Geohazards in the three Gorges Reservoir Area, China – Lessons learned from decades of research

Abstract The impoundment of the 660-km long reservoir behind the huge Three Gorges Dam, the world's largest hydropower station, increased regional seismicity and reactivated severe geohazards. Before the reservoir filling was initiated in 2003, the region had approximately two earthquakes per year with magnitudes between 3.0 and 4.9; after the full impoundment in 2008, approximately 14 earthquakes per year occurred with magnitudes between 3.0 and 5.4. In addition, hundreds of landslides were reactivated and are now in a state of intermittent creep. Many landslides exhibit step-like annual pattern of displacement in response to quasi-regular variations in seasonal rainfall and reservoir level. Additional problems include rock avalanches, impulse waves and debris flows. The seriousness of these events motivated numerous studies that resulted in 1) Better insight into the behavior and evolution mechanism of geohazards in the Three Gorges Reservoir Area (TGRA); 2) Implementation of monitoring and early-warning systems of geohazards; and 3) Design and construction of preventive countermeasures including lattice anchors, stabilizing piles, rock bolts, drainage canals and tunnels, and huge revetments. This paper reviews the hydro-geologic setting of TGRA geohazards, examines their occurrence and evolution in the past few decades, offers insight learned from extensive research on TGRA geohazards, and suggests topics for future research to address the remaining challenges

Recommendations for Tourism Sites Using the Mamdani Fuzzy Logic Method and Floyd Warshall Algorithm (Case Study in Yogyakarta)

Tourism is one of the activities carried out for recreation or leisure in a place with a variety of purposes and objectives. In Indonesia, many cities provide attractive tourism places, and one of them is the city of Yogyakarta. Because it has interesting and diverse tourism places, Yogyakarta is in great demand by local and foreign tourists. Thus to be able to maximize the visits of tourists who come to Yogyakarta, we need a system that is able to provide information on tourist attractions to tourists precisely in accordance with what the tourists want. The proposed system uses the Fuzzy Logic method and Floyd Warshall Algorithm which are combined, so as to obtain results in the form of recommendations for tourist attractions based on the costs of tourists, the length of time and distance needed to reach the tourist attraction

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

A BIM-based Approach for Predictive Safety Planning in the Construction Industry

The number of safety incidents in the construction industry is higher than that in most of the other industries. These safety incidents can be attributed to a lack of information and training. The new line of thinking in management has been moving toward predictive decision-making methods with the aid of artificial intelligence (AI). In this regard, the construction industry has been lagging on embracing modern management concepts. Hence, it is vital to re-engineer construction management to be on par with industries such as manufacturing. Building Information Modelling (BIM) can be recognized as the most promising technology that is introduced to the construction sector in the recent past. The information contained in a BIM model can be manipulated to aid construction safety management. This research presents BIM-based methods for predictive safety planning in the construction industry. At first, a comprehensive review of construction management challenges was conducted. This review revealed that although there are some studies regarding BIM-based predictive decision-making, still some knowledge gaps can be mentioned in the safety management of construction workers and building residents. To address the mentioned challenges, at first, this study integrates BIM with fuzzy logic to improve predictive safety planning to reduce the safety incidents in the construction projects. A Fuzzy Inference System (FIS) was developed based on the causality of safety incidents. The FIS extracts construction project data from BIM models while automatically assessing the risk of each potential hazard and also the total risk of a project. The proposed method enables construction managers to prevent construction incidents and enhance the health and safety of construction workers. Furthermore, this study develops a methodological framework for rule checking and the safety-focused ruleset for BIM-enabled building construction projects in Ontario, Canada. Identified safety standards were defined in Solibri Model checker software as a ruleset. The outcomes of this section will ensure the occupant’s safety through a proper design. Moreover, the findings of this will support promoting BIM in the Canadian construction industry

VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts

Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9 Workshops Stream 1 10 Workshop Stream 2 11 Workshop Stream 3 12 Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14 Session 2 – Visualisation, communication & Teaching 27 Session 3 – Applying Machine Learning in Geosciences 36 Session 4 – Digital Outcrop Characterisation & Analysis 49 Session 5 – Airborne & Remote Mapping 58 Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69 Session 7 – Applications in Hydrology & Ecology 82 Poster Contributions 9

