Diagnosing and Predicting the Earth’s Health via Ecological Network Analysis
Ecological balance is one of the most attractive topics in biological, environmental, earth sciences, and so on. However, due to the complexity of ecosystems, it is not easy to find a perfect way to conclusively explain all the potential impacts. In this paper, by considering several important eleme...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2013/741318 |
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| _version_ | 1832565391044378624 |
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| author | Zi-Ke Zhang Ye Sun Chu-Xu Zhang Kuan Fang Xiang Xu Chuang Liu Xueqi Wang Kui Zhang |
| author_facet | Zi-Ke Zhang Ye Sun Chu-Xu Zhang Kuan Fang Xiang Xu Chuang Liu Xueqi Wang Kui Zhang |
| author_sort | Zi-Ke Zhang |
| collection | DOAJ |
| description | Ecological balance is one of the most attractive topics in biological, environmental, earth sciences, and so on. However, due to the complexity of ecosystems, it is not easy to find a perfect way to conclusively explain all the potential impacts. In this paper, by considering several important elements, we seek to build a dynamic network model to predict the Earth’s health, trying to identify and explain how the human behavior and policies affect the model results. We firstly empirically analyze both the topological properties and time-dependent features of nodes and propose an Earth’s health index based on Shannon Entropy. Secondly, we identify the importance of each element by a machine learning approach. Thirdly, we use a spreading model to predict the Earth’s health. Finally, we integrate the topological property and the proposed health index to identify the influential nodes in the observed ecological network. Experimental results show that the oceans are the key nodes in affecting the Earth’s health, and Big countries are also important nodes in influencing the Earth’s health. In addition, the results suggest a possible solution that returning more living lands might be an effective way to solve the dilemma of ecological balance. |
| format | Article |
| id | doaj-art-719719e1935f4b0f94f18981a423fd06 |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-719719e1935f4b0f94f18981a423fd062025-02-03T01:08:00ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2013-01-01201310.1155/2013/741318741318Diagnosing and Predicting the Earth’s Health via Ecological Network AnalysisZi-Ke Zhang0Ye Sun1Chu-Xu Zhang2Kuan Fang3Xiang Xu4Chuang Liu5Xueqi Wang6Kui Zhang7Institute of Information Economy, Hangzhou Normal University, Hangzhou 310036, ChinaInstitute of Information Economy, Hangzhou Normal University, Hangzhou 310036, ChinaInstitute of Information Economy, Hangzhou Normal University, Hangzhou 310036, ChinaWeb Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, ChinaInstitute of Information Economy, Hangzhou Normal University, Hangzhou 310036, ChinaInstitute of Information Economy, Hangzhou Normal University, Hangzhou 310036, ChinaDivision of Translational Medicine, Shanghai Changzheng Hospital, Second Military Medical University, Shanghai 200003, ChinaCollege of Communication Engineering, Chongqing University, Chongqing 400044, ChinaEcological balance is one of the most attractive topics in biological, environmental, earth sciences, and so on. However, due to the complexity of ecosystems, it is not easy to find a perfect way to conclusively explain all the potential impacts. In this paper, by considering several important elements, we seek to build a dynamic network model to predict the Earth’s health, trying to identify and explain how the human behavior and policies affect the model results. We firstly empirically analyze both the topological properties and time-dependent features of nodes and propose an Earth’s health index based on Shannon Entropy. Secondly, we identify the importance of each element by a machine learning approach. Thirdly, we use a spreading model to predict the Earth’s health. Finally, we integrate the topological property and the proposed health index to identify the influential nodes in the observed ecological network. Experimental results show that the oceans are the key nodes in affecting the Earth’s health, and Big countries are also important nodes in influencing the Earth’s health. In addition, the results suggest a possible solution that returning more living lands might be an effective way to solve the dilemma of ecological balance.http://dx.doi.org/10.1155/2013/741318 |
| spellingShingle | Zi-Ke Zhang Ye Sun Chu-Xu Zhang Kuan Fang Xiang Xu Chuang Liu Xueqi Wang Kui Zhang Diagnosing and Predicting the Earth’s Health via Ecological Network Analysis Discrete Dynamics in Nature and Society |
| title | Diagnosing and Predicting the Earth’s Health via Ecological Network Analysis |
| title_full | Diagnosing and Predicting the Earth’s Health via Ecological Network Analysis |
| title_fullStr | Diagnosing and Predicting the Earth’s Health via Ecological Network Analysis |
| title_full_unstemmed | Diagnosing and Predicting the Earth’s Health via Ecological Network Analysis |
| title_short | Diagnosing and Predicting the Earth’s Health via Ecological Network Analysis |
| title_sort | diagnosing and predicting the earth s health via ecological network analysis |
| url | http://dx.doi.org/10.1155/2013/741318 |
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