| Summary: | 碩士 === 國立暨南國際大學 === 土木工程學系 === 98 === Abstract
In this study, statistical analysis and to extract information on the major influencing factors to make hair debris, to establish an index of induced debris flow, to map out the probability distribution of debris flow, and compare the discriminant analysis, Logistic regression and Gist neural network three kinds of potential debris flow prediction models to provide a new reference landslide risk management approach. And the practical application of this method, before the occurrence of more landslides after the earthquake type of change.
In this study, the database 684 under the debris flow data, to analyze the eight factors (stream flow length, watershed area, landslide area, landslide rate, the average gradient of riverbed, shape coefficient, watershed average gradient, and geology), according to the eight factors of independence between the selection of the four independent factors, then Mann - Whitney U test with four factors and significant debris flow occurrence. Finally, principal component analysis extracted the principal components, to develop cultural indicators of debris flow occurred image. And with maximum rainfall intensity I and the total accumulated rainfall R define the product of debris flow rainfall-driven indicators image . A total of five to the text definition of composite indicator, three types of rainfall-driven targets. Get a overall index of 15 farming and the combination of rainfall-driven targets, will image. Value as the horizontal axis, image. Value as the axis of the occurrence of debris flow can be obtained or distribution. 15 groups image And image. The combination of three types of forecasting model potential analysis and comparison, the average accuracy of 75%, have been used image. Selected watershed area, collapse rate, the average stream bed slope and geology, image. Selected multiplied by the total accumulated rainfall maximum rainfall intensity, follow-up study of this combination is the most appropriate assessment factor. And to discuss this discussion earthquake probability of occurrence of debris flow. Made after the earthquake, with or without occurrence of debris flow stream image. Value had more distinct segments that conclusion. Value based on the distribution of information to assess the probability of earthquake occurrence before and after the debris flow changes, the study found that before the earthquake (of debris higher probability of points are in slightly after the earthquake with an incidence of 0.1 to 0.3 upgrade. But before the earthquake incidence is in the range of point and band, then with the increase in rainfall probability of debris flow have considerable variation.
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