scholarly journals Regional based exposure models to account for local building typologies

2021 ◽  
Author(s):  
G. Tocchi ◽  
M. Polese ◽  
M. Di Ludovico ◽  
A. Prota
Keyword(s):  
Seismic Risk ◽  
Large Scale ◽  
Census Data ◽  
Construction Material ◽  
Regional Scale ◽  
Exposure Model ◽  
Masonry Building ◽  
Building Inventory ◽  
Exposure Models ◽  
Regional Vulnerability

AbstractThe development of building inventory is a fundamental step for the evaluation of the seismic risk at territorial scale. Census data are usually employed for building inventory in large scale application and their use requires suitable rules to assign buildings typologies to vulnerability classes, that is an exposure model specific for the considered vulnerability model. Several exposure models are developed proposing class assignment rules that are calibrated on building typological data available from post-earthquake survey data. However, this approach has the drawback of being based on data from specific geographic areas that have been hit by damaging earthquakes. Indeed, the distribution of building typologies can vary greatly for different areas of a country and the diffusion of one building’s typology rather than another one may depend on the availability of construction material in the area, the evolution of construction techniques and the codes in force at the time of construction. This paper aims to improve the exposure modelling at regional scale, investigating the variability of masonry building typologies distribution. It proposes a methodology to recalibrate the exposure models at regional scale and evaluates the influence of the improved characterization of regional vulnerability on damage and risk assessment. The study shows that the analysis of local building typologies may strongly impact on the evaluation of the seismic risk at territorial scale.

Bayesian downscaling of building exposure models with remote sensing and ancillary information

2020 ◽  
Author(s):  
Raquel Zafrir ◽  
Massimiliano Pittore ◽  
Juan Camilo Gomez- Zapata ◽  
Patrick Aravena ◽  
Christian Geiß
Keyword(s):  
Remote Sensing ◽  
Census Data ◽  
Regional Scale ◽  
Probabilistic Approach ◽  
Residential Building ◽  
Bayesian Updating ◽  
Exposure Model ◽  
Auxiliary Data ◽  
Building Inventory ◽  
Exposure Models

<p>Residential building exposure models for risk and loss estimations related to natural hazards are usually defined in terms of specific schemas describing mutually exclusive, collectively exhaustive (MECE) classes of buildings. These models are derived from: (1) the analysis of census data or (2) by means of individual observations in the field. In the first case, expert elicitation has been conventionally used to classify the building inventory into particular schemas, usually aggregated over geographical administrative units whose size area and shape are country-specific. In the second case, especially for large urban areas, performing a visual inspection of every building in order to assign a class according to the specific schema used is a highly time- and resource intensive task, often simply unfeasible.</p> <p>Remote sensing data based on the analysis of satellite imagery has proved successful in integrating large-scale information on the built environment and as such can provide valuable vulnerability-related information, although often lacking the level of spatial and thematic resolution requested by multi-hazard applications. Volunteered Geo Information (VGI) data can also prove useful in this context, although in most cases only geometric attributes (shape of the building footprint) and some occupancy information are recorded thus leaving out most of the building attributes controlling the vulnerability of the structures to the different hazards. An additional drawback of VGI is the incompleteness of the information, which is based on the unstructured efforts of voluntary mappers.</p> <p>Former efforts have been proposing a top-down/bottom-up approach moving from regional scale to neighbourhood and per-building scale, based on the analysis and integration of different data sources at increasing spatial resolutions and thematic detail. Following the same principle, this work focuses on the downscaling of already existing building exposure models based on census data making use of a probabilistic approach based on Bayesian updating. Different aggregation models can be taken into account to increase the spatial resolution of the building exposure model, also including variable-resolution models based on geostatistical approaches. Land-use masks are first generated after a supervised classification of Sentinel-2 images, in order to better relate the built- up area to meaningful geographical entities. Two independent statistical models are then created based on prior input information. Maximum likelihood estimations are obtained for each model. Two types of auxiliary data have been employed in order to constrain the downscaling via a specific likelihood term in the Bayesian updating: 1) building footprints area from the open-source-volunteered geo-information OpenStreetMaps  and 2) built-up height and density estimators based on remote sensing developed by the DLR (the German Aerospace Agency).</p> <p>This approach, developed within the scope of the RIESGOS, was tested in Valparaiso and Viña del Mar (Chile) where the residential building exposure model proposed by the GEM-SARA project has been downscaled. The performance of the different auxiliary data were separately tested and compared. An independent building survey has also been carried out by experts from CIGIDEN (Chile) using a Rapid Remote Visual Screening Survey and used for preliminary validation of the approach.</p>

