dataesr/scientific-paragraphs-categorization huggingface dataset:
Particular attention has been paid to Algeria-a high exposure, seismic-prone region-to show the benefit of reducing uncertainties by considering regional events for loss models. Several models and exposure variables were also tested (population, GDP). In the following section, we present the data on seismological features (magnitude, location) and economic, social, and physical consequences. When the regression models are developed, the results and their interpretations are discussed. Finally, these models are applied to a case study of Constantine, Algeria using data from a large-scale vulnerability assessment that was carried out previously using data mining-based methods.
Mediterranean Earthquakes and Losses Data To develop a seismic loss model for a specific region with any degree of reliability, historical data of earthquake impact must be carefully considered and compiled in a comprehensive way. This involves the review of existing earthquake catalogs, engineering damage reports, and databases of losses reported after earthquakes, among other sources. The objective of the present study was not to offer a comparative analysis of data quality, and the selection of the data sources here was based on their international use and dissemination, or considered as being authoritative at the national scale. For this study, hundreds of reports and publications were consulted, but only a few reports that described losses in detail were ultimately used. The region considered is the Mediterranean region (Fig.
1 ), which includes several earthquake-prone countries and is exposed to both strong (for example, Italy, Algeria, Greece) and moderate (for example, France, Morocco, Libya) seismic hazard. The data are drawn from the period of 1900-2015, and are for earthquakes for which detailed loss reports were available. Following a scientific approach, only accessible, public documents were considered, and these are referenced to allow the traceability of the information used. The characteristics of the earthquakes are given in Tables  1  and 2. Magnitude and intensity (the Mercalli modified intensity) are from the U. S. Geological Survey earthquake search catalog 2  for earthquakes after 1980, and from public reports or peer-reviewed articles for others. As an example,  Benouar (1994)  completed a database of the strongest Algerian earthquakes for the 1900-1980 period, the Algerian Technical Office  (Azzouz 2002; Azzouz and Rebzani 2005)  provided information on specific earthquakes, and on behalf of the French ministry in charge of natural disaster management,  Payany (1983)  provided information on the damaging earthquakes that occurred in France in 1909.
The final dataset consisted of 65 earthquakes that occurred between 1908 and 2014, with maximal epicentral intensities C VI (Table  1 ), and located in nine countries: Algeria (24), Italy (12), Greece (11), Turkey (11), Morocco (2), Spain (2), France, Libya, and Egypt (1 each). Figure  2a  shows the distribution of the earthquakes per country. This study does not include all the damaging earthquakes in the Mediterranean region over this period, mainly because either no data or only data from largely uncertain sources were available for some in terms of economic and human losses. The Earthquake Engineering Research Institute (EERI) reports  (EERI 1986 (EERI , 1992 (EERI , 1995 (EERI , 1998 (EERI , 1999 (EERI , 2004 (EERI , 2012) )  provided a lot of post-earthquake descriptions that were collected in the field soon after the events, and these were used to complete the seismological information in our study.
In terms of damage and economic and human losses, the EERI reports are often limited to information available immediately after the event. Several months, or even years, can pass after an earthquake before this information can be considered to be comprehensive, and definitive missing information on total socioeconomic losses is never updated in these reports. Additional official databases were therefore examined to gather the missing information for this study: the International Disaster Database EM-DAT (Guha-Sapir et al. 2016), the Damaging Earthquakes Database CATDAT  (Daniell et al. 2011; CATDAT 2016) , and the Significant Earthquake Database of the National Geophysical Datacenter (NGDC 2016). In these databases, no information was available on the reliability and accuracy of the data that they contained, related to, for instance, the exposed populations, economic losses, and fatalities, as these databases consisted only of compilations of reports.
