Shared near neighbours neural network model: a debris flow warning system

The main purpose of this study is to develop a new type of artificial neural network based model for constructing a debris flow warning system. The Chen‐Eu‐Lan river basin, which is located in Central Taiwan, is assigned as the study area. The creek is one of the most well‐known debris flow areas where several damaging debris flows have been reported in the last two decades. The hydrological and geological data, which might have great influence on the occurrence of debris flows, are first collected and analysed, then, the shared near neighbours neural network (SNN + NN) is presented to construct the debris flow warning system for the watershed. SNN is an unsupervised learning method that has the advantage of dealing with non‐globular clusters, besides presenting computational efficiency. By using SNN, the compiled hydro‐geological data set can easily and meaningfully be clustered into several categories. These categories can then be identified as ‘occurrence’ or ‘no‐occurrence’ of debris flows. To improve the effectiveness of the debris flow warning system, a neural network framework is designed to connect all the clusters produced by the SNN method, whereas the connected weights of the network are adjusted through a supervised learning method. This framework is used and its applicability and practicability for debris flow warning are investigated. The results demonstrate that the proposed SNN + NN model is an efficient and accurate tool for the development of a debris flow warning system. Copyright © 2007 John Wiley & Sons, Ltd.     

Investigating the impact of the Chi‐Chi earthquake on the occurrence of debris flows using artificial neural networks

Debris flows have caused enormous losses of property and human life in Taiwan during the last two decades. An efficient and reliable method for predicting the occurrence of debris flows is required. The major goal of this study is to explore the impact of the Chi‐Chi earthquake on the occurrence of debris flows by applying the artificial neural network (ANN) that takes both hydrological and geomorphologic influences into account. The Chen‐Yu‐Lan River watershed, which is located in central Taiwan, is chosen for evaluating the critical rainfall triggering debris flows. A total of 1151 data sets were collected for calibrating model parameters with two training strategies. Significant differences before and after the earthquake have been found: (1) The size of landslide area is proportioned to the occurrence of debris flows; (2) the amount of critical rainfall required for triggering debris flows has reduced significantly, about half of the original critical rainfall in the study case; and (3) the frequency of the occurrence of debris flows is largely increased. The overall accuracy of model prediction in testing phase has reached 96·5%; moreover, the accuracy of occurrence prediction is largely increased from 24 to 80% as the network trained with data from before the Chi‐Chi earthquake sets and with data from the lumped before and after the earthquake sets. The results demonstrated that the ANN is capable of learning the complex mechanism of debris flows and producing satisfactory predictions. Copyright © 2009 John Wiley & Sons, Ltd.     

宁南县城后山泥石流生物治理

后山属泥石流活跃区,需加以治理。治理区面积8.39平方公里,县城面积2.00平方公里。1985—1989年开展治理。后山泥石流生物治理总投资10.09万元。其完成的工程有:造林面积2077亩,栽桑面积50亩,栽油桐面积450亩。生物治理后,创产值48.74万元。因而投入与产出比1.0∶4.8。森林覆盖率从1985年的27.3％增加到1989年的43.8％。生物治理在后山泥石流综合治理中起着举足轻重的作用。     

Site-specific Vulnerability Assessment for Debris Flows： Two Case Studies

Introduction Here vulnerability is related to the hazard-affected bodies including human beings and material objects. Without hazard-affected bodies, there would be no disasters. Disaster is a combination of the hazard-affected bodies and the hazard-forma…     

泥石流自动化观测系统

东川泥石流自动化观测系统是在原有的半自动化监测系统基础上研制完成的。它通过综控中心、地声遥测、泥位遥测、雨量遥测、有线泥位和冲击力接口六个子系统共同完成对泥石流构成的一些主要参数包括降雨量、地声强度、泥位高度、表面平均流速、冲击力强度等数据进行同步采样并储存；系统对现代软硬件技术的广泛运用使该系统具有很高的智能性和可扩充性。     

蒋家沟泥石流表面流速公式试验研究

根据蒋家沟泥石流体的物理特征,建立泥石流运动力学方程,用因次分析法导出泥石流流速公式的无因次乘积表达式。通过蒋家沟泥石流流动试验,确定其无因次乘积数,得出蒋家沟泥石流表面流速公式为Uc=Vy Hc~(1/2)g~(1/2)/2.405 Vc Ic~(1/2),Uc为泥石流表面流速,Vy为泥石流中土体的损失容重,g为重力加速度,Yc为泥石流体容重,Ic为河床纵坡。模型试验和原型观测所测得的流速与按试验公式计算的流速对比,其误差分别为1.0—4.5％和1.0—14.5％。由此表明,试验得出的表面流速公式符合实际情况,反映了蒋家沟泥石流运动规律。     

粗细颗粒同时存在时的流动阻力

为了研究同时存在粗细颗粒的流动阻力,进行了一组专门设计的试验。分析试验资料结果表明:由细的粘土颗粒引起的粘性力与由粗颗粒引起的离散力是可以线性叠加的。离散力是由于粗颗粒相互之间的碰撞,以及粗颗粒与边壁之间的碰撞所造成的。而粘土颗粒的絮凝作用形成絮团和絮网结构,它们对粗颗粒之间的碰撞起到缓冲作用。因而当有细的粘土颗粒存在时,离散力大幅度降低。     

细颗粒浆体、泥石流浆体对泥石流运动的作用

通过泥石流野外观测资料分析,得出稀性泥石流及粘性泥石流存在颗粒不分选的浆体,并讨论了一般形式的泥石流流速公式,它适用于类似蒋家沟各种类型的泥石流。     

泥石流防撞墩冲击力理论计算方法

在西部泥石流多发区,交通线路经常需要穿越泥石流沟,设置在泥石流沟床中的桥墩容易遭受泥石流大块石的冲击,为此,常常在其前端设置泥石流防撞墩,以达到保护桥墩的目的。防撞墩设计的关键参数是泥石流大块石的冲击力。目前,有关泥石流冲击力的计算方法都比较粗糙,计算结果与实际情况不符。本文以Thornton理想弹塑性接触模型为基础,并考虑防撞墩的弯矩变形特性,根据能量守恒定律,推导了泥石流大块石冲击力的计算公式。结果表明,考虑结构弹塑性特性后,泥石流大块石冲击力大大降低,远小于按弹性冲击理论所确定的冲击力,计算结果更符合实际情况。     

泥石流坝后侵蚀坑纵剖面形态及最大深度实验研究

泥石流拦砂坝坝后侵蚀坑形态和深度是泥石流冲刷基础研究的薄弱环节。通过室内水槽实验,探讨了泥石流坝后侵蚀坑的形态和不同实验控制条件下侵蚀坑深度的变化规律等。由实验观察可知,侵蚀坑纵剖面整体上呈现两端浅中间深的形态特征,其最深点的位置随水槽坡度增大向下游方向发展;侵蚀坑坑内上游坡度较下游坡度陡,对于具有相同级配的粘性砂和无粘性砂,无粘性砂的侵蚀坑坑内坡度较粘性砂的缓;侵蚀豌的最大深度受沟床纵坡、泥石流的容重、沟床组成物质的性质（特征粒径、粘性）等因素的影响较大;泥砂粘性的存在将大大削弱侵蚀的深度。