원문정보
보안공학연구지원센터(IJUNESST)
International Journal of u- and e- Service, Science and Technology
Vol.8 No.2
2015.02
pp.101-108
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
In order to improve the accuracy of forecasting forests diseases and the number of insect number, the paper makes conclusion by using gray model and Markova chain model. It takes Hongxing forestry bureau as a demonstration site and also forecasts the insect number according to the historical data from it. By demonstrating the disease of larch that fall early among the sites in 10 years, the result shows that the forecasts are coincided with the practical case. And the rate of coincidence can be up to 90%.
목차
Abstract
1. Introduction
2. Gray Model and Markov Process
2.1 Markov Process
2.2 Gray Model
3. Application of Markov Process Analysis in Forest Disease Forecasting
4. Application of Gray Model in Forest Disease Forecasting
4.1 Utilize the Gray Model to Simulate the Pathogenesis Process and Predict the Time Point of the Onset of the Mycosphaerella Laricileptolepis Lto, et al.
4.2 Utilize Gray Model to Analyze and Predict the Disease Index of Mycosphaerella Laricileptolepis Lto, et al.
5. Conclusion
Acknowledgements
References
1. Introduction
2. Gray Model and Markov Process
2.1 Markov Process
2.2 Gray Model
3. Application of Markov Process Analysis in Forest Disease Forecasting
4. Application of Gray Model in Forest Disease Forecasting
4.1 Utilize the Gray Model to Simulate the Pathogenesis Process and Predict the Time Point of the Onset of the Mycosphaerella Laricileptolepis Lto, et al.
4.2 Utilize Gray Model to Analyze and Predict the Disease Index of Mycosphaerella Laricileptolepis Lto, et al.
5. Conclusion
Acknowledgements
References
저자정보
참고문헌
자료제공 : 네이버학술정보