首页|Studies from Yunnan University Have Provided New Data on Machine Learning (Asses sing the destabilization risk of ecosystems dominated by carbon sequestration ba sed on interpretable machine learning method)
Studies from Yunnan University Have Provided New Data on Machine Learning (Asses sing the destabilization risk of ecosystems dominated by carbon sequestration ba sed on interpretable machine learning method)
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2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news originating from Kunming, People's Republic of China, by NewsRx editors, the research stated, "Increasing carbon sequestrati on (CS) in soils and biomass is an important land-based solution in mitigating g lobal warming." Funders for this research include Chinese Academy of Sciences. The news journalists obtained a quote from the research from Yunnan University: "Ecosystems provide a wide range of ecosystem services (ESs). The necessity to a ugment CS may engender alterations in the interrelationships among ESs, thereby heightening the probability of ecosystem destabilization. This study developed a framework that integrates machine learning and interpretable predictions to eva luate the destabilization risk resulting from alterations in ecosystem service r elationships dominated by CS. We selected Northeastern China as study area to es timate six ESs and identified areas of destabilization risk among the three serv ices most relevant to CS, including food production (FP), soil retention (SR), a nd habitat quality (HQ). Subsequently, we compared three machine learning models (random forest, extreme gradient boosting, and support vector machine) and intr oduced the Shapley additive interpretation (SHAP) method for driving mechanism a nalysis. The results showed that: (1) CS-FP had 30.28% of its area at destabilization risk and is the most significant ecosystem service pair; (2) Heilongjiang Province was the region with the highest destabilization risk of C S, with CS-FP and CS-SR accounting for 44.76% and 52.89% of all regions, respectively; (3) a non-linear relationship and the presence of threshold features between socio-ecological factors and the prediction of destab ilization risk."
Yunnan UniversityKunmingPeople's Rep ublic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
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