首页|Researcher from Beijing Forestry University Reports Recent Findings in Machine L earning (Establishment of a Reference Evapotranspiration Forecasting Model Based on Machine Learning)

Researcher from Beijing Forestry University Reports Recent Findings in Machine L earning (Establishment of a Reference Evapotranspiration Forecasting Model Based on Machine Learning)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news reporting from Beijing, People’s Republic o f China, by NewsRx journalists, research stated, “Water scarcity is a global pro blem. Deficit irrigation (DI) reduces evapotranspiration, improving water effici ency in agriculture.” Our news journalists obtained a quote from the research from Beijing Forestry Un iversity: “Reference evapotranspiration (ET0) is an important factor in determin ing DI. ET0 forecasting predicts field water consumption and enables proactive i rrigation decisions, offering guidance for water resource management. However, i mplementation of ET0 forecasting faces challenges due to complex calculations an d extensive meteorological data requirements. This project aims to develop a mac hine learning system for ET0 forecasting. The project involves studying ET0 meth ods and identifying required meteorological parameters. Historical meteorologica l data and weather forecasts were obtained from meteorological websites and anal yzed for accuracy after preprocessing. A machine learning-based model was create d to forecast reference crop evapotranspiration. The model’s input parameters we re selected through path analysis before it was optimized using Bayesian optimiz ation to reduce overfitting and improve accuracy. Three forecasting models were developed: one based on historical meteorological data, one based on weather for ecasts, and one that corrects the weather forecasts.”

Beijing Forestry UniversityBeijingPe ople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(MAY.22)