Abstract
The main objective of this study was to fit and recognize spatial distribution patterns of grassland insects using various neural networks, and to analyze the feasibility of neural networks for detecting spatial distribution patterns of grassland insects. BP neural network, Learning vector quantization (LVQ) neural network, linear neural network and Fisher’s linear discriminant analysis were used to fit and recognize spatial distribution patterns at different ecological scales. Various comparisons and analysis were conducted. The results showed that BP, LVQ and linear neural networks were better algorithms for recognizing spatial distribution patterns of grassland insects. BP neural network was the best algorithm to fit spatial distribution patterns. BP network may be used to recognize the spatial details of distribution patterns, and the recognition performance of BP network became better as the increase of the number of hidden layers and neurons. Performance of linear neural network for pattern recognition was similar to linear discrimination method. Linear neural network would yield better performance in finding the general trends of distribution patterns. Recognition performance of LVQ network was just between BP network and linear network. It was found that recognition performance of neural networks depended upon not only the ecological scale but also the criterion for classification. Under the uniform criterion, recognition efficiency of linear methods tended to be weak as ecological scale became to be coarser. A joint use of neural networks was suggested in order to achieve both overall and detailed understanding on spatial distribution patterns.
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Acknowledgments
This research was granted by “973” program of China (2006CB102005), and “948” program of China (2006-G32). We thank all participants of insects investigation, Mr. WG Zhou, HQ Dai, and undergraduates of ecological science, Zhongshan University, China.
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Zhang, W., Zhong, X. & Liu, G. Recognizing spatial distribution patterns of grassland insects: neural network approaches. Stoch Environ Res Risk Assess 22, 207–216 (2008). https://doi.org/10.1007/s00477-007-0108-3
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DOI: https://doi.org/10.1007/s00477-007-0108-3