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We explore the effect of dam wall collapse scenarios on the extent and speed of inundation resulting from a dam-break by taking advantage of the easy inclusion of dynamic moving objects in the smoothed particle hydrodynamics (SPH) method. Insight into the degree of practical variation in the flood behaviours that can be generated by both the presence of dam wall fragments and the collapse sequence is investigated via a case study using key locations downstream of the Geheyan Dam, Hubei, China. Scenarios considered include ones initiated by seismic fracture, overtopping and foundation failure. The nature of the scenario determines the rate of increase of area of the breach and therefore the discharge flow rate, which in turn strongly influences the extent and timing of inundation for timescales of up to 1 h and distances of up to 10 km from the dam wall. Beyond this, the influence of the specific scenario declines. The presence of the dam wall fragments in the flow strongly influences the pattern of flooding and can protect some locations and lead to increased flooding in others. The flow in all cases has a complex three-dimensional structure, with multiple hydraulic jumps due to variations in the valley floor gradient and width. The SPH method therefore provides the ability to include realistic variations in the dam-break mechanism, thereby leading to more informed risk analysis planning before a dam-break occurs. The methodology for including these SPH flood predictions into a geographical information system (GIS) is also described. One of the collapse scenarios is used to demonstrate the ability of the GIS to then make predictions of expected flood area and the extent of flooding of villages, towns and key infrastructure. The inclusion of SPH into a GIS framework will allow the modelling to be used for disaster management following a dam-break event.

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ABSTRACT:Aiming at the significant loss of high frequency information during reducing noise and the pixel independence in change detection of multi-scale remote sensing image, an unsupervised algorithm is proposed based on the combination between Dual-tree Complex Wavelet Transform (DT-CWT) and Markov random Field (MRF) model. This method first performs multi-scale decomposition for the difference image by the DT-CWT and extracts the change characteristics in high-frequency regions by using a MRF-based segmentation algorithm. Then our method estimates the final maximum a posterior (MAP) according to the segmentation algorithm of iterative condition model (ICM) based on fuzzy c-means(FCM) after reconstructing the high-frequency and low-frequency subbands of each layer respectively. Finally, the method fuses the above segmentation results of each layer by using the fusion rule proposed to obtain the mask of the final change detection result. The results of experiment prove that the method proposed is of a higher precision and of predominant robustness properties.

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