The EM-DAT database was created in 1998 and is maintained by the Centre for Research on the Epidemiology of Disasters in Brussels, Belgium. This database contains comprehensive data related to the occurrence and effects of over 18,000 natural disasters throughout the world, from 1900 until the present day. The EM-DAT data were cross-checked with other sources of information, such as UN agencies, nongovernmental organizations, and reinsurance companies. The NGDC database that was developed by the National Center for Environmental Information is similar to EM-DAT. It contains data on over 5700 destructive earthquakes from 2150 B. C. to the present, including information related to socioeconomic losses. CATDAT was created in 2010, and it contains over 20,000 sources of information that provide data on losses from over 12,000 historical damaging earthquakes  (Daniell et al.
2011 ). In the present study, we considered information from CATDAT for earthquakes that occurred between 2011 and 2014. Finally, additional sources of information, such as public and official reports and peer-reviewed articles completed the loss databases used in this study (Table  2 ). Figure  2b-d  shows the distribution of the earthquakes considered in the present study for each data source, in terms of economic losses (Fig. 2b ), home loss (Fig. 2c ), and number of damaged buildings (Fig. 2d ). The earthquakes were mainly located in the most seismically active countries of the Mediterranean region (Italy, Greece, Turkey, and Algeria), which represent 90% of the dataset. As particular attention was given to the Algerian region in the present study, 37% of the selected earthquakes were in Algeria.
This proportion is not representative of the seismicity rate over this period, but is due to the information that was available to the authors. As shown in Fig. 2 , more than 60% of the data for socioeconomic losses was drawn from the EM-DAT (Guha-Sapir et al. 2016) and NGDC (2016) databases. Because our objective was to construct the most comprehensive and traceable dataset possible, the data sources were cross-referenced according to their international audience or dissemination and validation at the international level, and references for the preferred source of information for earthquake occurrence were included in Tables  1  and 2.
Method Based on data from the Mediterranean earthquake and loss database developed for this study, and using regression analysis between economic and human losses and hazard and exposure factors, empirical loss models for earthquakes in the Mediterranean region are developed in this study and their accuracy is examined. These models are then applied in a case study of Algeria for earthquake loss prediction. As a first step to develop empirical seismic loss  models from the predictor variables available in the study database, the regression model forms proposed by  Cha (1998)  for GDP and seismic intensities, Heatwole and Rose (2013) for magnitude, population, and GDP, and Samardjieva and  Badal (2002)  for population and magnitude are adopted in this study. Economic losses (L) are considered as a key dependent variable of the regression models.
For the Algerian earthquakes and the single French earthquake of Lambesc (1909), L EQ was given in currencies that no longer exist. Here, the French Institute of Statistics and Economic Studies converter 4 was used to convert L EQ into current currency in Euros on the date of the earthquake, which was then adjusted to the L $2015 (Table  2 ). Home loss (H), injury (I), and fatality (F) are used as dependent variables of the regression models for human (social) losses, and their statistics came directly from the EM-DAT and NGDC databases for most of the earthquakes considered in the present study, or from referenced reports (Table  1 ). Earthquake intensity and magnitude and building damage are selected as hazard variables. Damage (D) is usually reported as the total number of buildings that suffer damage.
The present study determined the total number of buildings based on those having suffered heavy to strong damage, as classified by the damage grades D4 and D5 (Table  2 ), according to the EMS98 damage scale. Population affected by the earthquakes (POP. Unit) is considered an important exposure variable, and these data came either from direct reports or other references (Table  2 ): POP:Unit ¼ POP Tot A Aff A Tot ð1Þ where A Aff is the size of the affected area, A Tot is the surface area, and POP Tot is the population of the country, on the date of the earthquake. POP Tot was provided by the  Population City database, 5  taking into account the population growth rate. A Aff was obtained either from references or through estimation from seismic intensity maps. GDP per capita on the date of the earthquake (GDP PC ), as another key exposure variable, is scaled by the population (POP.
Only 54    Cha (1998) , the linear regression models are different for seismic intensities (MMI) of VIII to X, which confirms the differences between the worldwide and regional models, and the need to derive a loss-prediction model for any specific area using the appropriate data  (Heatwole and Rose 2013). For moderate seismic-prone regions, lossprediction models for weak seismic intensity (VII) are also relevant, as shown by the moderate earthquake-induced losses referenced in the present dataset (Tables  1, 2 ). Table  3  summarizes the results of the regression models for intensities VII-X using Eq. 3. Considering R 2 , the model fits are relatively good, at 0. 62-0. 76, and is as good as for the models provided by  Cha (1998). Regression for losses was also performed considering the exposed population as a predictor variable and using the same regression model form as Eq.
