Matches in SemOpenAlex for { <https://semopenalex.org/work/W2028293566> ?p ?o ?g. }
Showing items 1 to 76 of
76
with 100 items per page.
- W2028293566 endingPage "1126" @default.
- W2028293566 startingPage "1119" @default.
- W2028293566 abstract "이 논문에서는 공간적 통계기법에 근거한 예측적 공간 데이터 마이닝 방법을 제안하고, 산불위험지역을 예측하는데 적용하였다. 제안된 방법은 조건부 확률과 우도비를 이용한 방법으로 과거 산불발생지역에 대해 산불과 관련된 공간데이터 집합들 사이의 정량적 관계에 의존적인 예측 모델이다. 두 가지 방법을 이용하여 산불위험지역 예측도를 만들고, 각 모델의 예측력을 평가하기 위해 산불위험율(FHR : Forest Fire Hazard Rate)과 예측률곡선(PRC : Prediction Rate Curve)을 이용하였다. 제안된 두 가지 예측모델의 예측력 비교분석 결과, 우도비 방법이 조건부 확률 방법보다 더 우수한 것으로 나타났다. 이 논문에서 제안된 산불위험지역 예측모델을 이용하여 작성된 산불위험지역 예측도는 산불예방과 산불감시장비 및 인력의 효율적인, 배치 등 산불관리의 효율성을 높이는데 많은 도움을 줄 것으로 기대된다. In this paper, we propose two predictive spatial data mining based on spatial statistics and apply for predicting the forest fire hazardous area. These are conditional probability and likelihood ratio methods. In these approaches, the prediction models and estimation procedures are depending un the basic quantitative relationships of spatial data sets relevant forest fire with respect to selected the past forest fire ignition areas. To make forest fire hazardous area prediction map using the two proposed methods and evaluate the performance of prediction power, we applied a FHR (Forest Fire Hazard Rate) and a PRC (Prediction Rate Curve) respectively. In comparison of the prediction power of the two proposed prediction model, the likelihood ratio method is mort powerful than conditional probability method. The proposed model for prediction of forest fire hazardous area would be helpful to increase the efficiency of forest fire management such as prevention of forest fire occurrence and effective placement of forest fire monitoring equipment and manpower." @default.
- W2028293566 created "2016-06-24" @default.
- W2028293566 creator A5033399095 @default.
- W2028293566 creator A5041986918 @default.
- W2028293566 creator A5043932631 @default.
- W2028293566 creator A5077876218 @default.
- W2028293566 date "2002-12-01" @default.
- W2028293566 modified "2023-09-26" @default.
- W2028293566 title "Prediction of Forest Fire Hazardous Area Using Predictive Spatial Data Mining" @default.
- W2028293566 cites W2048429184 @default.
- W2028293566 cites W2067734778 @default.
- W2028293566 cites W2149706766 @default.
- W2028293566 cites W2295297234 @default.
- W2028293566 cites W2508163925 @default.
- W2028293566 doi "https://doi.org/10.3745/kipstd.2002.9d.6.1119" @default.
- W2028293566 hasPublicationYear "2002" @default.
- W2028293566 type Work @default.
- W2028293566 sameAs 2028293566 @default.
- W2028293566 citedByCount "0" @default.
- W2028293566 crossrefType "journal-article" @default.
- W2028293566 hasAuthorship W2028293566A5033399095 @default.
- W2028293566 hasAuthorship W2028293566A5041986918 @default.
- W2028293566 hasAuthorship W2028293566A5043932631 @default.
- W2028293566 hasAuthorship W2028293566A5077876218 @default.
- W2028293566 hasBestOaLocation W20282935661 @default.
- W2028293566 hasConcept C105795698 @default.
- W2028293566 hasConcept C119857082 @default.
- W2028293566 hasConcept C124101348 @default.
- W2028293566 hasConcept C127413603 @default.
- W2028293566 hasConcept C159620131 @default.
- W2028293566 hasConcept C169258074 @default.
- W2028293566 hasConcept C178790620 @default.
- W2028293566 hasConcept C185592680 @default.
- W2028293566 hasConcept C22507642 @default.
- W2028293566 hasConcept C33923547 @default.
- W2028293566 hasConcept C39432304 @default.
- W2028293566 hasConcept C41008148 @default.
- W2028293566 hasConcept C45804977 @default.
- W2028293566 hasConcept C49261128 @default.
- W2028293566 hasConcept C548081761 @default.
- W2028293566 hasConceptScore W2028293566C105795698 @default.
- W2028293566 hasConceptScore W2028293566C119857082 @default.
- W2028293566 hasConceptScore W2028293566C124101348 @default.
- W2028293566 hasConceptScore W2028293566C127413603 @default.
- W2028293566 hasConceptScore W2028293566C159620131 @default.
- W2028293566 hasConceptScore W2028293566C169258074 @default.
- W2028293566 hasConceptScore W2028293566C178790620 @default.
- W2028293566 hasConceptScore W2028293566C185592680 @default.
- W2028293566 hasConceptScore W2028293566C22507642 @default.
- W2028293566 hasConceptScore W2028293566C33923547 @default.
- W2028293566 hasConceptScore W2028293566C39432304 @default.
- W2028293566 hasConceptScore W2028293566C41008148 @default.
- W2028293566 hasConceptScore W2028293566C45804977 @default.
- W2028293566 hasConceptScore W2028293566C49261128 @default.
- W2028293566 hasConceptScore W2028293566C548081761 @default.
- W2028293566 hasIssue "6" @default.
- W2028293566 hasLocation W20282935661 @default.
- W2028293566 hasOpenAccess W2028293566 @default.
- W2028293566 hasPrimaryLocation W20282935661 @default.
- W2028293566 hasRelatedWork W2054660914 @default.
- W2028293566 hasRelatedWork W2348730826 @default.
- W2028293566 hasRelatedWork W2360659369 @default.
- W2028293566 hasRelatedWork W2606383284 @default.
- W2028293566 hasRelatedWork W2899084033 @default.
- W2028293566 hasRelatedWork W2906922585 @default.
- W2028293566 hasRelatedWork W3009997128 @default.
- W2028293566 hasRelatedWork W3011265345 @default.
- W2028293566 hasRelatedWork W3204083693 @default.
- W2028293566 hasRelatedWork W4308095723 @default.
- W2028293566 hasVolume "9D" @default.
- W2028293566 isParatext "false" @default.
- W2028293566 isRetracted "false" @default.
- W2028293566 magId "2028293566" @default.
- W2028293566 workType "article" @default.