Network Modeling and Assessment of Ecosystem Health by a Multi-Population Swarm Optimized Neural Network Ensemble
Society is more and more interested in developing mathematical models to assess and forecast the environmental and biological health conditions of our planet. However, most existing models cannot determine the long-range impacts of potential policies without considering the complex global factors an...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2016-06-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2076-3417/6/6/175 |
| _version_ | 1811216722825314304 |
|---|---|
| author | Rong Shan Zeng-Shun Zhao Pan-Fei Chen Wei-Jian Liu Shu-Yi Xiao Yu-Han Hou Mao-Yong Cao Fa-Liang Chang Zhigang Wang |
| author_facet | Rong Shan Zeng-Shun Zhao Pan-Fei Chen Wei-Jian Liu Shu-Yi Xiao Yu-Han Hou Mao-Yong Cao Fa-Liang Chang Zhigang Wang |
| author_sort | Rong Shan |
| collection | DOAJ |
| description | Society is more and more interested in developing mathematical models to assess and forecast the environmental and biological health conditions of our planet. However, most existing models cannot determine the long-range impacts of potential policies without considering the complex global factors and their cross effects in biological systems. In this paper, the Markov property and Neural Network Ensemble (NNE) are utilized to construct an estimated matrix that combines the interaction of the different local factors. With such an estimation matrix, we could obtain estimated variables that could reflect the global influence. The ensemble weights are trained by multiple population algorithms. Our prediction could fit the real trend of the two predicted measures, namely Morbidity Rate and Gross Domestic Product (GDP). It could be an effective method of reflecting the relationship between input factors and predicted measures of the health of ecosystems. The method can perform a sensitivity analysis, which could help determine the critical factors that could be adjusted to move the ecosystem in a sustainable direction. |
| first_indexed | 2024-04-12T06:43:29Z |
| format | Article |
| id | doaj.art-2a065fb881c1471dbd4f1989187d2eaf |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-12T06:43:29Z |
| publishDate | 2016-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2a065fb881c1471dbd4f1989187d2eaf2022-12-22T03:43:39ZengMDPI AGApplied Sciences2076-34172016-06-016617510.3390/app6060175app6060175Network Modeling and Assessment of Ecosystem Health by a Multi-Population Swarm Optimized Neural Network EnsembleRong Shan0Zeng-Shun Zhao1Pan-Fei Chen2Wei-Jian Liu3Shu-Yi Xiao4Yu-Han Hou5Mao-Yong Cao6Fa-Liang Chang7Zhigang Wang8College of Electronics, Communication and Physics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Electronics, Communication and Physics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Electronics, Communication and Physics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Electronics, Communication and Physics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Electronics, Communication and Physics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Electronics, Communication and Physics, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Electronics, Communication and Physics, Shandong University of Science and Technology, Qingdao 266590, ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250061, ChinaKey Laboratory of Computer Vision and System, Ministry of Education, Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384, ChinaSociety is more and more interested in developing mathematical models to assess and forecast the environmental and biological health conditions of our planet. However, most existing models cannot determine the long-range impacts of potential policies without considering the complex global factors and their cross effects in biological systems. In this paper, the Markov property and Neural Network Ensemble (NNE) are utilized to construct an estimated matrix that combines the interaction of the different local factors. With such an estimation matrix, we could obtain estimated variables that could reflect the global influence. The ensemble weights are trained by multiple population algorithms. Our prediction could fit the real trend of the two predicted measures, namely Morbidity Rate and Gross Domestic Product (GDP). It could be an effective method of reflecting the relationship between input factors and predicted measures of the health of ecosystems. The method can perform a sensitivity analysis, which could help determine the critical factors that could be adjusted to move the ecosystem in a sustainable direction.http://www.mdpi.com/2076-3417/6/6/175ecosystem assessmentneural network ensembleMarkov analysis |
| spellingShingle | Rong Shan Zeng-Shun Zhao Pan-Fei Chen Wei-Jian Liu Shu-Yi Xiao Yu-Han Hou Mao-Yong Cao Fa-Liang Chang Zhigang Wang Network Modeling and Assessment of Ecosystem Health by a Multi-Population Swarm Optimized Neural Network Ensemble Applied Sciences ecosystem assessment neural network ensemble Markov analysis |
| title | Network Modeling and Assessment of Ecosystem Health by a Multi-Population Swarm Optimized Neural Network Ensemble |
| title_full | Network Modeling and Assessment of Ecosystem Health by a Multi-Population Swarm Optimized Neural Network Ensemble |
| title_fullStr | Network Modeling and Assessment of Ecosystem Health by a Multi-Population Swarm Optimized Neural Network Ensemble |
| title_full_unstemmed | Network Modeling and Assessment of Ecosystem Health by a Multi-Population Swarm Optimized Neural Network Ensemble |
| title_short | Network Modeling and Assessment of Ecosystem Health by a Multi-Population Swarm Optimized Neural Network Ensemble |
| title_sort | network modeling and assessment of ecosystem health by a multi population swarm optimized neural network ensemble |
| topic | ecosystem assessment neural network ensemble Markov analysis |
| url | http://www.mdpi.com/2076-3417/6/6/175 |
| work_keys_str_mv | AT rongshan networkmodelingandassessmentofecosystemhealthbyamultipopulationswarmoptimizedneuralnetworkensemble AT zengshunzhao networkmodelingandassessmentofecosystemhealthbyamultipopulationswarmoptimizedneuralnetworkensemble AT panfeichen networkmodelingandassessmentofecosystemhealthbyamultipopulationswarmoptimizedneuralnetworkensemble AT weijianliu networkmodelingandassessmentofecosystemhealthbyamultipopulationswarmoptimizedneuralnetworkensemble AT shuyixiao networkmodelingandassessmentofecosystemhealthbyamultipopulationswarmoptimizedneuralnetworkensemble AT yuhanhou networkmodelingandassessmentofecosystemhealthbyamultipopulationswarmoptimizedneuralnetworkensemble AT maoyongcao networkmodelingandassessmentofecosystemhealthbyamultipopulationswarmoptimizedneuralnetworkensemble AT faliangchang networkmodelingandassessmentofecosystemhealthbyamultipopulationswarmoptimizedneuralnetworkensemble AT zhigangwang networkmodelingandassessmentofecosystemhealthbyamultipopulationswarmoptimizedneuralnetworkensemble |