Economic Losses (L $2015 ) Versus Earthquake Magnitude, GDP, and Affected Population Heatwole and Rose (2013) used the magnitude of U. S. earthquakes as a hazard-related predictor variable for loss prediction, and considered the population of the affected area and GDP as exposure-related variables. Assuming a non-normal distribution of the variables, they considered a natural-log model using a conventional form, as follows: log L $2015 ð Þ¼a log X1 ð Þ þ b log X2 ð Þ þ c þ e; ð4Þ where log indicates the natural log; a, b, and c are the regression coefficients; e is the RMSE; X1 is the hazardrelated variable-magnitude or intensity; and X2 is the exposure-related variable-GDP. Unit or POP. Unit. The sparse nature of the data and the minimum number of predictor variables means that these variables are not normally distributed and are considered logged variables.
610 versus 0. 653) as for the regional U. S. earthquake-based model  (Heatwole and Rose 2013) , which also confirms the advantage of using regional earthquake loss databases. However, the magnitude characterizes the actual earthquake and does not take into account any seismic wave attenuation with distance, whereas the macroseismic intensity does. It is therefore reasonable to assume that intensity is a more relevant hazard-related predictor than magnitude. Table  6  gives the economic loss predictions with intensity as the hazard-related predictor variable, in the same manner as the formulation by  Cha (1998). The fit model parameters (R 2 , adjusted R 2 ) are higher with intensity than with magnitude, with values comparable to those from Eq. 3 considering GDP. Unit (Table  3 ), and R 2 close to 0.
The human loss variables are the number of homeless and injured people, and the fatalities, while considering the affected population (POP. Unit) as an exposure-related variable. The hazard-related predictor variables are seismic intensity and number of damaged buildings classified as D4 ? D5, according to EMS98. In our dataset, the quality of the data is relatively heterogeneous, with some references indicating the numbers of casualties with accuracy to the unit, while others indicating rounded quantities, and thus introducing uncertainty into the regression models. The primary objective of the present study was also to develop prediction models for the Mediterranean region, with special focus on Algeria, and here the Algerian earthquakes are defined separately from the total dataset in each analysis.
Combining damage as hazard-related variable and GDP or exposed population as exposure-related variables improve the accuracy of the prediction. Nevertheless, damage prediction is more challenging than intensity or magnitude for real-time predictions and especially for regions with no available building inventory or large-scale vulnerability models. New, rapid methods for large-scale damage estimation are becoming more readily available. These practices are based on statistical analysis or data mining of urban data derived from satellite remote sensing  (Saito and Spence 2011)  or national census databases  (Pittore and Wieland 2013; Riedel et al. 2014 Riedel et al. , 2015;; Guettiche et al. 2017).
(2014)  and  Riedel et al. (2015) , which were validated by comparing classical in situ engineering survey results. The elementary information provided by remote sensing and the national census (for example, period of construction, number of stories) was used to assess seismic vulnerability. We used the EMS98 damage matrix for intensities VII-X to compute seismic damage (D4 ? D5) for each intensity. Our study is not focused primarily on the Constantine loss assessment for a realistic earthquake scenario, but more as a case study to provide insights on the uncertainties of the loss model related to the database. Only the damage-related scenarios given in  Guettiche et al. (2017)  were considered for testing the loss models for intensities VII-X. The population of Constantine in 2008 was around 1 million, according to the National Office of Statistics.
