摘要
为了解决传统的贝叶斯分类技术在构建滑坡危险性分类和预测的模型的过程中难以有效地获取预测模型所需的参数及滑坡诱发因素定量刻画技术难题等问题,引入不确定贝叶斯算法,将不确定数据的可能世界模型与朴素贝叶斯分类模型结合起来,构建了不确定贝叶斯分类模型,从而有效刻画降雨量这一属性级不确定的属性,达到提高滑坡危险性预测精度的目的。通过实例验证了运用该方法进行滑坡危险评价的可行性和高效性。
The traditional Bayesian classification technology can＇t obtain the effective parameters which the prediction model needs during constructing the classification model. And the method to describe the inducing factors generating a landslide quantificationally is difficult. Therefore, the uncertain Bayes algorithm is introduced. The uncertain Bayes classification model is constructed by combining the uncertain world model with the naive Bayes classification model. Finally,the goal to effectively describe the character of the rainfall which is an uncertain attribute and improve the accuracy of the landslide hazard prediction is achieved. Experiment over a realistic dataset reveals that the approaches improve the landslide risk evaluation significantly.
出处
《计算机工程与应用》
CSCD
北大核心
2015年第17期238-244,共7页
Computer Engineering and Applications
基金
国家自然科学基金(No.41362015)
