The Theory of Earthquakes in Signalling Severe Political Events

This research seeks to conceptualise the use of an earthquake forecasting theory to signal severe political risks such as wars, coups d’état, demonstrations and revolutions. The justification for linking the theoretical framework of an earthquake with severe political risks is twofold. Firstly, it is generally random in its nature; however, there are some patterns which can help in predicting the occurrence of future earthquakes. Secondly, an earthquake is usually region-specific, i.e. there are geographical regions which are prone to earthquakes more than other locations, and there are regions where the odds of an earthquake occurrence are minimal; however, under certain circumstances there is always a negligible possibility of such an event occurring. Severe political events are similar in their nature as they are also location-specific and random in their occurrence. In order to establish the link between these two phenomena, a clearer definition of these two variables will need to be established. Thus this theoretical research will first define the nature of severe political risks in globalised world followed by definition of an earthquake and its nature. Once a clear definition of these two variables has been established, the discussion will move towards discussion of various models for signalling severe political risks and earthquakes. It will conclude by suggesting a new approach to signalling the possibility of an occurrence of severe political events based on various assessment models and methods employed in forecasting an occurrence of an earthquake

Determining the best set of seismicity indicators to predict earthquakes. Two case studies: Chile and the Iberian Peninsula

This work explores the use of different seismicity indicators as inputs for artificial neural networks. The combination of multiple indicators that have already been successfully used in different seismic zones by the application of feature selection techniques is proposed. These techniques evaluate every input and propose the best combination of them in terms of information gain. Once these sets have been obtained, artificial neural networks are applied to four Chilean zones (the most seismic country in the world) and to two zones of the Iberian Peninsula (a moderate seismicity area). To make the comparison to other models possible, the prediction problem has been turned into one of classification, thus allowing the application of other machine learning classifiers. Comparisons with original sets of inputs and different classifiers are reported to support the degree of success achieved. Statistical tests have also been applied to confirm that the results are significantly different than those of other classifiers. The main novelty of this work stems from the use of feature selection techniques for improving earthquake prediction methods. So, the infor-mation gain of different seismic indicators has been determined. Low ranked or null contribution seismic indicators have been removed, optimizing the method. The optimized prediction method proposed has a high performance. Finally, four Chilean zones and two zones of the Iberian Peninsula have been charac-terized by means of an information gain analysis obtained from different seismic indicators. The results confirm the methodology proposed as the best features in terms of information gain are the same for both regions.Ministerio de Ciencia y Tecnología BIA2004-01302Ministerio de Ciencia y Tecnología TIN2011-28956-C02-01Junta de Andalucía P11-TIC-752

Application of Gaode OPEN API in the Fire-Fighting Facility Planning Evaluation

n recent years, big data has been extensively applied in urban planning and design area. However, its wider application is constrained by the data acquisition approach. Accordingly, many efforts have been made to accurately find and solve urban problems by mining legal open source internet data, which gradually becomes a hot topic among urban researchers. In this context, this study applies the Gaode OPEN API in the urban real-time traffic circle construction and fire-fighting facility planning evaluation taking Nanjing as example. First, a batch processing model is established on the basis of Gaode OPEN API, including automatic acquisition of real-time traffic data in the study area and extraction of average vehicle speed of all the roads during specified time period. It found that speed involves division of the whole Nanjing City into several characteristic zones. Then, a case study is conducted to construct the urban real-time traffic circle through python language and Arcgis software based on existing fire-fighting facilities. In the end, through a subsequent evaluation, it is found that the existing fire-fighting facility planning has a stark regional difference between urban central area and suburban area, which shows a low coverage rate of 22% and 39% for respond time of 6min and 8min respectively. This study provides an integrated approach for acquisition and processing of Gaode data, mining of its space-time features, and application in the planning evaluation practice. This approach demonstrates the advantages of streaming features and distributed processing of big data, effectively utilizes the real-time characteristics of big data in the emergency facility planning, and is expected to broaden ideas for data acquisition

Geosystemics View of Earthquakes

Earthquakes are the most energetic phenomena in the lithosphere: their study and comprehension are greatly worth doing because of the obvious importance for society. Geosystemics intends to study the Earth system as a whole, looking at the possible couplings among the different geo-layers, i.e., from the earth’s interior to the above atmosphere. It uses specific universal tools to integrate different methods that can be applied to multi-parameter data, often taken on different platforms (e.g., ground,marine or satellite observations). Itsmain objective is to understand the particular phenomenon of interest from a holistic point of view. Central is the use of entropy, together with other physical quantities that will be introduced case by case. In this paper, we will deal with earthquakes, as final part of a long-term chain of processes involving, not only the interaction between different components of the Earth’s interior but also the coupling of the solid earth with the above neutral or ionized atmosphere, and finally culminating with the main rupture along the fault of concern. Particular emphasis will be given to some Italian seismic sequences.Publishedid 4121A. Geomagnetismo e PaleomagnetismoJCR Journa

Toward an integrated disaster management approach: How artificial intelligence can boost disaster management

Technical and methodological enhancement of hazards and disaster research is identified as a critical question in disaster management. Artificial intelligence (AI) applications, such as tracking and mapping, geospatial analysis, remote sensing techniques, robotics, drone technology, machine learning, telecom and network services, accident and hot spot analysis, smart city urban planning, transportation planning, and environmental impact analysis, are the technological components of societal change, having significant implications for research on the societal response to hazards and disasters. Social science researchers have used various technologies and methods to examine hazards and disasters through disciplinary, multidisciplinary, and interdisciplinary lenses. They have employed both quantitative and qualitative data collection and data analysis strategies. This study provides an overview of the current applications of AI in disaster management during its four phases and how AI is vital to all disaster management phases, leading to a faster, more concise, equipped response. Integrating a geographic information system (GIS) and remote sensing (RS) into disaster management enables higher planning, analysis, situational awareness, and recovery operations. GIS and RS are commonly recognized as key support tools for disaster management. Visualization capabilities, satellite images, and artificial intelligence analysis can assist governments in making quick decisions after natural disasters