基于粗糙集理论的区域降雨型滑坡预测预报

选择四川省雅安市雨城区为研究区域，以该区降雨型滑坡为数据样本，结合粗糙集理论，提取了研究区域的降雨因子和地质因子作为条件属性因子，利用粗糙集的计算机挖掘和智能知识发现功能，分析得到了预测降雨型滑坡发生与否的有价值的规则集。所生成的决策规则能够实现该区域滑坡的预测预报。研究结果表明:本方法较传统的统计方法更符合降雨型滑坡预测的非线性关系，而且考虑了地质因素的影响，较单纯降雨阈值的预测方法有更高的空间分辨率。

Rainfall-induced landslides prediction based on rough sets

The rainfall-induced landslides data in the Yucheng district，Ya’an city in Sichuang province are chosen as the sample data. Rainfall factors and geologic factor are extracted as the condition attribute of the rough sets. Based on the data mining and intelligent knowledge discovery function of the rough sets theory, the valuable rules are generated which can make judgments to rainfall-induced landslides occurrence. The decision rule can provide the prediction of regional landslides. The nonlinear characteristic and geological factors of the landslides are considered, and the method has a better predictive effect and higher spatial resolution than the traditional statistical method.