Curvature-based sparse rule base generation for fuzzy rule interpolation

Fuzzy logic has been successfully widely utilised in many real-world applications. The most common application of fuzzy logic is the rule-based fuzzy inference system, which is composed of mainly two parts including an inference engine and a fuzzy rule base. Conventional fuzzy inference systems always require a rule base that fully covers the entire problem domain (i.e., a dense rule base). Fuzzy rule interpolation (FRI) makes inference possible with sparse rule bases which may not cover some parts of the problem domain (i.e., a sparse rule base). In addition to extending the applicability of fuzzy inference systems, fuzzy interpolation can also be used to reduce system complexity for over-complex fuzzy inference systems. There are typically two methods to generate fuzzy rule bases, i.e., the knowledge driven and data-driven approaches. Almost all of these approaches only target dense rule bases for conventional fuzzy inference systems. The knowledge-driven methods may be negatively affected by the limited availability of expert knowledge and expert knowledge may be subjective, whilst redundancy often exists in fuzzy rule-based models that are acquired from numerical data. Note that various rule base reduction approaches have been proposed, but they are all based on certain similarity measures and are likely to cause performance deterioration along with the size reduction. This project, for the first time, innovatively applies curvature values to distinguish important features and instances in a dataset, to support the construction of a neat and concise sparse rule base for fuzzy rule interpolation. In addition to working in a three-dimensional problem space, the work also extends the natural three-dimensional curvature calculation to problems with high dimensions, which greatly broadens the applicability of the proposed approach. As a result, the proposed approach alleviates the ‘curse of dimensionality’ and helps to reduce the computational cost for fuzzy inference systems. The proposed approach has been validated and evaluated by three real-world applications. The experimental results demonstrate that the proposed approach is able to generate sparse rule bases with less rules but resulting in better performance, which confirms the power of the proposed system. In addition to fuzzy rule interpolation, the proposed curvature-based approach can also be readily used as a general feature selection tool to work with other machine learning approaches, such as classifiers

Development of an unsupervised remote sensing methodology of detect surface leakage from terrestrial CO2 storage sites

Imperial Users onl

Methodology for high resolution spatial analysis of the physical flood susceptibility of buildings in large river floodplains

