中国地质灾害与防治学报
中國地質災害與防治學報
중국지질재해여방치학보
THE CHINESE JOURNAL OF GEOLOGICAL HAZARD AND CONTROL
2011年
2期
14-19
,共6页
王杰%马凤山%郭捷%魏爱华%巩城城
王傑%馬鳳山%郭捷%魏愛華%鞏城城
왕걸%마봉산%곽첩%위애화%공성성
曲线拟合%QR分解%层次分析法%条件概率模型%滑坡危险性评价%GIS二次开发
麯線擬閤%QR分解%層次分析法%條件概率模型%滑坡危險性評價%GIS二次開髮
곡선의합%QR분해%층차분석법%조건개솔모형%활파위험성평개%GIS이차개발
curve fitting%QR decomposition%analytic hierarchy process%conditional probability model%hazard assessment of landslide%GIS secondary development
区域滑坡危险性评价方法还存在许多需要完善和改进的地方。以工程地质类比法为基础,用滑坡的面密度表示滑坡发生的危险性大小,基于线性代数中QR分解理论,提出了一种用高次多项式拟合致险因子与滑坡危险性间关系的算法,并把该算法与层次分析法模型、条件概率模型相融合,建立了一种改进的区域滑坡危险性评价模型。然后,通过在Visual Studio.Net C#环境下借助ArcEngine组件的二次开发实现了该模型。最后选取陕西省麟游县为实验区域,利用上述模型进行了滑坡危险性评价。经实际资料检验表明,该模型具有较高的可信度,可应用于今后的滑坡危险性区域评价工作中。
區域滑坡危險性評價方法還存在許多需要完善和改進的地方。以工程地質類比法為基礎,用滑坡的麵密度錶示滑坡髮生的危險性大小,基于線性代數中QR分解理論,提齣瞭一種用高次多項式擬閤緻險因子與滑坡危險性間關繫的算法,併把該算法與層次分析法模型、條件概率模型相融閤,建立瞭一種改進的區域滑坡危險性評價模型。然後,通過在Visual Studio.Net C#環境下藉助ArcEngine組件的二次開髮實現瞭該模型。最後選取陝西省麟遊縣為實驗區域,利用上述模型進行瞭滑坡危險性評價。經實際資料檢驗錶明,該模型具有較高的可信度,可應用于今後的滑坡危險性區域評價工作中。
구역활파위험성평개방법환존재허다수요완선화개진적지방。이공정지질류비법위기출,용활파적면밀도표시활파발생적위험성대소,기우선성대수중QR분해이론,제출료일충용고차다항식의합치험인자여활파위험성간관계적산법,병파해산법여층차분석법모형、조건개솔모형상융합,건립료일충개진적구역활파위험성평개모형。연후,통과재Visual Studio.Net C#배경하차조ArcEngine조건적이차개발실현료해모형。최후선취합서성린유현위실험구역,이용상술모형진행료활파위험성평개。경실제자료검험표명,해모형구유교고적가신도,가응용우금후적활파위험성구역평개공작중。
The methods for regional landslide hazard assessment still have many aspects that are need to be improved. Based on engineering geologieal analogy and QR decomposition, using the area density of landslide to describe the landslide hazard, an algorithm that uses high order polynomial to fit the relationship between the hazard-inducing factors and landslide hazard was presented. Then, combining this algorithm, analytic hierarchy process and conditional probability model an improved regional landslide hazard assessment model was established. Then, a software was developed to realize this model under the Visual Studio. Net C # environment and using ArcEngine Components. Finally, Linyou County in Shanxi Province was selected as an experimental area to do the landslide hazard assessment. Checked by the actual data, this model has a high reliability and can be used in the future.