In Constantine the total number of damaged buildings was between 195 (VII) and 1654 (X), which produced mean loss indicators for home loss, injured persons (injuries), and fatalities of 6067, 157, and 91, respectively, and economic losses of about USD 200 million. It is worth noting that the uncertainty here is relatively high, which results from the uncertainties related to the information in the database. However, decision makers are not directly concerned with magnitude, building damage, or intensity, but rather pay attention to performance indices for loss and injuries to occupants, or the homeless people that need to be sheltered in the event of a seismic disaster. Compared with previous studies, this integration of building damage as a hazard-related parameter improves the prediction, and should thus be incorporated into the analysis of earthquake losses.
Conclusions This study provided a compilation of data and empirical models to estimate the economic and human impacts of earthquakes in the Mediterranean region. Earthquakes are complex and rare events, and post-earthquake information has to be carefully scrutinized before regression models can be developed. But there is the need to define performance metrics that are relevant for decision makers and seismic risk mitigation. Generally speaking, these metrics relate to the risks of casualties for anticipation of the shortterm responses to an emergency, such as home loss and personal injuries, and anticipation of economic losses and fatalities for the long-term recovery processes. In this study, data related to 65 earthquakes in the Mediterranean region were compiled in a comprehensive database, with particular attention paid to Algeria.
Studies) data were prepared and provided by the  Centre Maurice Halbwachs (CMH). This study was sponsored by the  Urban Seismology project at the Institute of Earth Science ISTerre of the University of Grenoble-Alpes  and by a grant from the  Observatoire des Sciences de Univers de Grenoble  ( The Grenoble Observatory for Sciences of the Universe-Labex OSUG@2020 ) (Investissements d'avenir,  ANR10-LABX56 ). We thank the two anonymous reviewers for their constructive remarks and comments and the editors of the journal for the editing work.
South Africa has been measuring the ground-based solar UV index for more than two decades at six sites to raise awareness about the impacts of the solar UV index on human health. This paper is an exploratory study based on comparison with satellite UV index measurements from the OMI/AURA experiment. Relative UV index differences between ground-based and satellite-derived data ranged from 0 to 45% depending on the site and year. Most of time, these differences appear in winter. Some ground-based stations' data had closer agreement with satellite-derived data. While the ground-based instruments are not intended for long-term trend analysis, they provide UV index information for public awareness instead, with some weak signs suggesting such long-term trends may exist in the ground-based data.
Satellite-Derived Solar UVR Levels Launched in late 2004, the Aura satellite is dedicated to measuring ozone, aerosols, and other key gases in the atmosphere. The OMI (Ozone Monitoring Instrument) sensor aboard Aura measures ozone total quantities by analysing the backscattered solar radiation. The erythemally-weighted irradiance (290-400 nm) is computed by using an enhanced version of the TOMS (Total Ozone Mapping Spectrometer) surface UV-B flux algorithm  [11, 21]. A recent version of OMI datasets taking better consideration of aerosol optical depth and single scattering albedo obtained by modelling and ground-based observations from the AERONET network was used  [22]. Data from OMI/Aura for the UV index given at overpass are available on the GIOVANNI platform for download  [23]. We applied the 'all-sky UV index' data and not the 'clear-sky UV index' data in this study, since the ground-based solar UV index data included the effects of clouds, where clouds are known factors influencing ambient solar UVR levels  [1].
Methods and Statistical Analysis Both datasets were prepared separately and scrutinised for obvious errors. Erroneous night-time UV index values were removed. The ground-based dataset for Durban exhibited a 2-h time shift in instrument timing between 2009 and 2012 and this timing error was corrected. Ground-based data were analysed for diurnal, monthly, and annual trends and satellite-derived data for monthly and annual trends since one value (overpass satellite time) of the UV index was available per day. Both datasets were compared at satellite overpass time (around 1:45 p. m. for South Africa). To only keep clear sky days for the comparison, OMI Lambertian equivalent reflectivity (LER) was used. The days with an LER higher than 10% were removed  [25]. A comparison of ground-based and satellite-derived solar UV index values were made in two ways: (1) computation of a daily correlation year-by-year to see if ground-based and satellite data evolve in the same way; and (2) computation of a daily bias year-over-year to provide an estimate of the relative difference between the two datasets.
