首页|New Machine Learning Study Findings Have Been Reported by Researchers at Univers ity of Tabriz (Precipitation Modeling Based on Spatio-Temporal Variation in Lake Urmia Basin Using Machine Learning Methods)
New Machine Learning Study Findings Have Been Reported by Researchers at Univers ity of Tabriz (Precipitation Modeling Based on Spatio-Temporal Variation in Lake Urmia Basin Using Machine Learning Methods)
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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 from Tabriz, Iran, by NewsR x journalists, research stated, "The amount of rainfall in different regions is influenced by various factors, including time, place, climate, and geography." Our news journalists obtained a quote from the research from University of Tabri z: "In the Lake Urmia basin, Mediterranean air masses significantly impact preci pitation. This study aimed to model precipitation in the Lake Urmia basin using monthly rainfall data from 16 meteorological stations and five machine learning methods (RF, M5, SVR, GPR, and KNN). Eight input scenarios were considered, incl uding the monthly index, longitude, latitude, altitude, distance from stations t o Lake Urmia, and distance from the Mediterranean Sea. The results revealed that the random forest model consistently outperformed the other models, with a corr elation rate of 0.968 and the lowest errors (RMSE = 5.66 mm and MAE = 4.03 mm). This indicates its high accuracy in modeling precipitation in this basin. This s tudy's significant contribution is its ability to accurately model monthly preci pitation using spatial variables and monthly indexes without measuring precipita tion."
University of TabrizTabrizIranAsiaCyborgsEmerging TechnologiesMachine Learning
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