Search

Your search keyword '"Ge, Rubing"' showing total 16 results
Start Over Author "Ge, Rubing" Publication Year Range Last 10 years
16 results on '"Ge, Rubing"'

3. Landscape Pattern Changes of Coastal Wetland in Nansha District of Guangzhou City in Recent 20 Years

Author

Sun, Caige, Zhong, Kaiwen, Ge, Rubing, Zhao, Yu, Liu, Xulong, Lin, Tao, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Bian, Fuling, editor, and Xie, Yichun, editor

Published
2016
Full Text
View/download PDF

4. Prediction of PM 2.5 Concentration Using Spatiotemporal Data with Machine Learning Models.

Author

Ma, Xin, Chen, Tengfei, Ge, Rubing, Xv, Fan, Cui, Caocao, and Li, Junpeng

Subjects
MACHINE learning, REGRESSION trees, STANDARD deviations, SUPPORT vector machines, CITIES & towns, RANDOM forest algorithms
Abstract

Among the critical global crises curbing world development and sustainability, air quality degradation has been a long-lasting and increasingly urgent one and it has been sufficiently proven to pose severe threats to human health and social welfare. A higher level of model prediction accuracy can play a fundamental role in air quality assessment and enhancing human well-being. In this paper, four types of machine learning models—random forest model, ridge regression model, support vector machine model, extremely randomized trees model—were adopted to predict PM2.5 concentration in ten cities in the Jing-Jin-Ji region of north China based on multi-sources spatiotemporal data including air quality and meteorological data in time series. Data were fed into the model by using the rolling prediction method which is proven to improve prediction accuracy in our experiments. Lastly, the comparative experiments show that at the city level, RF and ExtraTrees models have better predictive results with lower mean absolute error (MAE), root mean square error (RMSE), and higher index of agreement (IA) compared to other selected models. For seasonality, level four models all have the best prediction performances in winter time and the worst in summer time, and RF models have the best prediction performance with the IA ranging from 0.93 to 0.98 with an MAE of 5.91 to 11.68 μg/m3. Consequently, the demonstration of how each model performs differently in each city and each season is expected to shed light on environmental policy implications. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

16. Research on the relationship between air quality and economy development in major cities of China.

Author

Ma, Xin, Ge, Rubing, and Zhang, Li

Subjects
*AIR quality, *ECONOMIC development, *SUPPORT vector machines, *AIR pollutants, *ENVIRONMENTAL policy
Abstract

Purpose – The purpose of this paper is to build a support vector machine (SVM) model to evaluate the city air quality level, using the three main air pollutants selected as evaluation index. Design/methodology/approach – PM10, SO2, NO2 are the most important three air pollutants and their concentration data are selected as the influencing factor. And the SVM model is build and used to evaluate the air quality level of 29 major cities in China 2011. The cross-validation is adopted to select optimal penalty parameters and optimal kernel function, and the classification accuracies achieved under different normalization methods and kernel functions are compared in the end. Findings – The study found, the parameters and kernel functions chosen by the SVM model have influence on the model's prediction accuracy. Through continuous optimization of model parameters, finally it is found that the model performs better with [0, 1] normalization method and RBF kernel function. It proves that SVM classification model is effective in dealing with the problem of city air quality evaluation. Practical implications – The result of this study shows that the SVM classification model can be well applied to predict the city air quality level by using air pollutants concentration data as evaluation index. It can help the government and relevant department issue corresponding environmental policy and environmental protection measures. Originality/value – The qualitative and quantitative study method are combined in this paper, on the basis of predecessors’ research results, as well as careful analysis to select evaluation index. The SVM classification model build is simulated by using Matlab technique, beyond comparing the accuracy, its outcomes and its efficiency in classification are demonstrated. [ABSTRACT FROM AUTHOR]

Published
2014
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results