Uncertain human consequences in asteroid risk analysis and the global catastrophe threshold

This paper studies the risk of collision between asteroids and Earth. It focuses on uncertainty in the human consequences of asteroid collisions, with emphasis on the possibility of global catastrophe to human civilization. A detailed survey of the asteroid risk literature shows that while human consequences are recognized as a major point of uncertainty, the studies focus mainly on physical and environmental dimensions of the risk. Some potential human consequences are omitted entirely, such as the possibility of asteroid explosions inadvertently causing nuclear war. Other human consequences are modeled with varying degrees of detail. Direct medical effects are relatively well-characterized, while human consequences of global environmental effects are more uncertain. The latter are evaluated mainly in terms of a global catastrophe threshold, but such a threshold is deeply uncertain and may not even exist. To handle threshold uncertainty in asteroid policy, this paper adapts the concept of policy boundaries from literature on anthropogenic global environmental change (i.e., planetary boundaries). The paper proposes policy boundaries of 100 m asteroid diameter for global environmental effects and 1 m for inadvertent nuclear war. Other policy implications include a more aggressive asteroid risk mitigation policy and measures to avoid inadvertent nuclear war. The paper argues that for rare events like large asteroid collisions, the absence of robust data means that a wide range of possible human consequences should be considered. This implies humility for risk analysis and erring on the side of caution in policy.     

Real-time identification of urban rainstorm waterlogging disasters based on Weibo big data

With the acceleration of urbanisation in China, preventing and reducing the economic losses and casualties caused by urban rainstorm waterlogging disasters have become a critical and difficult issue that the government is concerned about. As urban storms are sudden, clustered, continuous, and cause huge economic losses, it is difficult to conduct emergency management. Developing a more scientific method for real-time disaster identification will help prevent losses over time. Examining social media big data is a feasible method for obtaining on-site disaster data and carrying out disaster risk assessments. This paper presents a real-time identification method for urban-storm disasters using Weibo data. Taking the June 2016 heavy rainstorm in Nanjing as an example, the obtained Weibo data are divided into eight parts for the training data set and two parts for the testing data set. It then performs text pre-processing using the Jieba segmentation module for word segmentation. Then, the term frequency–inverse document frequency method is used to calculate the feature items weights and extract the features. Hashing algorithms are introduced for processing high-dimensional sparse vector matrices. Finally, the naive Bayes, support vector machine, and random forest text classification algorithms are used to train the model, and a test set sample is introduced for testing the model to select the optimal classification algorithm. The experiments showed that the naive Bayes algorithm had the highest macro-average accuracy.     

Review of comparative studies on market mechanisms for carbon emission reduction: a bibliometric analysis

This study uses the bibliometric method to analyze the comparative studies on different market mechanisms applied to carbon reduction between 1970 and 2016 based on the online databases of Science Citation Index Expanded (1970–2016) and Social Science Citation Index (2002–2016). We found by observing the characteristics of publications that such studies belong to a multidisciplinary field that has been continuously developing since the 1990s. The USA and the UK have maintained their leading research strengths in the field, whereas China entered late but has developed rapidly. The most productive journal, institution, and author in this field are Energy Policy, Resources for the Future, and Pizer from the USA, respectively. The auctorial and institutional cooperation degrees are growing and still have a broad collaboration space, but the cooperation among countries is fluctuating at a lower level. Nonetheless, the USA keeps its significant international cooperation ties. The bibliographic coupling and co-citation analysis reveals that articles in this field are closely related to one another. The basic literature in the field was written by Weitzman and published in 1974, and the article by Nordhaus published in Science in 1992 is the most cited in the field. The analysis of keywords and abstract shows that the hot spots include policy choice, price-versus-quantity analysis, and mechanism design.     

A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

In this paper, we evaluate the predictive performance of an adaptive neuro-fuzzy inference system (ANFIS) using six different membership functions (MF). In combination with a geographic information system (GIS), ANFIS was used for land subsidence susceptibility mapping (LSSM) in the Marand plain, northwest Iran. This area is prone to droughts and low groundwater levels and subsequent land subsidence damages. Therefore, a land subsidence inventory database was created from an extensive field survey. Areas of land subsidence or areas showing initial signs of subsidence were used for training, while one-third of inventory database were reserved for testing and validation. The inventory database randomly divided into three different folds of the same size. One of the folds was chosen for testing and validation. Other two folds was used for training. This process repeated for every fold in the inventory dataset. Thereafter, land subsidence related factors, such as hydrological and topographical factors, were prepared as GIS layers. Areas susceptible to land subsidence were then analyzed using the ANFIS approach, and land subsidence susceptibility maps were created, whereby six different MFs were applied. Lastly, the results derived from each MF were validated with those areas of the land subsidence database that were not used for training. Receiver operating characteristics (ROC) curves were drawn for all LSSMs, and the areas under the curves were calculated. The ROC analyses for the six LSSMs yielded very high prediction values for two out of the six methods, namely the difference of DsigMF (0.958) and GaussMF (0.951). The integration of ANFIS and GIS generally led to high LSSM prediction accuracies. This study demonstrated that the choice of training dataset and the MF significantly affects the results.     

Mining exploitation influence range

Mining exploitation has a negative impact on the natural environment. Voids created in the rockmass result in displacements and deformations of land surface. During planning and conducting the exploitation, the range of exploitation influence in the form of linear deformations is being determined. On the basis of mining-geological parameters of exploitation, the exploitation range of influences is calculated. According to the literature, many different ranges of exploitation influences can be determined depending on what has been the purpose of it. Different types of exploitation influence ranges can be distinguished, such as theoretical, damage or measurable. In the paper, the matters connected with determining those three types of the influence range are taken under consideration. The comparison of magnitudes of determined influence ranges is illustrated with two practical examples.     