Development of multi-hazard exposure models from individual building observations for multi-risk assessment purposes

2020 ◽  
Author(s):  
Simantini Shinde ◽  
Juan Camilo Gomez- Zapata ◽  
Massimiliano Pittore ◽  
Orlando Arroyo ◽  
Yvonne Merino- Peña ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Seismic Risk ◽  
Large Scale ◽  
Residential Building ◽  
Expert Elicitation ◽  
Exposure Model ◽  
Specific Reference ◽  
Exposure Models ◽  
Hazard Exposure ◽  
Administrative Units

<p>The modelling of residential building portfolio exposure model for risk and loss estimations due to natural hazards often do not receive as much attention as other components in the risk chain (e.g. hazard intensity distribution, physical vulnerability). Large-scale (nation or region-wide) exposure models, for instance, are often based on information derived from census and aggregated over geographical administrative units. Moreover, it is customary to employ specific exposure/vulnerability schemas that entail a set of mutually exclusive, collectively exhaustive (MECE) building classes, each associated with a fragility/vulnerability model focusing on the specific reference hazard (e.g. HAZUS).</p> <p>In order to improve the reliability of these models, particularly when the composition of the portfolio is expected to be heterogeneous, individual building observations may be required. This process is relevant in order to constrain and validate the underlying model assumptions. The assignment of  single-hazard building classes within a given schema is usually obtained through expert elicitation (e.g., a skilled surveyor). However, if the very same building has to be classified under another vulnerability schema, either for the same hazard (e.g. EMS98 and HAZUS for seismic risk) or, in a multi-risk context, for a different hazard (e.g. tsunami, lahars), this might require a different expertise and the uncertainty of the resulting models could even increase.</p> <p>We propose an innovative method to decouple the collection of exposure information from the development of exposure models in terms of specific vulnerability classes (schemas). Taking advantage of the methodology suggested by Pittore et al., 2018, individual building attributes are observed in the field for a set of surveyed buildings and described in terms of the GEM v2.0 taxonomy,  a widely used and well-established faceted building taxonomy (Brzev et al., 2013). The assignment of a class is carried out in a post-processing stage and within a fully probabilistic framework by evaluating the level of compatibility between the observed building attributes and the classes available within the considered schema.</p> <p>The proposed methodology has been exemplified in Chile and Peru within the framework of the RIESGOS project. Expert structural engineers from CIGIDEN (Chile) and the Universidad de la Sabana (Colombia) carried out a Rapid Remote Visual Screening Survey using the RRVS web tool (e.g. Haas et al., 2016). In the case of seismic risk we focused on three schemas, namely SARA (a custom schema developed within the GEM-SARA Project in South America), and the well-known EMS-98 and HAZUS. The tsunami-focused schema proposed by Suppasri et al. (2013) has been also implemented.</p> <p>Preliminary results for Gran Valparaiso (Chile) and Metropolitan Lima (Peru) study areas show the potential of the proposed methodology for streamlining the development of multi-hazard exposure models and significantly improving the transparency of the risk assessment procedures and the propagation of related uncertainties. The importance of extending the building taxonomy to encompass multi-hazard attributes is also discussed.</p>

Development of An Exposure Model for Bridge Structures in Northern Algeria

2018 ◽  
Author(s):  
Andrés Abarca ◽  
Ricardo Monteiro
Keyword(s):  
Risk Assessment ◽  
Large Scale ◽  
Risk Mitigation ◽  
Regional Scale ◽  
Mitigation Measures ◽  
Exposure Model ◽  
Building Stock ◽  
Earthquake Scenarios ◽  
Northern Algeria ◽  
Bridge Structures

