scholarly journals Physically-based landslide prediction over a large region: Scaling low-resolution hydrological model results for high-resolution slope stability assessment

2020 ◽  
Vol 124 ◽  
pp. 104607 ◽  
Author(s):  
Sheng Wang ◽  
Ke Zhang ◽  
Ludovicus P.H. van Beek ◽  
Xin Tian ◽  
Thom A. Bogaard
Keyword(s):  
High Resolution ◽  
Slope Stability ◽  
Hydrological Model ◽  
Large Region ◽  
Stability Assessment ◽  
Low Resolution ◽  
Landslide Prediction ◽  
Physically Based

scholarly journals HIRESSS: a physically based slope stability simulator for HPC applications

2013 ◽  
Vol 13 (1) ◽  
pp. 151-166 ◽  
Author(s):  
G. Rossi ◽  
F. Catani ◽  
L. Leoni ◽  
S. Segoni ◽  
V. Tofani
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Slope Stability ◽  
Real Time ◽  
Unsaturated Soil ◽  
Hydrological Model ◽  
Pressure Head ◽  
Stability Model ◽  
Physically Based ◽  
Geotechnical Stability

Abstract. HIRESSS (HIgh REsolution Slope Stability Simulator) is a physically based distributed slope stability simulator for analyzing shallow landslide triggering conditions in real time and on large areas using parallel computational techniques. The physical model proposed is composed of two parts: hydrological and geotechnical. The hydrological model receives the rainfall data as dynamical input and provides the pressure head as perturbation to the geotechnical stability model that computes the factor of safety (FS) in probabilistic terms. The hydrological model is based on an analytical solution of an approximated form of the Richards equation under the wet condition hypothesis and it is introduced as a modeled form of hydraulic diffusivity to improve the hydrological response. The geotechnical stability model is based on an infinite slope model that takes into account the unsaturated soil condition. During the slope stability analysis the proposed model takes into account the increase in strength and cohesion due to matric suction in unsaturated soil, where the pressure head is negative. Moreover, the soil mass variation on partially saturated soil caused by water infiltration is modeled. The model is then inserted into a Monte Carlo simulation, to manage the typical uncertainty in the values of the input geotechnical and hydrological parameters, which is a common weak point of deterministic models. The Monte Carlo simulation manages a probability distribution of input parameters providing results in terms of slope failure probability. The developed software uses the computational power offered by multicore and multiprocessor hardware, from modern workstations to supercomputing facilities (HPC), to achieve the simulation in reasonable runtimes, compatible with civil protection real time monitoring. A first test of HIRESSS in three different areas is presented to evaluate the reliability of the results and the runtime performance on large areas.

scholarly journals Sensitivity and Performance Analyses of the Distributed Hydrology–Soil–Vegetation Model Using Geomorphons for Landform Mapping

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2032
Author(s):  
Pâmela A. Melo ◽  
Lívia A. Alvarenga ◽  
Javier Tomasella ◽  
Carlos R. Mello ◽  
Minella A. Martins ◽  
...  
Keyword(s):  
Complex Terrain ◽  
Overland Flow ◽  
Hydrological Model ◽  
Model Performance ◽  
Soil Parameters ◽  
Vegetation Model ◽  
Landform Classification ◽  
Physically Based ◽  
And Performance ◽  
Streamflow Simulation

Landform classification is important for representing soil physical properties varying continuously across the landscape and for understanding many hydrological processes in watersheds. Considering it, this study aims to use a geomorphology map (Geomorphons) as an input to a physically based hydrological model (Distributed Hydrology Soil Vegetation Model (DHSVM)) in a mountainous headwater watershed. A sensitivity analysis of five soil parameters was evaluated for streamflow simulation in each Geomorphons feature. As infiltration and saturation excess overland flow are important mechanisms for streamflow generation in complex terrain watersheds, the model’s input soil parameters were most sensitive in the “slope”, “hollow”, and “valley” features. Thus, the simulated streamflow was compared with observed data for calibration and validation. The model performance was satisfactory and equivalent to previous simulations in the same watershed using pedological survey and moisture zone maps. Therefore, the results from this study indicate that a geomorphologically based map is applicable and representative for spatially distributing hydrological parameters in the DHSVM.

scholarly journals Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1013
Author(s):  
Sayan Maity ◽  
Mohamed Abdel-Mottaleb ◽  
Shihab S. Asfour
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Surveillance Video ◽  
Low Resolution ◽  
Model Free ◽  
Face Images

Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

scholarly journals Super Resolution Digital Image Correlation (SR-DIC): an Alternative to Image Stitching at High Magnifications

2021 ◽  
Author(s):  
R. S. Hansen ◽  
D. W. Waldram ◽  
T. Q. Thai ◽  
R. B. Berke
Keyword(s):  
High Resolution ◽  
Digital Image Correlation ◽  
Spatial Resolution ◽  
Digital Image ◽  
Super Resolution ◽  
Image Correlation ◽  
Low Resolution ◽  
Strain Measurements ◽  
Resolution Image ◽  
High Resolution Image

Abstract Background High-resolution Digital Image Correlation (DIC) measurements have previously been produced by stitching of neighboring images, which often requires short working distances. Separately, the image processing community has developed super resolution (SR) imaging techniques, which improve resolution by combining multiple overlapping images. Objective This work investigates the novel pairing of super resolution with digital image correlation, as an alternative method to produce high-resolution full-field strain measurements. Methods First, an image reconstruction test is performed, comparing the ability of three previously published SR algorithms to replicate a high-resolution image. Second, an applied translation is compared against DIC measurement using both low- and super-resolution images. Third, a ring sample is mechanically deformed and DIC strain measurements from low- and super-resolution images are compared. Results SR measurements show improvements compared to low-resolution images, although they do not perfectly replicate the high-resolution image. SR-DIC demonstrates reduced error and improved confidence in measuring rigid body translation when compared to low resolution alternatives, and it also shows improvement in spatial resolution for strain measurements of ring deformation. Conclusions Super resolution imaging can be effectively paired with Digital Image Correlation, offering improved spatial resolution, reduced error, and increased measurement confidence.

Freeway Slope Stability Evaluation Based on Multi-Level Fuzzy Neural Network

2012 ◽  
Vol 430-432 ◽  
pp. 1700-1703
Author(s):  
Yan Kai Wu ◽  
Xian Song Sang ◽  
Bin Niu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Slope Stability ◽  
Basic Principle ◽  
Fuzzy Neural Network ◽  
Stability Assessment ◽  
High Slope ◽  
Fuzzy Neural ◽  
Multi Level ◽  
Artificial Neural

On the basis of introduced basic principle of fuzzy-artificial neural network, this article constructed a slope stability assessment index system with multi-level fuzzy neural network, and made detailed evaluation criterion according to the assessment characteristics of slope stability. Through introducing the basic principle of multi-level comprehensive assessment from fuzzy mathematics and artificial neural network theory, it overcomes the defect of difficult to be quantified in evaluation process of slope stability. Therefore, it can be better to deal with some uncertain problems occurred in the slope stability assessment process, and as much as possible to express all factors influencing slope stability really and objectively. We selected 20 single factor evaluation indexes to assess slope stability based on surveying the high slope stability in Mian county-Ningqiang county freeway section. It took "normal distribution model function" as a membership function to develop a program with the model of fuzzy neural network. Furthermore, we took 30 typical slope examples as training sample to conduct effectiveness test and feedback test for the program. After the precision requirement was met, we used the program to evaluate 21 high slope examples and compared the results with the ones solved by traditional mechanical methods. The coincidence degree by using two kinds of methods to assess the same slope stability is 76.2%.

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