In risk assessment, the exposure component describes the elements exposed to the natural hazards and susceptible to damage or loss, while the vulnerability component defines the likelihood to incur...

Variable resolution probabilistic modeling of residential exposure and vulnerability for risk applications

Exposure is reported to be the biggest determinant of disaster risk, it is continuously growing and by monitoring and understanding its variations over time it is possible to address disaster risk reduction, also at the global level. This work uses Earth observation image archives to derive information on human settlements that are used to quantify exposure to five natural hazards. This paper first summarizes the procedure used within the global human settlement layer (GHSL) project to extract global built-up area from 40 year deep Landsat image archive and the procedure to derive global population density by disaggregating population census data over built-up area. Then it combines the global built-up area and the global population density data with five global hazard maps to produce global layers of built-up area and population exposure to each single hazard for the epochs 1975, 1990, 2000, and 2015 to assess changes in exposure to each hazard over 40 years. Results show that more than 35% of the global population in 2015 was potentially exposed to earthquakes (with a return period of 475 years); one billion people are potentially exposed to floods (with a return period of 100 years). In light of the expansion of settlements over time and the changing nature of meteorological and climatological hazards, a repeated acquisition of human settlement information through remote sensing and other data sources is required to update exposure and risk maps, and to better understand disaster risk and define appropriate disaster risk reduction strategies as well as risk management practices. Regular updates and refined spatial information on human settlements are foreseen in the near future with the Copernicus Sentinel Earth observation constellation that will measure the evolving nature of exposure to hazards. These improvements will contribute to more detailed and data-driven understanding of disaster risk as advocated by the Sendai Framework for Disaster Risk Reduction.

https://www.mdpi.com/2072-4292/10/9/1378/pdf

Remote Sensing Derived Built-Up Area and Population Density to Quantify Global Exposure to Five Natural Hazards over Time

Seismic loss estimation software: A comprehensive review of risk assessment steps, software development and limitations

This study evaluates the potential of object-based image analysis in combination with supervised machine learning to identify urban structure type patterns from Landsat Thematic Mapper TM images. The main aim is to assess the influence of several critical choices commonly made during the training stage of a learning machine on the classification performance and to give recommendations for classifier-dependent intelligent training. Particular emphasis is given to assess the influence of size and class distribution of the training data, the approach of training data sampling user-guided or random and the type of training samples squares or segments on the classification performance of a Support Vector Machine SVM. Different feature selection algorithms are compared and segmentation and classifier parameters are dynamically tuned for the specific image scene, classification task, and training data. The performance of the classifier is measured against a set of reference data sets from manual image interpretation and furthermore compared on the basis of landscape metrics to a very high resolution reference classification derived from light detection and ranging lidar measurements. The study highlights the importance of a careful design of the training stage and dynamically tuned classifier parameters, especially when dealing with noisy data and small training data sets. For the given experimental set-up, the study concludes that given optimized feature space and classifier parameters, training an SVM with segment-shaped samples that were sampled in a guided manner and are balanced between the classes provided the best classification results. If square-shaped samples are used, a random sampling provided better results than a guided selection. Equally balanced sample distributions outperformed unbalanced training sets.

/pdf/object-based-urban-structure-type-pattern-recognition-from-2gvf4hoouf.pdf

Object-based urban structure type pattern recognition from Landsat TM with a Support Vector Machine