In recent years, the use of large scale seismic risk assessment has become increasingly popular to evaluate the fragility of a specific region to an earthquake event, through the convolution of hazard, exposure and vulnerability. These studies tend to focus on the building stock of the region and sometimes neglect the evaluation of the infrastructure, which has great importance when determining the ability of a social group to attend to a disaster and to eventually resume normal activities. This study, developed within the scope of the EU-funded project ITERATE (Improved Tools for Disaster Risk Mitigation in Algeria), focuses on the proposal of an exposure model for bridge structures in Northern Algeria. The proposed model was developed using existing national data surveys, as well as satellite information and field observations. As a result, the location and detailed characterization of a significant share of the Algeria roadway bridge inventory was developed, as well as the definition of a taxonomy that is able to classify the most common structural systems used in Algerian bridge construction. The outcome of this study serves as input to estimate the fragility of the bridge infrastructure inventory and, furthermore, to the overall risk assessment of the Northern Algerian region. Such fragility model will, in turn, enable the evaluation of earthquake scenarios at a regional scale and provide valuable information to decision makers for the implementation of risk mitigation measures.

scholarly journals Classification of Recreation Opportunity Spectrum Using Night Lights for Evidence of Humans and POI Data for Social Setting

2021 ◽  
Vol 13 (14) ◽  
pp. 7782
Author(s):  
Wenjing Zeng ◽  
Yongde Zhong ◽  
Dali Li ◽  
Jinyang Deng
Keyword(s):  
Large Scale ◽  
Regional Scale ◽  
Hunan Province ◽  
Social Settings ◽  
Recreation Opportunity Spectrum ◽  
City District ◽  
Night Lights ◽  
Recreational Resources ◽  
Metropolitan Counties ◽  
Land Types

The recreation opportunity spectrum (ROS) has been widely recognized as an effective tool for the inventory and planning of outdoor recreational resources. However, its applications have been primarily focused on forest-dominated settings with few studies being conducted on all land types at a regional scale. The creation of a ROS is based on physical, social, and managerial settings, with the physical setting being measured by three criteria: remoteness, size, and evidence of humans. One challenge to extending the ROS to all land types on a large scale is the difficulty of quantifying the evidence of humans and social settings. Thus, this study, for the first time, developed an innovative approach that used night lights as a proxy for evidence of humans and points of interest (POI) for social settings to generate an automatic ROS for Hunan Province using Geographic Information System (GIS) spatial analysis. The whole province was classified as primitive (2.51%), semi-primitive non-motorized (21.33%), semi-primitive motorized (38.60%), semi-developed natural (30.99%), developed natural (5.61%), and highly developed (0.96%), which was further divided into three subclasses: large-natural (0.63%), small natural (0.27%), and facilities (0.06%). In order to implement the management and utilization of natural recreational resources in Hunan Province at the county (city, district) level, the province’s 122 counties (cities, districts) were categorized into five levels based on the ROS factor dominance calculated at the county and provincial levels. These five levels include key natural recreational counties (cities, districts), general natural recreational counties (cities, districts), rural counties (cities, districts), general metropolitan counties (cities, districts), and key metropolitan counties (cities, districts), with the corresponding numbers being 8, 21, 50, 24, and 19, respectively.

scholarly journals A High-Speed, Light-Weight Scalar Magnetometer Bird for km Scale UAV Magnetic Surveying: On Sensor Choice, Bird Design, and Quality of Output Data

2021 ◽  
Vol 13 (4) ◽  
pp. 649
Author(s):  
Arne Døssing ◽  
Eduardo Lima Simoes da Silva ◽  
Guillaume Martelet ◽  
Thorkild Maack Rasmussen ◽  
Eric Gloaguen ◽  
...  
Keyword(s):  
High Speed ◽  
Large Scale ◽  
Signal To Noise Ratio ◽  
Regional Scale ◽  
Weather Conditions ◽  
Light Weight ◽  
Output Data ◽  
Efficient Manner ◽  
Flexible System ◽  
Scalar Data

