical depth (AOD) and uses it to predict desalination efficiency, incorporating meteorological data. A dust-aware control logic was developed to optimize plant operations, and an interactive dashboard was created for decision support.