Research of Regional Forest Fire Prediction Method based on Multivariate Linear Regression
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- 발행기관
- 보안공학연구지원센터(IJSH) 바로가기
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- 간행물
- International Journal of Smart Home 바로가기
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- 통권
- Vol.9 No.1 (2015.01)바로가기
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- 페이지
- pp.13-22
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A239350
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- In order to achieve the predicted speed, high accuracy, the use of simple purpose, forest fire prediction of the key issues is to choose the main predictors. Forest fire prediction involves many factors, some of which are stable factors such as climate, topography, forest characteristics; and some unstable factors, such as fuel moisture content, meteorological factors, and other sources of ignition. Currently leading factor in the prediction of forest fire is often used in the fuel moisture, precipitation or dry days, relative humidity, temperature and wind five factors. In this paper, some of the data Yichun fire nearly a decade predict the forest fire meteorological data analysis, using multivariate linear regression to derive forest fire prediction method in the wireless sensor networks.
목차
Abstract
1. Regional Overview Research
2. Analysis of Factors Affecting the Occurrence of Forest Fires
2.1. The Influence of Seasonal Variation on the Occurrence of Forest Fires
2.2. The Influence of Meteorological Factors on the Occurrence of Forest Fires
2.3. The Influence of Other Factors on the Occurrence of Forest Fires
3. The Contribution of a Single Factor of Meteorological Factors Occurring Forest Fires
3.1. The Contribution of the Daily Maximum Temperature on Forest Fire Occurrence
3.2. The Contribution of Diurnal Temperature Range on Forest Fire Occurrence
3.3. The Contribution of Diurnal Temperature Range on Forest Fire Occurrence
3.4. The Average Relative Humidity of the Air Three Days Before on Forest Fire Occurrence
3.5. The Contribution of 24 Hours of Precipitation on Forest Fire Occurrence
3.6. The Contribution of Wind Speed on Forest Fire Occurrence
4. Logistic Regression Test the Contribution of Various Meteorological Factors
4.1. Logistic Regression Method
4.2. Calculate the Likelihood Ratio of the Meteorological Factors Using Logistic Regression
5. The Achievement of Integrated Meteorological Indicators to Predict Forest Fire Method
5.1. Multiple Linear Regression Equation
5.2. Multiple Linear Regression Equation to Predict Forest Fires Fire Rating
6. Conclusion
Acknowledgement
References
키워드
wireless sensor network forest fire prediction Multivariate Linear Regression저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJSH) [Science & Engineering Research Support Center, Republic of Korea(IJSH)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of Smart Home
- 간기
- 격월간
- pISSN
- 1975-4094
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
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