Wolf is Coming—Dynamic Classification Prediction Model of Vespa Mandarinia

Authors

  • Yang Yue Mathematical Modeling Innovation Lab, North China University of Science and Technology, Tangshan, Hebei, 063210,China;School of Chemical Engineering, North China University of Science and Technology, Tangshan, Hebei, 063210, China
  • Haomiao Niu Mathematical Modeling Innovation Lab, North China University of Science and Technology, Tangshan, Hebei, 063210,China;School of Chemical Engineering, North China University of Science and Technology, Tangshan, Hebei, 063210, China
  • Jiao Liu School of Clinical Medicine, North China University of Science and Technology, Tangshan, Hebei, 063210, China

DOI:

https://doi.org/10.30564/jees.v3i1.2924

Abstract

Given the threat of Vespa mandarinia invasion to ecological balance, according to the data and information provided, the dynamic reproduction model of Vespa mandarinia is established by using natural domain interpolation, and the variation law of total bumblebee with time, latitude, and longitude is obtained. At the same time, we established the classification prediction model by using a neural network and established the mapping relationship between time and space to evaluation grade.

We meshed the area provided by the title, assigned values to the location of Vespa mandarinia (VM), and established a VM diffusion model with natural neighborhood interpolation. Its propagation process is simulated by cellular automata. It is determined that VM spreads in a circular shape centered at (122.93174°W, 48.93457°N) and (122.57376°W, 49.07848°N) in the Washington area, with the farthest distance being 1184.4 km and 985 km respectively.

We set up a classification prediction model for better classification. According to the image upload time and location, SVM and neural network are used for classification prediction, and the classification accuracy is 74.26% and 97.60%, respectively, and the neural network has higher classification accuracy. So we choose the neural network.

Keywords:

Neural network, Ecological equilibrium, Dynamic reproduction

References

[1] Requier, Fabrice, et al. "Predation of the invasive Asian hornet affects foraging activity and survival probability of honey bees in Western Europe." Journal of pest science 92.2 (2019): 567-578.

[2] Beggs, Jacqueline R., et al. "Ecological effects and management of invasive alien Vespidae." BioControl 56.4 (2011): 505-526.

[3] Tan, Ken, et al. "Honey bee inhibitory signaling is tuned to threat severity and can act as a colony alarm signal." PLoS biology 14.3 (2016): e1002423.

[4] Cole, Vivienne, and Jochen Albrecht. "Modelling the spread of invasive species: parameter estimation using cellular automata." In Proceedings Second International Workshop on Dynamic and Multi-Dimensional GIS (DMGIS' 99. 1999.

[5] Gao Jianhua. Several Improvement and Application of Evolutionary Strategy.2015. Wuhan University, Ph.D. dissertation.

[6] Robinet, Christelle, Christelle Suppo, and Eric Darrouzet. "Rapid spread of the invasive yellow-legged hornet in F rance: the role of human-mediated dispersal and the effects of control measures." Journal of Applied Ecology 54.1 (2017): 205-215.

[7] AbdelRahman, Mohamed AE, et al. "Deciphering Soil Spatial Variability through Geostatistics and Interpolation Techniques." Sustainability 13.1 (2021): 194.

[8] Nuñez-Penichet, Claudia, et al. "Geographic potential of the world's largest hornet, Vespa mandarinia Smith (Hymenoptera: Vespidae), worldwide and particularly in North America." PeerJ 9 (2021): e10690.

[9] Feng Guohe." Comparison of kernel function and parameter selection for SVM classification." Computer Engineering and Applications, 47.03(2011):123- 124+128.

[10] Sze Bi, Tinglei Huang." Design of Support Vector Machines Based on Linear Distance Kernels." Journal of Beijing University of Electronic Science and Technology (2013):478-481.

[11] Li Song, Liu Lijun, and Zhai Man." Improved Particle Swarm Optimization Algorithm for ShortTerm Traffic Flow Prediction Based on BP Neural Network." Journal of Systems Engineering 32.09(2012):2045-2049.

[12] Li Xiaoyan. Research on the Optimization of Grey Neural Network Prediction Model.2009. Wuhan University of Technology, MA Thesis.

Downloads

How to Cite

Yue, Y., Niu, H., & Liu, J. (2021). Wolf is Coming—Dynamic Classification Prediction Model of Vespa Mandarinia. Journal of Environmental & Earth Sciences, 3(1), 41–47. https://doi.org/10.30564/jees.v3i1.2924

Issue

Article Type

Article