Recurrence Based Similarity Identification of Climate Data
Climate change has become a challenging and emerging research problem in many research related areas. One of the key parameters in analyzing climate change is to analyze temperature variations in different regions. The temperature variation in a region is periodic within the interval. Temperature va...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2017/7836720 |
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| _version_ | 1849403300793810944 |
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| author | Anita Bai Swati Hira S. Deshpande Parag |
| author_facet | Anita Bai Swati Hira S. Deshpande Parag |
| author_sort | Anita Bai |
| collection | DOAJ |
| description | Climate change has become a challenging and emerging research problem in many research related areas. One of the key parameters in analyzing climate change is to analyze temperature variations in different regions. The temperature variation in a region is periodic within the interval. Temperature variations, though periodic in nature, may vary from one region to another and such variations are mainly dependent on the location and altitude of the region and also on other factors like the nearness of sea and vegetation. In this paper, we analyze such periodic variations using recurrence plot (RP), cross recurrence plot (CRP), recurrence rate (RR), and correlation of probability of recurrence (CPR) methods to find similarities of periodic variations between and within climatic regions and to identify their connectivity trend. First, we test the correctness of our method by applying it on voice and heart rate data and then experimentation is performed on synthetic climate data of nine regions in the United States and eight regions in China. Finally, the accuracy of our approach is validated on both real and synthetic datasets and demonstrated using ANOVA, Kruskal–Wallis, and z-statistics significance tests. |
| format | Article |
| id | doaj-art-93f696fc3c744f2c8d65977308c33b45 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-93f696fc3c744f2c8d65977308c33b452025-08-20T03:37:19ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2017-01-01201710.1155/2017/78367207836720Recurrence Based Similarity Identification of Climate DataAnita Bai0Swati Hira1S. Deshpande Parag2Department of Computer Science & Engineering, VNIT, Nagpur, IndiaDepartment of Computer Science & Engineering, RCOEM, Nagpur, IndiaDepartment of Computer Science & Engineering, VNIT, Nagpur, IndiaClimate change has become a challenging and emerging research problem in many research related areas. One of the key parameters in analyzing climate change is to analyze temperature variations in different regions. The temperature variation in a region is periodic within the interval. Temperature variations, though periodic in nature, may vary from one region to another and such variations are mainly dependent on the location and altitude of the region and also on other factors like the nearness of sea and vegetation. In this paper, we analyze such periodic variations using recurrence plot (RP), cross recurrence plot (CRP), recurrence rate (RR), and correlation of probability of recurrence (CPR) methods to find similarities of periodic variations between and within climatic regions and to identify their connectivity trend. First, we test the correctness of our method by applying it on voice and heart rate data and then experimentation is performed on synthetic climate data of nine regions in the United States and eight regions in China. Finally, the accuracy of our approach is validated on both real and synthetic datasets and demonstrated using ANOVA, Kruskal–Wallis, and z-statistics significance tests.http://dx.doi.org/10.1155/2017/7836720 |
| spellingShingle | Anita Bai Swati Hira S. Deshpande Parag Recurrence Based Similarity Identification of Climate Data Discrete Dynamics in Nature and Society |
| title | Recurrence Based Similarity Identification of Climate Data |
| title_full | Recurrence Based Similarity Identification of Climate Data |
| title_fullStr | Recurrence Based Similarity Identification of Climate Data |
| title_full_unstemmed | Recurrence Based Similarity Identification of Climate Data |
| title_short | Recurrence Based Similarity Identification of Climate Data |
| title_sort | recurrence based similarity identification of climate data |
| url | http://dx.doi.org/10.1155/2017/7836720 |
| work_keys_str_mv | AT anitabai recurrencebasedsimilarityidentificationofclimatedata AT swatihira recurrencebasedsimilarityidentificationofclimatedata AT sdeshpandeparag recurrencebasedsimilarityidentificationofclimatedata |