1995 Fiscal Year Final Research Report Summary
DEVELOPMENT OF FORCASTING AND WARNING SYSTEM FOR DEBRIS FLOWS,MT.UNZENDAKE
| Project/Area Number |
06558058
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| Research Category |
Grant-in-Aid for Developmental Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
Natural disaster science
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| Research Institution | KYUSHU UNIVERSITY |
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Principal Investigator |
HIRANO Muneo KYUSHU UNIVERSITY,FACULTY OF ENGINEERING,DEPARTMENT OF CIVIL ENGINEERING,PROFESSOR, 工学部, 教授 (50037850)
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| Co-Investigator(Kenkyū-buntansha) |
MORIYAMA Toshiyuki KYUSHU UNIVERSITY,FACULTY OF ENGINEERING,DEPARTMENT OF CIVIL ENGINEERING,RESEARC, 工学部, 助手 (50136537)
HASHIMOTO Haruyuki KYUSHU UNIVERSITY,FACULTY OF ENGINEERING,DEPARTMENT OF CIVIL ENGINEERING,ASSOCIA, 工学部, 助教授 (70117216)
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| Project Period (FY) |
1994 – 1995
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| Keywords | DEBRIS FLOWS / MT.UNZEN / FIFLD MEASUREMENT / PRECIPITATION RADAR / FORCASTING SYSTEM / WARNING SYSTEM |
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| Research Abstract |
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
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Research Products
(6 results)
