首页|New Findings in Machine Learning Described from King Saud University (Machine Le arning Forecast of Dust Storm Frequency in Saudi Arabia Using Multiple Features)
New Findings in Machine Learning Described from King Saud University (Machine Le arning Forecast of Dust Storm Frequency in Saudi Arabia Using Multiple Features)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting originating from Riyadh, Sa udi Arabia, by NewsRx correspondents, research stated, “Dust storms are signific ant atmospheric events that impact air quality, public health, and visibility, e specially in arid Saudi Arabia.” The news editors obtained a quote from the research from King Saud University: “ This study aimed to develop dust storm frequency predictions for Riyadh, Jeddah, and Dammam by integrating meteorological and environmental variables. Our model s include multiple linear regression, support vector machine, gradient boosting regression tree, long short-term memory (LSTM), and temporal convolutional netwo rk (TCN). This study highlights the effectiveness of LSTM and TCN models in capt uring the complex temporal dynamics of dust storms and demonstrates that they ou tperform traditional methods, as evidenced by their lower mean absolute error (M AE) and root mean square error (RMSE) values and higher R2 score. In Riyadh, the TCN model demonstrates its remarkable performance, with an R2 score of 0.51, an MAE of 2.80, and an RMSE of 3.48, highlighting its precision, adaptability, and responsiveness to changes in dust storm frequency. Conversely, in Dammam, the L STM model proved to be the most accurate, achieving an MAE of 3.02, RMSE of 3.64 , and R2 score of 0.64.”
King Saud University, Riyadh, Saudi Arab ia, Asia, Cyborgs, Emerging Technologies, Machine Learning
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