A network-based system for assessment and management of infrastructure interdependency

Critical infrastructures (CIs) provide services that are essential to both the economy and well-being of nations and their citizens. Over the years, CIs are becoming more complex and interconnected, they are all interdependent in various ways, including logically, functionally, and geographically. The interconnection between CIs results in a very complex and dynamic system which increases their vulnerability to failures. In fact, when an infrastructure is experiencing failures, it can rapidly generate a cascade or domino effect to impact the other infrastructures. Thus, identifying, understanding and modeling infrastructure interdependency is a new field of research that deals with interrelationships between critical infrastructure sectors for disaster management. In the present research project, an integrated network-based analysis system with a user-friendly graphic user interface (GUI) was developed for risk analysis of complex critical infrastructure systems and their component interdependencies, called FCEPN (Fragility Curve and Extended Petri Net analysis). This approach combines: 1) Fragility Curve analysis of the vulnerability of the infrastructure, based on predefined "damage states" due to particular "hazards"; 2) Extended Petri Net analysis of the infrastructure system interdependency to determine the possible failure states and risk values. Two types of Extended Petri Net, Stochastic Petri Net and Fuzzy Petri Net were discussed in this study respectively. The FCEPN system was evaluated using the Bluestone Dam in West Virginia and Huai River Watershed in China as the case studies. Evaluation study results suggested that the FCEPN system provides a useful approach for analyzing dam system design, potential and actual vulnerability of dam networks to flood related impact, performance and reliability of existing dam systems, and appropriate maintenance and inspection work

Towards development of fuzzy spatial datacubes : fundamental concepts with example for multidimensional coastal erosion risk assessment and representation

