首页|Reports from University of California Davis Advance Knowledge in Machine Learnin g (Solaret: a Generalizable Machine Learning Approach To Estimate Reference Evap otranspiration From Solar Radiation)
Reports from University of California Davis Advance Knowledge in Machine Learnin g (Solaret: a Generalizable Machine Learning Approach To Estimate Reference Evap otranspiration From Solar Radiation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news reporting from Davis, California, by NewsRx journalists, research stated, “Irrigation is the most significant consume r of freshwater worldwide. Deciding on the right amount of irrigation is crucial for sustainable water management and food production.” The news correspondents obtained a quote from the research from the University o f California Davis, “The Penman-Monteith (P-M) reference crop evapotranspiration (ETO) is the gold standard in irrigation management and scheduling; however, it s calculation requires measurements from multiple sensors over an extended refer ence grass surface. The cost of land, sensors, maintenance, and water to keep th e grass surface green impedes having a dense network of ETO stations. To solve t his challenge, this research aims to develop an input-limited ETO estimation app roach based on historical weather data and machine learning (ML) algorithms to r elax the need for a reference grass surface. This approach, called ‘SolarET,’ ta kes solar radiation (RS) data as its sole input. RS is the only meteorological d riving factor of ETO that does not rely on the measuring surface. To test the ge neralizability of SolarET, we test its performance over unseen arbitrary locatio ns across California. California is chosen as the case study since it is one of the world’s most hydrologically altered and agriculturally productive regions. I n total, 19,088,736 hourly data samples from 131 automated weather stations have been used in this study. The ML models have been trained over 114 stations and tested over 17 unseen stations, each representing a California climatic zone. Ou r findings point to the superiority of decision tree-based algorithms versus neu ral networks. SolarET works best in irrigation-oriented regions of California (e .g., Central Valley) and is less accurate in coastal and desert zones.”
DavisCaliforniaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversit y of California Davis
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