Magnetic surveying is a widely used and cost-efficient remote sensing method for the detection of subsurface structures at all scales. Traditionally, magnetic surveying has been conducted as ground or airborne surveys, which are cheap and provide large-scale consistent data coverage, respectively. However, ground surveys are often incomplete and slow, whereas airborne surveys suffer from being inflexible, expensive and characterized by a reduced signal-to-noise ratio, due to increased sensor-to-source distance. With the rise of reliable and affordable survey-grade Unmanned Aerial Vehicles (UAVs), and the developments of light-weight magnetometers, the shortcomings of traditional magnetic surveying systems may be bypassed by a carefully designed UAV-borne magnetometer system. Here, we present a study on the development and testing of a light-weight scalar field UAV-integrated magnetometer bird system (the CMAGTRES-S100). The idea behind the CMAGTRES-S100 is the need for a high-speed and flexible system that is easily transported in the field without a car, deployable in most terrain and weather conditions, and provides high-quality scalar data in an operationally efficient manner and at ranges comparable to sub-regional scale helicopter-borne magnetic surveys. We discuss various steps in the development, including (i) choice of sensor based on sensor specifications and sensor stability tests, (ii) design considerations of the bird, (iii) operational efficiency and flexibility and (iv) output data quality. The current CMAGTRES-S100 system weighs ∼5.9 kg (including the UAV) and has an optimal surveying speed of 50 km/h. The system was tested along a complex coastal setting in Brittany, France, targeting mafic dykes and fault contacts with magnetite infill and magnetite nuggets (skarns). A 2.0 × 0.3 km area was mapped with a 10 m line-spacing by four sub-surveys (due to regulatory restrictions). The sub-surveys were completed in 3.5 h, including >2 h for remobilisation and the safety clearance of the area. A noise-level of ±0.02 nT was obtained and several of the key geological structures were mapped by the system.

Lineaments Revisited

SEG Discovery ◽  
2000 ◽  
pp. 1-20
Author(s):  
JEREMY P. RICHARDS
Keyword(s):  
Large Scale ◽  
Mineral Exploration ◽  
Porphyry Copper ◽  
Regional Scale ◽  
Tectonic Events ◽  
Magma Reservoirs ◽  
Magma Supply ◽  
Magmatic History ◽  
Lithospheric Stress ◽  
Crustal Magma

ABSTRACT Large-scale crustal lineaments are recognized as corridors (up to 30 km wide) of aligned geological, structural, geomorphological, or geophysical features that are distinct from regional geological trends such as outcrop traces. They are commonly difficult to observe on the ground, the scale of the features and their interrelationships being too large to map except at a regional scale. They are therefore most easily identified from satellite imagery and geophysical (gravity, magnetic) maps. Lineaments are believed to be the surface expressions of ancient, deep-crustal or trans-lithospheric structures, which periodically have been reactivated as planes of weakness during subsequent tectonic events. These planes of weakness, and in particular their intersections, may provide high-permeability channels for ascent of deeply derived magmas and fluids. Optimum conditions for magma penetration are provided when these structures are placed under tension or transtension. In regions of subduction-related magmatism, porphyry copper and related deposits may be generated along these lineaments because the structures serve to focus the ascent of relatively evolved magmas and fluid distillates from deep-crustal magma reservoirs. However, lineament intersections can only focus such activity where a magma supply exists, and when lithospheric stress conditions permit. A comprehensive understanding of regional tectono-magmatic history is therefore required to interpret lineament maps in terms of their prospectivity for mineral exploration.

