Regional Multiseverity Casualty Estimation Due to Building Damage following a Mw 8.8 Earthquake Scenario in Lima, Peru

2018 ◽  
Vol 34 (4) ◽  
pp. 1739-1761 ◽  
Author(s):  
Luis Ceferino ◽  
Anne Kiremidjian ◽  
Gregory Deierlein
Keyword(s):  
Probability Distribution ◽  
Joint Probability ◽  
Building Damage ◽  
Patient Treatment ◽  
Joint Probability Distribution ◽  
Model Parameters ◽  
Probabilistic Framework ◽  
Earthquake Scenario ◽  
Selection Of

This paper presents the application of a rigorous probabilistic framework that estimates the number, severity, and distribution of casualties over a region. A brief summary of the model is included in this paper. The application is for casualties resulting from a Mw 8.8 earthquake scenario occurring on the sub-duction fault along the coastline of Lima, Peru. The case study demonstrates an application of the casualty model, including the procedures for acquiring the required information, the selection of model parameters, and a step-by-step explanation of the model-solving algorithms. The model provides an estimate of the joint probability distribution of multiseverity casualties, including spatial and across-severity correlations. This paper also shows how the model can be useful for (1) estimating 90th-percentile casualties, (2) identifying unsafe communities and structural typologies, and (3) providing evidence to support hospital collaboration policies across different districts to increase the patient treatment reliability. Additionally, the results demonstrate that empirical fatality prediction methodologies can underestimate fatality rates in countries with scarce and outdated fatality data.

Improved Locality Preserving Projection for Hyperspectral Image Classification in Probabilistic Framework

2021 ◽  
Author(s):  
Reza Seifi Majdar ◽  
Hassan Ghassemian
Keyword(s):  
Distribution Function ◽  
Probability Distribution ◽  
Probability Distribution Function ◽  
Spatial Information ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Spectral Features ◽  
Probabilistic Framework ◽  
Locality Preserving ◽  
Training Samples

Unlabeled samples and transformation matrix are two main parts of unsupervised and semi-supervised feature extraction (FE) algorithms. In this manuscript, a semi-supervised FE method, locality preserving projection in the probabilistic framework (LPPPF), to find a sufficient number of reliable and unmixed unlabeled samples from all classes and constructing an optimal projection matrix is proposed. The LPPPF has two main steps. In the first step, a number of reliable unlabeled samples are selected based on the training samples, spectral features, and spatial information in the probabilistic framework. In this way, the spectral and spatial probability distribution function is calculated for each unlabeled sample. Therefore, the spectral features and spatial information are integrated together with a joint probability distribution function. Finally, a sufficient number of unlabeled samples with the highest joint probability distribution are selected. In the second step, the selected unlabeled samples are applied to construct the transformation matrix based on the spectral and spatial information of the unlabeled samples. The adjacency graph is improved by using new weights based on spectral and spatial information. This method is evaluated on three data sets: Indian Pines, Pavia University, and Kennedy Space Center (KSC) and compared with some recent and well-known supervised, semi-supervised, and unsupervised FE methods. Various experiments demonstrate the efficiency of the LPPPF in comparison with the other FE methods. LPPPF has also considerable performance with limited training samples.

scholarly journals Accounting for dependencies in regionalized signatures for predictions in ungauged catchments

2015 ◽  
Vol 12 (6) ◽  
pp. 5389-5426 ◽  
Author(s):  
S. Almeida ◽  
N. Le Vine ◽  
N. McIntyre ◽  
T. Wagener ◽  
W. Buytaert
Keyword(s):  
Joint Probability ◽  
Joint Probability Distribution ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Independent Information ◽  
Streamflow Data ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Selection Of

Abstract. A recurrent problem in hydrology is the absence of streamflow data to calibrate rainfall-runoff models. A commonly used approach in such circumstances conditions model parameters on regionalized response signatures. While several different signatures are often available to be included in this process, an outstanding challenge is the selection of signatures that provide useful and complementary information. Different signatures do not necessarily provide independent information, and this has led to signatures being omitted or included on a subjective basis. This paper presents a method that accounts for the inter-signature error correlation structure so that regional information is neither neglected nor double-counted when multiple signatures are included. Using 84 catchments from the MOPEX database, observed signatures are regressed against physical and climatic catchment attributes. The derived relationships are then utilized to assess the joint probability distribution of the signature regionalization errors that is subsequently used in a Bayesian procedure to condition a rainfall-runoff model. The results show that the consideration of the inter-signature error structure may improve predictions when the error correlations are strong. However, other uncertainties such as model structure and observational error may outweigh the importance of these correlations. Further, these other uncertainties cause some signatures to appear repeatedly to be disinformative.

