三峡库区泥石流灾害预警研究

 采用三峡库区有历史记录的40次泥石流灾害的日降雨量以及前15 d的降雨量,当日1 h及10 min最大降雨强度等降雨参数作为训练数据,在对研究区进行荒溪分类以及灾害危险区域制图的基础上,结合雨季的降雨特征监测以及实时天气预报模型,构建了基于神经网络的三峡库区泥石流灾害实时预报模型。详细阐述了应用神经网络方法建立三峡库区泥石流灾害实时预报模型的关键技术,包括确定输入层、输出层以及隐含层的神经节点,建立学习知识库以及各节点初始权重等相关指标。该模型可以预测泥石流发生的临界降雨量、警戒降雨量以及避灾降雨量等指标,因此,依据当地降雨的实时监测数据或天气预报,就可以预测泥石流的发生几率,从而减少泥石流发生的直接危害。模型采样GIS技术以及国际先进的软、硬件技术,系统性能稳定,运行结果准确性较高,且具有广泛的应用性。该模型为可视化信息系统,可以通过该系统监测荒溪类型变化,进行危险区域制图,实现荒溪科学管理,为三峡库区泥石流灾害预警及防治的管理和决策提供科学参考。

Early-warning system for debris flow disaster in the Three Gorges Reservoir region

The key techniques of building a real-time forecast model for debris flow disaster using neural network (NN) method are explained in detail in this paper, including the determination of neural nodes at the input layer, the output layer and the implicit layer, the construction of knowledge source and the initial weight values and so on. The neural network-based real-time forecast model for debris flow disaster is built using the rainfall parameters of 40 historical debris flow disasters as training data, which included multiple rainfall factors such as the rainfall of the day disaster happening, the rainfalls of 15 days before the disaster, the maximal rainfall intensity of one hour and ten minutes. Based on the torrent classification and hazard zone mapping of the study region, combined with the rainfall monitoring in the rainy season and real-time weather forecast models, the NN-based early-warning system for debris flow disaster ran well. In this system, GIS technique, advanced international software and hardware were applied, which made performance of the system steady and its applicability wide. It can forecast some most important indices, the probability, the critical rainfall, the warning rainfall, and the refuge rainfall of debris flow occurring, and reduce the direct disserve in the debris flow disasters through the real-time monitoring of rainfall or local weather forecast. As it was a visual information system, we could monitor the variation of the torrent types and hazardous zones, and the torrent management through it, so it could serve the local management and decision-making on the debris flow disaster warning and prevention.