首页|Reports Outline Machine Learning Study Results from Chengdu University of Inform ation Technology (Change in Fractional Vegetation Cover and Its Prediction durin g the Growing Season Based on Machine Learning in Southwest China)
Reports Outline Machine Learning Study Results from Chengdu University of Inform ation Technology (Change in Fractional Vegetation Cover and Its Prediction durin g the Growing Season Based on Machine Learning in Southwest China)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on artificial intell igence are discussed in a new report. According to news originating from Chengdu , People's Republic of China, by NewsRx correspondents, research stated, "Fracti onal vegetation cover (FVC) is a crucial indicator for measuring the growth of s urface vegetation. The changes and predictions of FVC significantly impact biodi versity conservation, ecosystem health and stability, and climate change respons e and prediction." Our news editors obtained a quote from the research from Chengdu University of I nformation Technology: "Southwest China (SWC) is characterized by complex topogr aphy, diverse climate types, and rich vegetation types. This study first analyze d the spatiotemporal variation of FVC at various timescales in SWC from 2000 to 2020 using FVC values derived from pixel dichotomy model. Next, we constructed f our machine learning models-light gradient boosting machine (LightGBM), support vector regression (SVR), * * k* * -nearest neighbor (KNN), and ridge regression (RR)-along with a weighted average heterogeneous ensemble model (WAHEM) to predi ct growing-season FVC in SWC from 2000 to 2023. Finally, the performance of the different ML models was comprehensively evaluated using tenfold cross-validation and multiple performance metrics. The results indicated that the overall FVC in SWC predominantly increased from 2000 to 2020. Over the 21 years, the FVC spati al distribution in SWC generally showed a high east and low west pattern, with e xtremely low FVC in the western plateau of Tibet and higher FVC in parts of east ern Sichuan, Chongqing, Guizhou, and Yunnan. The determination coefficient * * R * * 2 scores from tenfold cross-validation for the four ML models indicated that Ligh tGBM had the strongest predictive ability whereas RR had the weakest. WAHEM and LightGBM models performed the best overall in the training, validation, and test sets, with RR performing the worst. The predicted spatial change trends were co nsistent with the MODIS-MOD13A3-FVC and FY3D-MERSI-FVC, although the predicted F VC values were slightly higher but closer to the MODIS-MOD13A3-FVC. The feature importance scores from the LightGBM model indicated that digital elevation model (DEM) had the most significant influence on FVC among the six input features. I n contrast, soil surface water retention capacity (SSWRC) was the most influenti al climate factor."
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