Application of back-propagation networks in debris flow prediction   总被引：6，自引：0，他引：6  

Tung-Chueng Chang  Ru-Jen Chao   《Engineering Geology》2006,85(3-4):270-280

Debris flows have caused serious loss of human lives and a lot of damage to properties in Taiwan over the past decades. Moreover, debris flows have brought massive mud causing water pollution in reservoirs and resulted in water shortage for daily life locally and affected agricultural irrigation and industrial usages seriously. A number of methods for prediction of debris flows have been studied. However, the successful prediction ratio of debris flows cannot always maintain a stable and reliable level. The objective of this study is to present a stable and reliable analytical model for occurrence predictions of debris flows. This study proposes an Artificial Neural Networks (ANN) model that was constructed by seven significant factors using back-propagation (BP) algorithm. These seven factors include (1) length of creek, (2) average slope, (3) effective watershed area, (4) shape coefficient, (5) median size of soil grain, (6) effective cumulative rainfall, and (7) effective rainfall intensity. A total of 178 potential cases of debris flows collected in eastern Taiwan were fed into the ANN model for training and testing. The average ratio of successful prediction reaching 93.82% demonstrates that the presented ANN model with seven significant factors can provide a stable and reliable result for the prediction of debris flows in hazard mitigation and guarding systems.  相似文献   

A north-south section across the Queyras Schistes lustrés (Piedmont zone, Western Alps): Syn-collision refolding of a subduction wedge     

Pierre Tricart  Stephane Schwartz 《Eclogae Geologicae Helvetiae》2006,99(3):429-442

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Mathematical modeling of overthrusting fault as a cause of andalusite-kyanite metamorphic zoning in the Yenisei Ridge     

S. N. Korobeinikov  O. P. Polyansky  I. I. Likhanov  V. G. Sverdlova  V. V. Reverdatto 《Doklady Earth Sciences》2006,408(4):652-656

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Sheathlike metasomatic garnet crystals from the Maksyutovo metamorphic complex, the southern Urals     

V. V. Zhdanov  A. E. Glikin 《Doklady Earth Sciences》2006,409(5):814-817

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Petrology of postorogenic granitoids of the northern Baltic Shield     

V. R. Vetrin  N. G. Berezhnaya  N. V. Rodionov 《Doklady Earth Sciences》2006,411(9):1476-1479

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Origin of Pleistocene-Holocene ashes of the Russian northeast based on trace and rare earth element data     

V. G. Sakhno  L. I. Bazanova  O. Yu. Glushkova  I. V. Melekestsev  V. V. Ponomareva  A. A. Surnin  J. Olaf 《Doklady Earth Sciences》2006,411(9):1351-1356

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Integrated stratigraphy and paleoenvironmental reconstruction of the Miocene series of the Chateauredon Dome, S.E. France     

Anne Bialkowski  Jean-Jacques Chateauneuf  Isabelle Cojan  Hugues Bauer 《Eclogae Geologicae Helvetiae》2006,99(1):1-15

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Variations in the electric field and turbulence in the surface atmospheric layer     

B. M. Koprov  S. V. Anisimov  V. M. Koprov 《Doklady Earth Sciences》2006,407(2):312-316

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Duration of migmatite formation in the granulite-facies metamorphism zone of Svecofennides of the Ladoga region, southeastern Baltic Shield     

Sh. K. Baltybaev  O. A. Levchenkov  V. A. Glebovitsky  L. K. Levskii  E. V. Kuz&#;mina  A. F. Makeev  S. Z. Yakovleva 《Doklady Earth Sciences》2006,407(2):271-274

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Bayesian Spatial Modelling of Gamma Ray Count Data     

Daniela Leonte  David J. Nott 《Mathematical Geology》2006,38(2):135-154

Gamma ray logging is a method routinely employed by geophysicists and environmental engineers in site geology evaluations. Modelling of gamma ray data from individual boreholes assists in the local identification of major lithological changes; modelling these data from a network of boreholes assists with lithological mapping and spatial stratigraphic correlation. In this paper we employ Bayesian spatial partition models to analyse gamma ray data spatially. In particular, a spatial partition is defined via a Voronoi tessellation and the mean intensity is assumed constant in each cell of the partition. The number of vertices generating the tessellation as well as the locations of vertices are assumed unknown, and uncertainty about these quantities is described via a hierarchical prior distribution. We describe the advantages of the spatial partition modelling approach in the context of smoothing gamma ray count data and describe an implementation that may be extended to the fitting of a more general model than a constant mean within each cell of the partition. As an illustration of the methodology we consider a data set collected from a network of eight boreholes, which is part of a geophysical study to assist in mapping the lithology of a site. Gamma ray logs are linked with geological information from cores and the spatial analysis of log data assists with predicting the lithology at unsampled locations.  相似文献