降雨影响下的动态加权贝叶斯尾矿库溃坝风险评估

Dynamic weighted Bayesian dam break risk assessment of tailings pond under the influence of rainfall

溃坝风险存在于尾矿库运行期的各个阶段,但传统的风险预测难以反映出尾矿库溃坝风险实时动态变化。为解决这一问题,本文首先构建了包含动态指标和静态指标的评价指标体系,并计算指标权重和时间权重,建立了嵌入时间权重和指标权重的动态加权贝叶斯网络尾矿库溃坝风险评估模型;然后通过区间数优度排序改进参数学习数据不足情况下条件概率的求取方法;最后对不同降雨持续时间和不同降雨量影响下尾矿库溃坝风险的动态贝叶斯网络进行概率推理。研究结果表明:当持续3 d降雨量为A级(高风险)和B级(较高风险)时,降雨量造成尾矿库溃坝的风险概率分别从19.0%和9.1%上升到34.9%和20.4%。由此可见,引入时间权重的评估模型体现出了尾矿库溃坝的动态发展过程,动态评估更符合客观事实。

Dam break risks exist in all stages of the tailings pond operation period,but traditional risk predictions cannot reflect the real-time dynamic changes of tailings pond dam-break risks.To solve this problem,this paper builds an evaluation index system including dynamic indicators and static indicators is constructed,and index weights and time weights.A dynamic weighted Bayesian network tailings reservoir dam break risk assessment model with embedded time weights and indicator weights is established.Then improves the method of obtaining conditional probability in the case of insufficient parameter learning data through interval number goodness sorting.Finally,the dynamic Bayesian network of tailing pond dam break risk under different rainfall durations and different rainfall effects is carried out probabilistic inference.The results show that when the rainfall for 3 days is class A(high risk)and class B(higher risk),the risk probability of dam break of tailings pond caused by rainfall increases from 19.0%and 9.1%to 34.9%and 20.4%respectively.Research shows that the introduction of the timeweighted evaluation model reflects the dynamic development process of the research object,dynamic assessment is more in line with objective facts.