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滑坡预测模型的选择直接影响到滑坡预测的准确性,是滑坡预测的关键所在。该研究利用意大利Alpago地区的滑坡数据和其他相关地理空间数据,以模糊伽马模型、模糊代数积模型、模糊代数和模型以及模糊最小模型等4个定量滑坡预测模型为例,探讨滑坡预测模型的预测率在对比、评价和选择不同模型方面的作用。滑坡预测模型的预测率是,模型预测结果图的各个级别类型中,未用于建模的滑坡面积百分比的累积分布函数。在地理信息系统中,利用已知的滑坡分布数据和模型的预测结果图,可以计算滑坡预测模型的预测率。研究结果表明,滑坡模型的预测率是滑坡预测模型自身特性的度量,在输入图层和滑坡类型确定的条件下,滑坡预测模型的预测率可作为对比、评价和选择不同模型的定量指标,可以用来确定最合适的预测模型。
The choice of landslide prediction model directly affects the accuracy of landslide prediction, which is the key of landslide prediction. Taking the landslide data and other related geospatial data in Alpago region of Italy as an example, this study uses four quantitative landslide prediction models, such as fuzzy gamma model, fuzzy algebra product model, fuzzy algebraic model and fuzzy minimum model, to study the landslide prediction model Predicting rates in the comparison, evaluation and selection of different models of the role. The prediction rate of the landslide prediction model is the cumulative distribution function of the percentage of landslide area that is not used for modeling in each level type of the model prediction result map. In GIS, the prediction rate of the landslide prediction model can be calculated by using the known landslide distribution data and the prediction results of the model. The results show that the prediction rate of landslide model is a measure of the characteristics of the landslide prediction model. Under the conditions of the input layer and landslide type, the prediction rate of landslide prediction model can be used as a quantitative index to evaluate and select different models, Used to determine the most suitable prediction model.