Research from Chinese Academy of Sciences Provides New Study Findings on Machine Learning (Potential of Sample Migration and Explainable Machine Learning Model for Monitoring Spatiotemporal Changes of Wetland Plant Communities)
Research from Chinese Academy of Sciences Provides New Study Findings on Machine Learning (Potential of Sample Migration and Explainable Machine Learning Model for Monitoring Spatiotemporal Changes of Wetland Plant Communities)
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 out of Changchun, People's Republic of China, by NewsRx editors, research stated, "The composition and dyna mics of wetland plant communities play a critical role in maintaining the functi onality of wetland ecosystems and serve as important indicators of wetland degra dation and restoration." Financial supporters for this research include National Natural Science Foundati on of China; Natural Science Foundation of Jilin Province. Our news editors obtained a quote from the research from Chinese Academy of Scie nces: "Accurately identifying wetland plant communities using remote sensing tec hniques remains challenging due to the complex environment and cloud contaminati on. Here, we applied a sample migration method based on change vector analysis a nd a random forest (RF) classifier incorporating SHapley Additive exPlanations ( SHAP) to explore the spatiotemporal changes of wetland plant communities in the western Songnen Plain of China between 2016 and 2022, and to better understand t he decision logic of the RF model. Our work achieved accurate annual wetland cla ssification at the community scale, with an average overall accuracy of 89.5% and an average kappa coefficient of 0.87. Our analysis revealed different spatio temporal change characteristics of wetland plant communities in the western Song nen Plain and three national nature reserves. The SHAP model showed that MOS_ IRECI is the most important feature determining the prediction results of the RF model, and the importance of the features differs at global and local levels. T his study confirms the feasibility of annual dynamic monitoring of wetland plant communities at a regional scale."
Key words
Chinese Academy of Sciences/Changchun/People's Republic of China/Asia/Cyborgs/Emerging Technologies/Machine Learni ng
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