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| Content Provider | frontiers |
|---|---|
| Author | Ma, Yi Pei, Zhipeng Ma, Yutang Wu, Bo Zhai, Shailei Luo, Jiqiang Kong, Peng |
| Abstract | In recent years, the frequent fouling accidents have posed a serious threat to people’s life and property safety. Owing to the wide distribution of pollution sources and variable meteorological factors, it is a very time-consuming and labor-intensive task to map the pollution distribution using traditional methods. In this work, a study on the mapping of pollution distribution based on satellite remote sensing is carried out in Yunnan Province, China, as an example. Several machine learning methods (e.g. K-nearest neighbor, support vector machine) are used to analyze the effects of conditions such as multiple air pollution and meteorological data on pollution distribution map levels. The results indicate that the ensemble learning model has the highest accuracy of 72.32% in this application. The new pollution distribution map using this classifier has 5,506 more pixels in the most severe pollution level than the traditional map. Last, the remote sensing-based map and the manual measurement-based map were combined with corresponding experience weight to obtain a weighted pollution distribution map. |
| ISSN | 2296665X |
| DOI | 10.3389/fenvs.2022.938806 |
| Volume Number | 10 |
| Journal | Frontiers in Environmental Science |
| Language | English |
| Publisher Date | 2022-07-22 |
| Access Restriction | Open |
| Subject Keyword | Machine learning Environmental Pollution Remote sensing Pollution distribution map Fusion Electric power system |
| Content Type | Text |
| Resource Type | Article |
| Subject | Environmental Science |
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