Multilevel Regression and Poststratification Versus Survey Sample Weighting for Estimating Population Quantities in Large Population Health Studies

2020 ◽  
Vol 189 (7) ◽  
pp. 717-725 ◽  
Author(s):  
Marnie Downes ◽  
John B Carlin
Keyword(s):  
Population Health ◽  
Large Scale ◽  
Census Data ◽  
Large Population ◽  
Population Data ◽  
Superior Performance ◽  
Population Parameter ◽  
Multilevel Regression ◽  
Sample Weighting ◽  
Survey Weighting

Abstract Multilevel regression and poststratification (MRP) is a model-based approach for estimating a population parameter of interest, generally from large-scale surveys. It has been shown to be effective in highly selected samples, which is particularly relevant to investigators of large-scale population health and epidemiologic surveys facing increasing difficulties in recruiting representative samples of participants. We aimed to further examine the accuracy and precision of MRP in a context where census data provided reasonable proxies for true population quantities of interest. We considered 2 outcomes from the baseline wave of the Ten to Men study (Australia, 2013–2014) and obtained relevant population data from the 2011 Australian Census. MRP was found to achieve generally superior performance relative to conventional survey weighting methods for the population as a whole and for population subsets of varying sizes. MRP resulted in less variability among estimates across population subsets relative to sample weighting, and there was some evidence of small gains in precision when using MRP, particularly for smaller population subsets. These findings offer further support for MRP as a promising analytical approach for addressing participation bias in the estimation of population descriptive quantities from large-scale health surveys and cohort studies.

Evaluation Indexes System for Assessing Road Construction with Recycled Waste Materials to Achieve Better Environmental Impacts

2011 ◽  
Vol 250-253 ◽  
pp. 1001-1006 ◽  
Author(s):  
De Zhen Chen ◽  
Cui Jie Geng ◽  
Wen Zhou Sun
Keyword(s):  
Energy Consumption ◽  
Environmental Impact ◽  
Waste Disposal ◽  
Large Scale ◽  
Construction Material ◽  
Bottom Ash ◽  
Road Construction ◽  
Waste Materials ◽  
Evaluation Indexes ◽  
Recycled Waste

Evaluation indexes system has been put forward in this paper for quantifying thesystematical energy consumption, resources consumption, total emissions’ change and waste disposal capacity in road construction with recycled waste materials involved. With help of this evaluation indexes system, the contributions to environmental improvement caused by recycling waste materials in road construction can be quantified through calculating savings on environmental impact potentials, savings on energy consumption, on virgin materials’ consumption and waste disposal capacity provided by road construction. Based on the construction project of a road section numbered No.20 EWK0+400 ~ EWK0+600 of North highway to Shanghai Pudong international airport, which was the first trial project of using several kinds of recycled waste materials including bottom ash from incinerators to replace commonly used materials such as gravel in large scale in road pavement, the results of the four indexes, namely, savings on energy consumption and virgin materials’ consumption, environmental impact potentials as well as waste disposal capacity were obtained. It was found out that with multi recycled waste materials replacing part of the common construction material, systematical energy consumption can be reduced by 30%, a large amount of virgin resource consumption can be avoid and road construction also provides a remarkable large “dumping site” for solid wastes; while at the same time environmental impact potentials were saved for most impact categories except for increase in Ecotoxicity, water chronic, which was caused by heavy metals’ leaching and can be prevented by pre-treatment. Those results are useful for guiding the utilization of recycled waste materials, as well as for developing new technology process and advanced materials in road construction.

scholarly journals Remote sensing of <i>Trichodesmium</i> spp. mats in the western tropical South Pacific

2018 ◽  
Vol 15 (16) ◽  
pp. 5203-5219 ◽  
Author(s):  
Guillaume Rousset ◽  
Florian De Boissieu ◽  
Christophe E. Menkes ◽  
Jérôme Lefèvre ◽  
Robert Frouin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
New Caledonia ◽  
Hot Spot ◽  
Regional Scale ◽  
Atmospheric Correction ◽  
South Pacific ◽  
Aggregation Processes ◽  
Modis Imagery ◽  
Generate Surface