To compare the ground-based data with satellite data, we calculated the bias, median, standard deviation, and root mean square error (RMSE). Bias is defined as the ground-based observation subtracted by the satellite observation and can be interpreted as the difference between the two datasets. The median separates the data in two equal parts. Standard deviation is defined as the dispersion of the previous bias. RMSE is defined as a measure of the differences between calculated values (here, OMI data) and observed values, i. e. , ground-based observations. Lower RMSE values indicate less residual variance. The equation (Equation (  2 )) for the R-squared correlation is given below: r = ∑ n i=1 S GND,i -S GND S OMI,i -S OMI ∑ n i=1 S GND,i -S GND 2 ∑ n i=1 S OMI,i -S OMI 2 ( 2 ) where S is the UV index, S is mean UV index, GND is the ground-based observation, OMI is the satellite observation, and n the number of observation.
We computed a bias (Equation (  3 )) and mean absolute percentage error (MAPE) (Equation (  4 )), a standard deviation (Equation (  5 )), and an RMSE (Equation (  6 )) as given below: Bias = 1 n ∑ n i=1 (S OMI,i -S GND,i ) (3) MAPE = 1 n ∑ n i=1 100 * |S OMI,i -S GND,i | S GND,i (4) Std = 1 n -1 ∑ n i=1 ((S OMI,i -S GND,i ) -Bias) 2 (5) RMSE = 1 n -1 ∑ n i=1 (S OMI,i -S GND,i ) 2 ( 6 ) where S is the UV index, GND is the ground-based observation, OMI is the satellite observation, and n is the number of observations. These calculations were made for the full period of comparison, i. e. , 2005 to 2015, as well as year-over-year in order to quantify the temporal evolution of change in solar UVR data from ground-based sites and satellite-derived observations.
Ground-Based Solar UVR Observations All solar UVR results are given in UV index units. Hourly data from the six ground-based solar UVR measurements sites were analysed for the period 1994-2015 where data were available for each site. Raw data are provided in Figure  2. Four stations initiated observations in 1994, while De Aar and Port Elizabeth observations begun later, in 2000 and 2002, respectively. Several data gaps are also evident, with the longest being at De Aar between 2010 and 2012. The typical diurnal solar UVR pattern is evident with UV index levels increasing during the day, reaching a peak at solar noon and decreasing during the afternoon as solar zenith angles increase again. Annual variability, modulated by the seasonal cycle, is also evident in Figure  2 , with summer UV index values regularly exceeding 11 UV index units at De Aar.
It is apparent that the hour of maximum intensity (i. e. , daily peak UV index) depends on the longitude of the site. Cape Town had the latest mean hour of maximum UV index followed by De Aar, Port Elizabeth, Pretoria, and Durban. Since the satellite-derived solar UVR data were only available for the period of 2005 to 2015, the ground-based solar UVR dataset was reduced, for comparison purposes and further analysis (see Section 3. 3 below), to only include the UV index at satellite overpass time readings from 2005 to 2015 (Figure  5 ). If we do not consider De Aar, which is the second highest and the clearest site, Cape Town shows the more exposed site to UVR around noon. Cape Town and Cape Point show similar UVR levels; they are two geographically-close sites (about 50 km apart). If we do not consider De Aar, which is the second highest and the clearest site, Cape Town shows the more exposed site to UVR around noon.
Comparison of Ground-Based and Satellite-Derived Solar UVR Data Ground-based UV index measurements at satellite overpass time were compared with those derived from the satellite OMI measurements for the six South African sites and for the period 2005-2015 (Figure  7 ). A similarity in the timing patterns of seasonal peaks is apparent in the two datasets. A visual comparison shows large differences for certain periods of time between the instruments. For example, at Cape Town in 2007, a new logger was used. At De Aar, in February 2007 the logger was changed because of lightning damage. At Pretoria, in 2013, the amplitude increases suddenly because of the roof was painted silver, although the sinusoidal pattern is preserved. Correlation analysis confirmed a positive correlation between the two datasets (Figure  8 ) where R 2 values ranged from a moderate correlation co-efficient of 0.