Due to their expected high seismic resistance, a significant number of tunnel formwork buildings have been built over the past few decades, especially in regions of high seismic hazard. However, only few real data analyses have been reported and are available so far. Different techniques, including real data analysis, numerical simulations, and their combination, are employed to investigate and compare their potentials for estimating different aspects of a tunnel formwork building. This study is based on data from a 16-story residential building in Istanbul, Turkey. The real data analysis of the building’s dynamic characteristics is based on both the vibrational (spectral analysis) and waveform (deconvolution interferometry) approaches, which yield information on the soil–structure system and the fixed-base building. For this purpose, dense ambient vibration/generated source measurements and earthquake recordings of both temporary and permanent networks are used. Furthermore, finite element analysis (FEA) of the building is carried out. The order of the first bending and torsional modes is reversed in the results of the numerical simulations compared to those from the real data. This can be attributed to the simplifications and assumptions made in the numerical simulations of tunnel formwork structures. It emphasizes the importance of analyzing real data for better constraining numerical simulations. Finally, the response of the building (synthetic seismograms) to a real data input is calculated using FEA. Deconvolved wavefields of synthetic and the real data are estimated. Despite the changed order of the modes, the deconvolved wavefields for both cases match very well.

Real data and numerical simulations-based approaches for estimating the dynamic characteristics of a tunnel formwork building

This work provides an insight into the development of the first harmonized exposure model for Central Asia. The model was derived in the frame of the Earthquake Model Central Asia (EMCA) project, which is the regional initiative for Central Asia to the Global Earthquake Model (GEM). The EMCA exposure model combines commonly used data sources and acquisition techniques (e.g., rapid visual screening) with novel rapid assessment approaches (e.g., satellite remote sensing and omnidirectional imaging) in the framework of an integrated sampling scheme and stores the data in a multi-resolution spatio-temporal database. The exposure model implements a newly developed building typology, harmonized for the five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan) and follows the international building taxonomy of the GEM. Emphasis is given to the multi-scale nature and the temporal dynamics of exposure data. Results from a selected urban area are provided to illustrate the current state of the continuously updated exposure model.

/pdf/towards-a-cross-border-exposure-model-for-the-earthquake-2etvpt0pvq.pdf

Towards a cross-border exposure model for the Earthquake Model Central Asia

Within the framework of the EMCA - Earthquake Model Central Asia - project, the cities of Bishkek (Kyrgyzstan) and Dushanbe (Tajikistan) were selected for building monitoring using measurements of seismic noise to obtain the dynamical properties of the buildings. Eight buildings of different construction types, date of construction and building height, both, Soviet-era and recently constructed buildings, were instrumented for a period of a few hours. In this study, an overview of the experiment is given, including a short description of each monitored building’s structure and the performed installation. Preliminary results for a representative Soviet-era residential building in Dushanbe are presented. Modal analysis is performed using the Frequency Domain Decomposition (FDD) method to estimate the natural frequencies and the mode shapes. The wave propagation velocities in the two directions along the building axes are determined by an interferometric approach. Although the experimental set-up was not the optimal, valuable information about the dynamical characteristics of the buildings were still obtained.

/pdf/building-monitoring-in-bishkek-and-dushanbe-by-the-use-of-2ssh66z869.pdf

Building monitoring in Bishkek and Dushanbe by the use of ambient vibration analysis

This study is intended for decision-makers in the field of water resources use (irrigation and hydropower) and for emergency prevention authorities. Another problem remains intensive technogenic pollution of the environment, including water bodies, and one of the main tasks in the conditions of technogenesis is the development of predictive models for the migration of polluting chemical elements and substances in the environment. To calculate the runoff of the river the HBV3-ETH9 hydrological model and the CMIP5 RCP4.5 and RCP8.5 climate projections were used, according to which the air temperature is expected to increase by 2.3 and 4.3 °C, respectively, in annual precipitation - by 10% of the norm. In the period 2023-2080, a gradual increase in annual runoff is expected by 9-23%, and according to the extreme scenario, by 9-36% of the norm, which is associated with intensive glacier water loss, seasonal snowmelt and rainfall. In the future, the passage of two flood peaks (in June and July) is possible. The increase in water content in April and May will contribute to increased mudflow and flood activity. 

Methods for assessing hydrological and geoecological risks based on modelling

Buildings data from Remote Rapid Visual survey (RRVS) for exposure modelling in Kyrgyzstan and Tajikistan

Study of the effects of the meteorological parameters on the kyzylsu run-off formation and the components

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