Quantile regression C-vine copula model for spatial extremes

A spatial quantile regression model is proposed to estimate the quantile curve for a given probability of non-exceedance, as function of locations and covariates. Canonical vines copulas are considered to represent the spatial dependence structure. The marginal at each location is an asymmetric Laplace distribution where the parameters are functions of the covariates. The full conditional quantile distribution is given using the Joe–Clayton copula. Simulations show the flexibility of the proposed model to estimate the quantiles with special dependence structures. A case study illustrates its applicability to estimate quantiles for spatial temperature anomalies.     

Archaeoseismological evidence of past earthquakes in Rome (fifth to ninth century A.D.) used to quantify dating uncertainties and coseismic damage

The transformation of Rome during the Late Antiquity and the Early Middle Ages has been investigated by archaeologists and historians. Social and political changes are the main aspects which led to a progressive modification of the urban framework; abandonment, spoliation and transformation of buildings are quite diffused as documented by the archaeological literature. The consequence of these practices is a higher vulnerability of the buildings which, from the seismological point of view, played a main role in increasing the effects of seismic shaking. A number of earthquakes have struck Rome during the period of investigation (fifth to ninth century A.D.), known from historical sources: 443, 484–508, 618, 801, 847; in some cases (443, 484–508, 801) damage has been documented. In contrast, the archaeological sources characterise collapse layers and evidence of destruction at different sites with changing and not always conclusive chronological constraints. Consequently, collapse and destruction have been alternatively attributed to the above-mentioned earthquakes. Through a geoarchaeological and stratigraphic analysis of potentially coseismic collapse units, we want (1) to describe the archaeoseismic evidence derived from recent excavations and from the available literature (e.g. Piazza Madonna di Loreto, Piazza Venezia, Palazzo Valentini Crypta Balbi, Colosseo, Basilica Hilariana, Basilica di Santa Petronilla, Santa Maria Antiqua,…); (2) to discuss the chronological problems and the uncertainty of attribution of the collapse units to known historical earthquakes; (3) to discuss the earthquake damage exaggeration due to erroneous attribution of seismic origin to the evidence of destruction derived from archaeological data. Finally, we will infer the role that earthquakes may have had on the development of the urban landscape in the fifth to ninth century A.D.     

Socioeconomic vulnerability and electric power restoration timelines in Florida: the case of Hurricane Irma

Large-scale damage to the power infrastructure from hurricanes and high-wind events can have devastating ripple effects on infrastructure, the broader economy, households, communities, and regions. Using Hurricane Irma’s impact on Florida as a case study, we examined: (1) differences in electric power outages and restoration rates between urban and rural counties; (2) the duration of electric power outages in counties exposed to tropical storm force winds versus hurricane Category 1 force winds; and (3) the relationship between the duration of power outage and socioeconomic vulnerability. We used power outage data for the period September 9, 2017–September 29, 2017. At the peak of the power outages following Hurricane Irma, over 36% of all accounts in Florida were without electricity. We found that the rural counties, predominantly served by rural electric cooperatives and municipally owned utilities, experienced longer power outages and much slower and uneven restoration times. Results of three spatial lag models show that large percentages of customers served by rural electric cooperatives and municipally owned utilities were a strong predictor of the duration of extended power outages. There was also a strong positive association across all three models between power outage duration and urban/rural county designation. Finally, there is positive spatial dependence between power outages and several social vulnerability indicators. Three socioeconomic variables found to be statistically significant highlight three different aspects of vulnerability to power outages: minority groups, population with sensory, physical and mental disability, and economic vulnerability expressed as unemployment rate. The findings from our study have broader planning and policy relevance beyond our case study area, and highlight the need for additional research to deepen our understanding of how power restoration after hurricanes contributes to and is impacted by the socioeconomic vulnerabilities of communities.     

An agent-based evacuation model for the 2011 Brisbane City-scale riverine flood

In this study, an agent-based model is proposed in order to provide new insights into the policy analysis and strategy assessment of city-scale evacuation management. The proposed model is suitable for assessment of the influence of different departure times and communications among peer evacuees on the number of residents at risk who arrive at official shelters. A case study is applied to build a simulation model for the coastal city of Brisbane in Australia. The Brisbane River catchment experiences regular flooding almost every year; the second severest flood since the twentieth century occurred in 2011. During that event, over 15,000 properties were inundated and around 3600 households evacuated in metropolitan Brisbane alone. Making use of high-performance computing clusters, the evacuation simulation was coupled with results from a validated hydrodynamic model to test a variety of escaping scenarios based on the 2011 flood situation. This case study demonstrates the proposed model’s capacity to represent the dynamic evacuation process and also shows that the model is able to help develop flood emergency plans and evaluate response measures through exploring key elements in a range of scenarios.     

Effect of climatic oscillations on flood occurrence on Papaloapan River,México,during the 1550–2000 period

This study presents a chronology of historical and measured flood events in the Papaloapan River basin of Mexico during 450 years. Twenty-eight historical floods were recorded during the period 1550–1948 on this river and one flood event (1969) in the instrumental era (1949–2000), of which 14 were extraordinary floods and only 15 were catastrophic ones. There were several flood-rich decades during 1860–1870, 1880–1890, 1920–1930 and 1940–1950. Wavelet analysis found a significant flooding periodicity of 58 years. The wavelet coherence analysis found that flooding had an in-phase relationship with the Atlantic Multidecadal Oscillation and also with the Pacific Decadal Oscillation. Logistic regression corroborated that there exists a positive relationship between floods events and these two natural climatic oscillations. The logistic regression model predicted correctly 92% of flood events.