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DEVELOPMENT OF FORCASTING AND WARNING SYSTEM FOR DEBRIS FLOWS,MT.UNZENDAKE

云仙岳山泥石流预报预警系统的开发

基本信息

批准号：
06558058
负责人：
HIRANO Muneo
金额：
$ 1.79万
依托单位：
KYUSHU UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Developmental Scientific Research (B)
财政年份：
1994
资助国家：
日本
起止时间：
1994 至 1995
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-06558058/
关键词：
DEBRIS FLOWS MT.UNZEN FIFLD MEASUREMENT PRECIPITATION RADAR FORCASTING SYSTEM WARNING SYSTEM

项目摘要

Debris flows have frequently occurred in the Mizunashi River and caused severe damage in the downstream area at Unzen Volcano by deposition of large amount of sediments. Therefore it becomes important to predict the debris flows. The results obtained in this study are as follows :(1) Field observations and measurements of debris flows have been carried out at two locations on Mt.Unzendake. Radio current-meter and ultrasonic water level gauge were used to obtain surface velocity, depth and discharge. Peak discharge was found Q=195m^3/s at the Mizunashi River and Q=40m^3/s at the Nakao River.(2) A neural network is used to make a runoff model of debris flow. The data of velocity and depth of debris flow were collected at the Mizunashi River on 12-13 June, 1993. For learning, the discharge of debris flow Q (t), and ten-minute rainfall 20 to 70 minute ahead, r (t-20), r (t-30), ・・・・・・, r (t-70), are used as the input units. The recognized values show close agreement with the observed ones. … More As on other reliable data of hydrograph has been taken at the Mizunashi River, it is hard to verify the model by using the hydrograph of other events. However, the amounts of deposits were measured by the Ministry of Japan Construction. The applicability of the model can be checked by comparing the total amount of debris flow with the measured amounts of deposits. The volume of debris flow integrated from the predicted hydrographs show fairly good agreement with observed ones. Neural networks are useful for making runoff analyzes of debris flows.(3) In the previous study, the neural networks with back-propagation method (BP) were applied to predict the occurrence of debris flow. It was also found that this model is useful to estimate the critical rainfall. In this study, LVQ (Learning Vector Quantization) is introduced to improve the accuracy of the prediction. The LVQ and BP are applied to the debris flow at Unzen and Sakurajima Volcanoes. Comparison between the results by both methods confirms that LVQ has an advantage in prediction. The BP model is used to find the critical condition at Unzen Volcano. Validity of the method is demonstrated by the theory of the occurrence criteria of debris flow. Less

泥石流在水石河频繁发生，并因沉积大量泥沙而对云顶火山下游地区造成严重破坏。因此，泥石流预报变得十分重要。本研究取得的成果如下：(1)在昂森达克山的两个地点进行了泥石流现场观测。采用无线电测流仪和超声波水位计测量水面流速、水深和流量。泥石流流量峰值Q=195m^3/S，泥石流洪峰流量q=40m^3/S。(2)建立泥石流径流模型。1993年6月12-13日在水城河采集了泥石流流速和泥石流深度资料。学习时，将泥石流流量Q(T)和提前20-70分钟的10分钟降雨量r(t-20)、r(t-30)、···、r(t-70)作为输入单位。识别值与观测值吻合较好。…由于水石河已经取得了其他可靠的水文资料，利用其他事件的水文资料很难对模型进行验证。然而，存款的数量是由日本建筑部衡量的。通过将泥石流总量与实测泥石流堆积量进行比较，可以检验模型的适用性。从预测的过程线积分得到的泥石流体积与观测值吻合较好。神经网络可用于泥石流径流分析。(3)在以往的研究中，将神经网络与反向传播方法(BP)应用于泥石流的发生预测。研究还发现，该模型对估算临界降雨量具有一定的实用价值。为了提高预测的准确性，本文引入学习矢量量化算法。将LVQ和BP应用于云顶火山和樱岛火山的泥石流。将两种方法的预测结果进行比较，证实了LVQ在预测方面的优势。利用BP神经网络模型对温泉火山的临界状态进行了求解。通过泥石流赋存判据的理论验证了该方法的有效性。较少