Abstract. Trichodesmium is the major nitrogen-fixing species in the western tropical South Pacific (WTSP) region, a hot spot of diazotrophy. Due to the paucity of in situ observations, remote-sensing methods for detecting Trichodesmium presence on a large scale have been investigated to assess the regional-to-global impact of this organism on primary production and carbon cycling. A number of algorithms have been developed to identify Trichodesmium surface blooms from space, but determining with confidence their accuracy has been difficult, chiefly because of the scarcity of sea-truth information at the time of satellite overpass. Here, we use a series of new cruises as well as airborne surveys over the WTSP to evaluate their ability to detect Trichodesmium surface blooms in the satellite imagery. The evaluation, performed on MODIS data at 250 m and 1 km resolution acquired over the region, shows limitations due to spatial resolution, clouds, and atmospheric correction. A new satellite-based algorithm is designed to alleviate some of these limitations, by exploiting optimally spectral features in the atmospherically corrected reflectance at 531, 645, 678, 748, and 869 nm. This algorithm outperforms former ones near clouds, limiting false positive detection and allowing regional-scale automation. Compared with observations, 80 % of the detected mats are within a 2 km range, demonstrating the good statistical skill of the new algorithm. Application to MODIS imagery acquired during the February-March 2015 OUTPACE campaign reveals the presence of surface blooms northwest and east of New Caledonia and near 20∘ S–172∘ W in qualitative agreement with measured nitrogen fixation rates. Improving Trichodesmium detection requires measuring ocean color at higher spectral and spatial (<250 m) resolution than MODIS, taking into account environment properties (e.g., wind, sea surface temperature), fluorescence, and spatial structure of filaments, and a better understanding of Trichodesmium dynamics, including aggregation processes to generate surface mats. Such sub-mesoscale aggregation processes for Trichodesmium are yet to be understood.

scholarly journals Application of global models and satellite data for smaller-scale groundwater recharge studies

2016 ◽  
Author(s):  
Rogier Westerhoff ◽  
Paul White ◽  
Zara Rawlinson
Keyword(s):  
New Zealand ◽  
Satellite Data ◽  
Input Data ◽  
Large Scale ◽  
Regional Scale ◽  
Global Scale ◽  
Global Models ◽  
Scale Models ◽  
Boundary Issues ◽  
Rainfall Recharge

Abstract. Large-scale models and satellite data are increasingly used to characterise groundwater and its recharge at the global scale. Although these models have the potential to fill in data gaps and solve trans-boundary issues, they are often neglected in smaller-scale studies, since data are often coarse or uncertain. Large-scale models and satellite data could play a more important role in smaller-scale (i.e., national or regional) studies, if they could be adjusted to fit that scale. In New Zealand, large-scale models and satellite data are not used for groundwater recharge estimation at the national scale, since regional councils (i.e., the water managers) have varying water policy and models are calibrated at the local scale. Also, some regions have many localised ground observations (but poor record coverage), whereas others are data-sparse. Therefore, estimation of recharge is inconsistent at the national scale. This paper presents an approach to apply large-scale, global, models and satellite data to estimate rainfall recharge at the national to regional scale across New Zealand. We present a model, NGRM, that is largely inspired by the global-scale WaterGAP recharge model, but is improved and adjusted using national data. The NGRM model uses MODIS-derived ET and vegetation satellite data, and the available nation-wide datasets on rainfall, elevation, soil and geology. A valuable addition to the recharge estimation is the model uncertainty estimate, based on variance, covariance and sensitivity of all input data components in the model environment. This research shows that, with minor model adjustments and use of improved input data, large-scale models and satellite data can be used to derive rainfall recharge estimates, including their uncertainty, at the smaller scale, i.e., national and regional scale of New Zealand. The estimated New Zealand recharge of the NGRM model compare well to most local and regional lysimeter data and recharge models. The NGRM is therefore assumed to be capable to fill in gaps in data-sparse areas and to create more consistency between datasets from different regions, i.e., to solve trans-boundary issues. This research also shows that smaller-scale recharge studies in New Zealand should include larger boundaries than only a (sub-)aquifer, and preferably the whole catchment. This research points out the need for improved collaboration on the international to national to regional levels to further merge large-scale (global) models to smaller (i.e., national or regional) scales. Future research topics should, collaboratively, focus on: improvement of rainfall-runoff and snowmelt methods; inclusion of river recharge; further improvement of input data (rainfall, evapotranspiration, soil and geology); and the impact of recharge uncertainty in mountainous and irrigated areas.

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