首页|New Machine Learning Study Findings Have Been Reported by Researchers at Shanxi Agricultural University (Predicting Individual Tree Mortality of * * Larix gmeli nii* * var. * * Principis-rupprechtii* * in Temperate Forests Using Machine Lear ning ...)
New Machine Learning Study Findings Have Been Reported by Researchers at Shanxi Agricultural University (Predicting Individual Tree Mortality of * * Larix gmeli nii* * var. * * Principis-rupprechtii* * in Temperate Forests Using Machine Lear ning ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-A new study on artificial intelligence is now available. According to news reporting from Taiyuan, People's Republic o f China, by NewsRx journalists, research stated, "Accurate prediction of individ ual tree mortality is essential for informed decision making in forestry." Funders for this research include Shanxi Province Basic Research Program, Youth Science Research Project; Shanxi Province Key Research And Development Program; National Natural Science Foundation of China. The news editors obtained a quote from the research from Shanxi Agricultural Uni versity: "In this study, we proposed machine learning models to forecast individ ual tree mortality within the temperate * * Larix gmelinii* * var. * * principis -rupprechtii* * forests in Northern China. Eight distinct machine learning techn iques including random forest, logistic regression, artificial neural network, g eneralized additive model, support vector machine, gradient boosting machine, k- nearest neighbors, and naive Bayes models were employed, to construct an ensembl e learning model based on comprehensive dataset from this specific ecosystem. Th e random forest model emerged as the most accurate, demonstrating 92.9% accuracy and 92.8% sensitivity, making it the best model among tho se tested. We identified key variables impacting tree mortality, and the results showed that a basal area larger than the target trees (BAL), a diameter at 130 cm (DBH), a basal area (BA), an elevation, a slope, NH4-N, soil moisture, crown density, and the soil's available phosphorus are important variables in the * * Larix Principis-rupprechtii* * individual mortality model. The variable importan ce calculation results showed that BAL is the most important variable with an im portance value of 1.0 in a random forest individual tree mortality model."
Shanxi Agricultural UniversityTaiyuanPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learn ing
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