scholarly journals Accounting for dependencies in regionalized signatures for predictions in ungauged catchments

2016 ◽  
Vol 20 (2) ◽  
pp. 887-901 ◽  
Author(s):  
Susana Almeida ◽  
Nataliya Le Vine ◽  
Neil McIntyre ◽  
Thorsten Wagener ◽  
Wouter Buytaert
Keyword(s):  
Joint Probability ◽  
Joint Probability Distribution ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Independent Information ◽  
Streamflow Data ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Selection Of

Abstract. A recurrent problem in hydrology is the absence of streamflow data to calibrate rainfall–runoff models. A commonly used approach in such circumstances conditions model parameters on regionalized response signatures. While several different signatures are often available to be included in this process, an outstanding challenge is the selection of signatures that provide useful and complementary information. Different signatures do not necessarily provide independent information and this has led to signatures being omitted or included on a subjective basis. This paper presents a method that accounts for the inter-signature error correlation structure so that regional information is neither neglected nor double-counted when multiple signatures are included. Using 84 catchments from the MOPEX database, observed signatures are regressed against physical and climatic catchment attributes. The derived relationships are then utilized to assess the joint probability distribution of the signature regionalization errors that is subsequently used in a Bayesian procedure to condition a rainfall–runoff model. The results show that the consideration of the inter-signature error structure may improve predictions when the error correlations are strong. However, other uncertainties such as model structure and observational error may outweigh the importance of these correlations. Further, these other uncertainties cause some signatures to appear repeatedly to be misinformative.

scholarly journals SIMULATION OF SEA STORMS INCLUDING MULTIVARIATE STORM EVOLUTION

2018 ◽  
pp. 35
Author(s):  
Sebastián Solari ◽  
Miguel A. Losada
Keyword(s):  
Probability Distribution ◽  
Autoregressive Model ◽  
Joint Probability ◽  
New Method ◽  
Joint Probability Distribution ◽  
Northern Coast ◽  
Vector Autoregressive Model ◽  
Vector Autoregressive ◽  
Storm Evolution

A new method for the simulation of storms is proposed which takes into account the multivariate evolution of the storms, allowing to innovate in the form of each simulated storm, for all the variables involved. The method is based on two novel aspects: (a) measured storms are grouped using clusters techniques and a set of average evolution forms is defined for each cluster, one for each of the variables involved, and (b) a Vector Autoregressive model is fitted to the differences between the average evolution of each variable and the actual measured evolutions. The ability of the methodology to properly reproduce the joint probability distribution of all the variables involved is demonstrated for a case study at the mid Rio de la Plata northern coast.

scholarly journals Multidimensional characterisation of time-dependent image data: A case study for the peripheral nervous system in ageing mice

2021 ◽  
Author(s):  
Matthias Weber ◽  
Thomas Wilhelm ◽  
Volker Schmidt
Keyword(s):  
Joint Probability ◽  
Image Data ◽  
Joint Probability Distribution ◽  
Model Parameters ◽  
Myelin Sheaths ◽  
Local Thresholding ◽  
Segmentation Algorithms ◽  
Copula Approach ◽  
The Given

Segmentation of µm-resolution image data of irregularly shaped objects poses challenges to existing segmentation algorithms. This is especially true, when imperfections like noise, uneven lightning or traces of sample preparation are present in the image data. In this paper, considering electron micrographs of femoral quadriceps nerve sections of mice, a segmentation method to extract single axons surrounded by myelin sheaths is developed which is able to cope with various imperfections and artefacts. This approach successfully combines established methods like local thresholding and marker-based watershed transform to achieve a reliable segmentation of the given data. Indeed, the resulting segmentation map can be used to quantitatively determine geometrical characteristics of the axons and myelin sheaths. This is exemplified by modelling the joint probability distribution of axon area and myelin sphericity using a parametric copula approach, and by analysing the evolution of the model parameters for image data obtained from mice of different ages.

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