The Prediction Algorithm and Characteristics Analysis of Kuroshio Sea Surface Temperature Anomalies
Based on 130 climate signal indexes provided by National Climate Center of China, this paper established a decision tree diagnostic prediction model for Spring Kuroshio Sea Surface Temperature (SST) from 1961 to 2015 (65 years) by using Chi-Squared Automatic Interaction Detector (CHAID) algorithm in...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2022/7236527 |
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| _version_ | 1832562311590576128 |
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| author | Dawei Shi Chao Li Zhu Zhu Runqing Lv Shengjie Chen Yunfeng Zhu |
| author_facet | Dawei Shi Chao Li Zhu Zhu Runqing Lv Shengjie Chen Yunfeng Zhu |
| author_sort | Dawei Shi |
| collection | DOAJ |
| description | Based on 130 climate signal indexes provided by National Climate Center of China, this paper established a decision tree diagnostic prediction model for Spring Kuroshio Sea Surface Temperature (SST) from 1961 to 2015 (65 years) by using Chi-Squared Automatic Interaction Detector (CHAID) algorithm in data mining and obtained five rule sets to determine whether Spring Kuroshio SST is high or not. Considering the data of the 44 years from 1961 to 2004 as the training set of the model and the other years as the test set, the training accuracy of the model can reach to 95.45% and the test accuracy can reach to 81.82%. Three types of Spring Kuroshio SST are different in intensity and distribution. The results show that the prediction model of Spring Kuroshio SST based on CHAID algorithm has a high prediction accuracy, with the reasonable and effective model and the well-thought-out decision rules. Moreover, based on the results of decision classification, the SST anomalies correspond to different distribution characteristics of summer daily precipitation anomalies in eastern China, which can provide a new idea and method for climate prediction of regional summer precipitation. |
| format | Article |
| id | doaj-art-261fc4dc236d422187789784e3804a37 |
| institution | Kabale University |
| issn | 1687-9317 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-261fc4dc236d422187789784e3804a372025-02-03T01:22:57ZengWileyAdvances in Meteorology1687-93172022-01-01202210.1155/2022/7236527The Prediction Algorithm and Characteristics Analysis of Kuroshio Sea Surface Temperature AnomaliesDawei Shi0Chao Li1Zhu Zhu2Runqing Lv3Shengjie Chen4Yunfeng Zhu5Lianyungang Meteorological BureauKey Laboratory of Traffic MeteorologyAnhui Meteorological ObservatoryJiangsu Meteorological ObservatoryJiangsu Meteorological ObservatoryLianyungang Meteorological BureauBased on 130 climate signal indexes provided by National Climate Center of China, this paper established a decision tree diagnostic prediction model for Spring Kuroshio Sea Surface Temperature (SST) from 1961 to 2015 (65 years) by using Chi-Squared Automatic Interaction Detector (CHAID) algorithm in data mining and obtained five rule sets to determine whether Spring Kuroshio SST is high or not. Considering the data of the 44 years from 1961 to 2004 as the training set of the model and the other years as the test set, the training accuracy of the model can reach to 95.45% and the test accuracy can reach to 81.82%. Three types of Spring Kuroshio SST are different in intensity and distribution. The results show that the prediction model of Spring Kuroshio SST based on CHAID algorithm has a high prediction accuracy, with the reasonable and effective model and the well-thought-out decision rules. Moreover, based on the results of decision classification, the SST anomalies correspond to different distribution characteristics of summer daily precipitation anomalies in eastern China, which can provide a new idea and method for climate prediction of regional summer precipitation.http://dx.doi.org/10.1155/2022/7236527 |
| spellingShingle | Dawei Shi Chao Li Zhu Zhu Runqing Lv Shengjie Chen Yunfeng Zhu The Prediction Algorithm and Characteristics Analysis of Kuroshio Sea Surface Temperature Anomalies Advances in Meteorology |
| title | The Prediction Algorithm and Characteristics Analysis of Kuroshio Sea Surface Temperature Anomalies |
| title_full | The Prediction Algorithm and Characteristics Analysis of Kuroshio Sea Surface Temperature Anomalies |
| title_fullStr | The Prediction Algorithm and Characteristics Analysis of Kuroshio Sea Surface Temperature Anomalies |
| title_full_unstemmed | The Prediction Algorithm and Characteristics Analysis of Kuroshio Sea Surface Temperature Anomalies |
| title_short | The Prediction Algorithm and Characteristics Analysis of Kuroshio Sea Surface Temperature Anomalies |
| title_sort | prediction algorithm and characteristics analysis of kuroshio sea surface temperature anomalies |
| url | http://dx.doi.org/10.1155/2022/7236527 |
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