首页|Reports from Yunnan University Advance Knowledge in Machine Learning (Improving the Hindcast of the Northward Shift of South Asian High In June With Machine Lea rning)
Reports from Yunnan University Advance Knowledge in Machine Learning (Improving the Hindcast of the Northward Shift of South Asian High In June With Machine Lea rning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting originating in Kunming,People's Republic of China,by NewsRx journalists,research stated,"Accurate prediction of the n orthward shift of the South Asian High (SAH) in June is crucial for improving th e flood and drought management of Asian countries during the summer. This study investigates the ability of three supervised machine learning (ML) models in pre dicting the meridional index of the SAH (SAHI) in June." Funders for this research include National Natural Science Foundation of China ( NSFC),Natural Science Foundation of Yunnan Province. The news reporters obtained a quote from the research from Yunnan University,"T he ML models include the extreme gradient boosting (XGBoost),the support vector machine (SVM),and the multi-layer perceptron (MLP) neural network. The trainin g data is derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) model data that is significantly correlated with the reference data at a 99 % confidence level. The hyperparameter optimization (HPO) is performed for each ML model using the particle swarm optimization (PSO). Six objective functions are defined for the HPO based on the conventional root-mean-square error (RMSE),the interannual variability skill score,and the temporal correlation coefficient ( TCC). The performance of optimized ML models is evaluated with the TCC and the s ame sign rate (SSR). The top two models are the PSOXGBoost model tuned with RMSE + IVS and the PSO-SVM model tuned with log(RMSE+TCC). Their stacked ensemble mo del has the TCC of 0.54 and the SSR of 72%. The average of the best model hindcasts has a higher TCC of 0.61 but a lower SSR of 67% t han the ensemble model. Further investigation suggests that the ensemble model o nly preserves the predictor-predictand relationships for two predictors. To impr ove the representation of the predictor-predictand relationship,we divided the predictors into two groups and trained the ML models separately with interannual increments of predictors in Group 1 and standardized anomalies of predictors in Group 2. The average of the best model hindcasts from the two groups have the T CC of 0.63 and the SSR of 72%. The improvement in the SAHI hindcast is associated with a more realistic predictor-predictand relationship in the ML models."
KunmingPeople's Republic of ChinaAsi aAsiaCyborgsEmerging TechnologiesMachine LearningYunnan University
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