首页|New Research on Machine Learning from Sichuan University Summarized (Machine Lea rning-Based Estimation of Daily Cropland Evapotranspiration in Diverse Climate Z ones)
New Research on Machine Learning from Sichuan University Summarized (Machine Lea rning-Based Estimation of Daily Cropland Evapotranspiration in Diverse Climate Z ones)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on artificial intelligen ce have been presented. According to news reporting from Chengdu, People's Repub lic of China, by NewsRx journalists, research stated, "The accurate prediction o f cropland evapotranspiration (ET) is of utmost importance for effective irrigat ion and optimal water resource management." Financial supporters for this research include National Natural Science Foundati on of China; Fundamental Research Funds For The Central Universities; Sichuan Sc ience And Technology Program. Our news reporters obtained a quote from the research from Sichuan University: " To evaluate the feasibility and accuracy of ET estimation in various climatic co nditions using machine learning models, three-, six-, and nine-factor combinatio ns (V3, V6, and V9) were examined based on the data obtained from global croplan d eddy flux sites and Moderate Resolution Imaging Spectroradiometer (MODIS) remo te sensing data. Four machine learning models, random forest (RF), support vecto r machine (SVM), extreme gradient boosting (XGB), and backpropagation neural net work (BP), were used for this purpose. The input factors included daily mean air temperature (Ta), net radiation (Rn), soil heat flux (G), evaporative fraction (EF), leaf area index (LAI), photosynthetic photon flux density (PPFD), vapor pr essure deficit (VPD), wind speed (U), and atmospheric pressure (P). The four mac hine learning models exhibited significant simulation accuracy across various cl imate zones, reflected by their global performance indicator (GPI) values rangin g from -3.504 to 0.670 for RF, -3.522 to 1.616 for SVM, -3.704 to 0.972 for XGB, and -3.654 to 1.831 for BP. The choice of suitable models and the different inp ut factors varied across different climatic regions. Specifically, in the temper ate-continental zone (TCCZ), subtropical-Mediterranean zone (SMCZ), and temperat e zone (TCZ), the models of BPC-V9, SVMS-V6, and SVMT-V6 demonstrated the highes t simulation accuracy, with average RMSE values of 0.259, 0.373, and 0.333 mm d- 1, average MAE values of 0.177, 0.263, and 0.248 mm d-1, average R2 values of 0. 949, 0.819, and 0.917, and average NSE values of 0.926, 0.778, and 0.899, respec tively."
Sichuan UniversityChengduPeople's Re public of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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