Les systèmes actuels de base de données géodécisionnels (GeoBI) ne tiennent généralement pas compte de l'incertitude liée à l'imprécision et le flou des objets; ils supposent que les objets ont une sémantique, une géométrie et une temporalité bien définies et précises. Un exemple de cela est la représentation des zones à risque par des polygones avec des limites bien définies. Ces polygones sont créés en utilisant des agrégations d'un ensemble d'unités spatiales définies sur soit des intérêts des organismes responsables ou les divisions de recensement national. Malgré la variation spatio-temporelle des multiples critères impliqués dans l’analyse du risque, chaque polygone a une valeur unique de risque attribué de façon homogène sur l'étendue du territoire. En réalité, la valeur du risque change progressivement d'un polygone à l'autre. Le passage d'une zone à l'autre n'est donc pas bien représenté avec les modèles d’objets bien définis (crisp). Cette thèse propose des concepts fondamentaux pour le développement d'une approche combinant le paradigme GeoBI et le concept flou de considérer la présence de l’incertitude spatiale dans la représentation des zones à risque. En fin de compte, nous supposons cela devrait améliorer l’analyse du risque. Pour ce faire, un cadre conceptuel est développé pour créer un model conceptuel d’une base de donnée multidimensionnelle avec une application pour l’analyse du risque d’érosion côtier. Ensuite, une approche de la représentation des risques fondée sur la logique floue est développée pour traiter l'incertitude spatiale inhérente liée à l'imprécision et le flou des objets. Pour cela, les fonctions d'appartenance floues sont définies en basant sur l’indice de vulnérabilité qui est un composant important du risque. Au lieu de déterminer les limites bien définies entre les zones à risque, l'approche proposée permet une transition en douceur d'une zone à une autre. Les valeurs d'appartenance de plusieurs indicateurs sont ensuite agrégées basées sur la formule des risques et les règles SI-ALORS de la logique floue pour représenter les zones à risque. Ensuite, les éléments clés d'un cube de données spatiales floues sont formalisés en combinant la théorie des ensembles flous et le paradigme de GeoBI. En plus, certains opérateurs d'agrégation spatiale floue sont présentés. En résumé, la principale contribution de cette thèse se réfère de la combinaison de la théorie des ensembles flous et le paradigme de GeoBI. Cela permet l’extraction de connaissances plus compréhensibles et appropriées avec le raisonnement humain à partir de données spatiales et non-spatiales. Pour ce faire, un cadre conceptuel a été proposé sur la base de paradigme GéoBI afin de développer un cube de données spatiale floue dans le system de Spatial Online Analytical Processing (SOLAP) pour évaluer le risque de l'érosion côtière. Cela nécessite d'abord d'élaborer un cadre pour concevoir le modèle conceptuel basé sur les paramètres de risque, d'autre part, de mettre en œuvre l’objet spatial flou dans une base de données spatiales multidimensionnelle, puis l'agrégation des objets spatiaux flous pour envisager à la représentation multi-échelle des zones à risque. Pour valider l'approche proposée, elle est appliquée à la région Perce (Est du Québec, Canada) comme une étude de cas.Current Geospatial Business Intelligence (GeoBI) systems typically do not take into account the uncertainty related to vagueness and fuzziness of objects; they assume that the objects have well-defined and exact semantics, geometry, and temporality. Representation of fuzzy zones by polygons with well-defined boundaries is an example of such approximation. This thesis uses an application in Coastal Erosion Risk Analysis (CERA) to illustrate the problems. CERA polygons are created using aggregations of a set of spatial units defined by either the stakeholders’ interests or national census divisions. Despite spatiotemporal variation of the multiple criteria involved in estimating the extent of coastal erosion risk, each polygon typically has a unique value of risk attributed homogeneously across its spatial extent. In reality, risk value changes gradually within polygons and when going from one polygon to another. Therefore, the transition from one zone to another is not properly represented with crisp object models. The main objective of the present thesis is to develop a new approach combining GeoBI paradigm and fuzzy concept to consider the presence of the spatial uncertainty in the representation of risk zones. Ultimately, we assume this should improve coastal erosion risk assessment. To do so, a comprehensive GeoBI-based conceptual framework is developed with an application for Coastal Erosion Risk Assessment (CERA). Then, a fuzzy-based risk representation approach is developed to handle the inherent spatial uncertainty related to vagueness and fuzziness of objects. Fuzzy membership functions are defined by an expert-based vulnerability index. Instead of determining well-defined boundaries between risk zones, the proposed approach permits a smooth transition from one zone to another. The membership values of multiple indicators (e.g. slop and elevation of region under study, infrastructures, houses, hydrology network and so on) are then aggregated based on risk formula and Fuzzy IF-THEN rules to represent risk zones. Also, the key elements of a fuzzy spatial datacube are formally defined by combining fuzzy set theory and GeoBI paradigm. In this regard, some operators of fuzzy spatial aggregation are also formally defined. The main contribution of this study is combining fuzzy set theory and GeoBI. This makes spatial knowledge discovery more understandable with human reasoning and perception. Hence, an analytical conceptual framework was proposed based on GeoBI paradigm to develop a fuzzy spatial datacube within Spatial Online Analytical Processing (SOLAP) to assess coastal erosion risk. This necessitates developing a framework to design a conceptual model based on risk parameters, implementing fuzzy spatial objects in a spatial multi-dimensional database, and aggregating fuzzy spatial objects to deal with multi-scale representation of risk zones. To validate the proposed approach, it is applied to Perce region (Eastern Quebec, Canada) as a case study

The Encyclopedia of Neutrosophic Researchers - vol. 1

This is the first volume of the Encyclopedia of Neutrosophic Researchers, edited from materials offered by the authors who responded to the editor’s invitation. The authors are listed alphabetically. The introduction contains a short history of neutrosophics, together with links to the main papers and books. Neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics, neutrosophic measure, neutrosophic precalculus, neutrosophic calculus and so on are gaining significant attention in solving many real life problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and indeterminacy. In the past years the fields of neutrosophics have been extended and applied in various fields, such as: artificial intelligence, data mining, soft computing, decision making in incomplete / indeterminate / inconsistent information systems, image processing, computational modelling, robotics, medical diagnosis, biomedical engineering, investment problems, economic forecasting, social science, humanistic and practical achievements