Network Modeling and Assessment of Ecosystem Health by a Multi-Population Swarm Optimized Neural Network Ensemble
AbstractSociety 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. View Full-Text
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Shan, R.; Zhao, Z.-S.; Chen, P.-F.; Liu, W.-J.; Xiao, S.-Y.; Hou, Y.-H.; Cao, M.-Y.; Chang, F.-L.; Wang, Z. Network Modeling and Assessment of Ecosystem Health by a Multi-Population Swarm Optimized Neural Network Ensemble. Appl. Sci. 2016, 6, 175.
Shan R, Zhao Z-S, Chen P-F, Liu W-J, Xiao S-Y, Hou Y-H, Cao M-Y, Chang F-L, Wang Z. Network Modeling and Assessment of Ecosystem Health by a Multi-Population Swarm Optimized Neural Network Ensemble. Applied Sciences. 2016; 6(6):175.Chicago/Turabian Style
Shan, Rong; Zhao, Zeng-Shun; Chen, Pan-Fei; Liu, Wei-Jian; Xiao, Shu-Yi; Hou, Yu-Han; Cao, Mao-Yong; Chang, Fa-Liang; Wang, Zhigang. 2016. "Network Modeling and Assessment of Ecosystem Health by a Multi-Population Swarm Optimized Neural Network Ensemble." Appl. Sci. 6, no. 6: 175.
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