Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia
Hazards of sandstorm are increasingly recognized and valued by the general public, scientific researchers, and even government decision-making bodies. This paper proposed an efficient sandstorm prediction method that considered both the effect of atmospheric movement and ground factors on sandstorm...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2019/5176576 |
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| author | Li Tiancheng Ren Qing-dao-er-ji Qiu Ying |
| author_facet | Li Tiancheng Ren Qing-dao-er-ji Qiu Ying |
| author_sort | Li Tiancheng |
| collection | DOAJ |
| description | Hazards of sandstorm are increasingly recognized and valued by the general public, scientific researchers, and even government decision-making bodies. This paper proposed an efficient sandstorm prediction method that considered both the effect of atmospheric movement and ground factors on sandstorm occurrence, called improved naive Bayesian-CNN classification algorithm (INB-CNN classification algorithm). Firstly, we established a sandstorm prediction model based on the convolutional neural network algorithm, which considered atmospheric movement factors. Convolutional neural network (CNN) is a deep neural network with convolution structure, which can automatically learn features from massive data. Then, we established a sandstorm prediction model based on the Naive Bayesian algorithm, which considered ground factors. Finally, we established a sandstorm prediction model based on the improved naive Bayesian-CNN classification algorithm. Experimental results showed that the prediction accuracy of the sandstorm prediction model based on INB-CNN classification algorithm is higher than that of others and the model can better reflect the law of sandstorm occurrence. This paper used two algorithms, naive Bayesian algorithm and CNN algorithm, to identify and diagnose the strength of sandstorm in Inner Mongolia and found that combining the two algorithms, INB-CNN classification algorithm had the greatest success in predicting the occurrence of sandstorms. |
| format | Article |
| id | doaj-art-04e5610a494f474892637a45d84f49a5 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-04e5610a494f474892637a45d84f49a52025-02-03T01:11:02ZengWileyAdvances in Meteorology1687-93091687-93172019-01-01201910.1155/2019/51765765176576Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner MongoliaLi Tiancheng0Ren Qing-dao-er-ji1Qiu Ying2School of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, ChinaSchool of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, ChinaSchool of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, ChinaHazards of sandstorm are increasingly recognized and valued by the general public, scientific researchers, and even government decision-making bodies. This paper proposed an efficient sandstorm prediction method that considered both the effect of atmospheric movement and ground factors on sandstorm occurrence, called improved naive Bayesian-CNN classification algorithm (INB-CNN classification algorithm). Firstly, we established a sandstorm prediction model based on the convolutional neural network algorithm, which considered atmospheric movement factors. Convolutional neural network (CNN) is a deep neural network with convolution structure, which can automatically learn features from massive data. Then, we established a sandstorm prediction model based on the Naive Bayesian algorithm, which considered ground factors. Finally, we established a sandstorm prediction model based on the improved naive Bayesian-CNN classification algorithm. Experimental results showed that the prediction accuracy of the sandstorm prediction model based on INB-CNN classification algorithm is higher than that of others and the model can better reflect the law of sandstorm occurrence. This paper used two algorithms, naive Bayesian algorithm and CNN algorithm, to identify and diagnose the strength of sandstorm in Inner Mongolia and found that combining the two algorithms, INB-CNN classification algorithm had the greatest success in predicting the occurrence of sandstorms.http://dx.doi.org/10.1155/2019/5176576 |
| spellingShingle | Li Tiancheng Ren Qing-dao-er-ji Qiu Ying Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia Advances in Meteorology |
| title | Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia |
| title_full | Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia |
| title_fullStr | Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia |
| title_full_unstemmed | Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia |
| title_short | Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia |
| title_sort | application of improved naive bayesian cnn classification algorithm in sandstorm prediction in inner mongolia |
| url | http://dx.doi.org/10.1155/2019/5176576 |
| work_keys_str_mv | AT litiancheng applicationofimprovednaivebayesiancnnclassificationalgorithminsandstormpredictionininnermongolia AT renqingdaoerji applicationofimprovednaivebayesiancnnclassificationalgorithminsandstormpredictionininnermongolia AT qiuying applicationofimprovednaivebayesiancnnclassificationalgorithminsandstormpredictionininnermongolia |