The impacts of floods on buildings in urban areas are increasing due to the intensification of extreme weather events, unplanned or uncontrolled settlements and the rising vulnerability of assets. There are some approaches available for assessing the flood damage to buildings and critical infrastructure. To this point, however, it is extremely difficult to adapt these methods widely, due to the lack of high resolution classification and characterisation approaches for built structures. To overcome this obstacle, this work presents: first, a conceptual framework for understanding the physical flood vulnerability and the physical flood susceptibility of buildings, second, a methodological framework for the combination of methods and tools for a large-scale and high-resolution analysis and third, the testing of the methodology in three pilot sites with different development conditions. The conceptual framework narrows down an understanding of flood vulnerability, physical flood vulnerability and physical flood susceptibility and its relation to social and economic vulnerabilities. It describes the key features causing the physical flood susceptibility of buildings as a component of the vulnerability. The methodological framework comprises three modules: (i) methods for setting up a building topology, (ii) methods for assessing the susceptibility of representative buildings of each building type and (iii) the integration of the two modules with technological tools. The first module on the building typology is based on a classification of remote sensing data and GIS analysis involving seven building parameters, which appeared to be relevant for a classification of buildings regarding potential flood impacts. The outcome is a building taxonomic approach. A subsequent identification of representative buildings is based on statistical analyses and membership functions. The second module on the building susceptibility for representative buildings bears on the derivation of depth-physical impact functions. It relates the principal building components, including their heights, dimensions and materials, to the damage from different water levels. The material’s susceptibility is estimated based on international studies on the resistance of building materials and a fuzzy expert analysis. Then depth-physical impact functions are calculated referring to the principal components of the buildings which can be affected by different water levels. Hereby, depth-physical impact functions are seen as a means for the interrelation between the water level and the physical impacts. The third module provides the tools for implementing the methodology. This tool compresses the architecture for feeding the required data on the buildings with their relations to the building typology and the building-type specific depth-physical impact function supporting the automatic process. The methodology is tested in three flood plains pilot sites: (i) in the settlement of the Barrio Sur in Magangué and (ii) in the settlement of La Peña in Cicuco located on the flood plain of Magdalena River, Colombia and (iii) in a settlement of the city of Dresden, located on the Elbe River, Germany. The testing of the methodology covers the description of data availability and accuracy, the steps for deriving the depth-physical impact functions of representative buildings and the final display of the spatial distribution of the physical flood susceptibility. The discussion analyses what are the contributions of this work evaluating the findings of the methodology’s testing with the dissertation goals. The conclusions of the work show the contributions and limitations of the research in terms of methodological and empirical advancements and the general applicability in flood risk management.:1 INTRODUCTION 1 1.1 Background 1 1.2 State of the art 2 1.3 Problem statement 6 1.4 Objectives 6 1.5 Approach and outline 6 2 CONCEPTUAL FRAMEWORK 9 2.1 Flood vulnerability 10 2.2 Physical flood vulnerability 12 2.3 Physical flood susceptibility 14 3 METHODOLOGICAL FRAMEWORK 23 3.1 Module 1: Building taxonomy for settlements 24 3.1.1 Extraction of building features 24 3.1.2 Derivation of building parameters for setting up a building taxonomy 38 3.1.3 Selection of representative buildings for a building susceptibility assessment 51 3.2 Module 2: Physical susceptibility of representative buildings 57 3.2.1 Identification of building components 57 3.2.2 Qualification of building material susceptibility 62 3.2.3 Derivation of a depth-physical impact function 71 3.3 Module 3: Technological integration 77 3.3.1 Combination of the depth-physical impact function with the building taxonomic code 77 3.3.2 Tools supporting the physical susceptibility analysis 78 3.3.3 The users and their requirements 79 4 RESULTS OF THE METHODOLOGY TESTING 83 4.1 Pilot site “Kleinzschachwitz” – Dresden, Germany – Elbe River 83 4.1.1 Module 1: Building taxonomy – “Kleinzschachwitz” 85 4.1.2 Module 2: Physical susceptibility of representative buildings – “Kleinzschachwitz” 97 4.1.3 Module 3: Technological integration – “Kleinzschachwitz” 103 4.2 Pilot site “La Peña” – Cicuco, Colombia – Magdalena River 107 4.2.1 Module 1: Building taxonomy – “La Peña” 108 4.2.2 Module 2: Physical susceptibility of representative buildings – “La Peña” 121 4.2.3 Module 3: Technological integration– “La Peña” 129 4.3 Pilot site “Barrio Sur” – Magangué, Colombia – Magdalena River 133 4.3.1 Module 1: Building taxonomy – “Barrio Sur” 133 4.3.2 Module 2: Physical susceptibility of representative buildings – “Barrio Sur” 141 4.3.3 Module 3: Technological integration – “Barrio Sur” 147 4.4 Empirical findings 151 4.4.1 Empirical findings of Module 1 151 4.4.2 Empirical findings of Module 2 155 4.4.3 Empirical findings of Module 3 157 4.4.4 Guidance of the methodology 157 5 DISCUSSION 161 5.1 Discussion on the conceptual framework 161 5.2 Discussion on the methodological framework 161 5.2.1 Discussion on Module 1: the building taxonomic approach 162 5.2.2 Discussion on Module 2: the depth-physical impact function 164 6 CONCLUSIONS AND OUTLOOK 167 6.1 Conclusions 167 6.2 Outlook 168 REFERENCES 171 INDEX OF FIGURES 199 INDEX OF TABLES 201 APPENDICES 203In vielen Städten nehmen die Auswirkungen von Hochwasser auf Gebäude aufgrund immer extremerer Wetterereignisse, unkontrollierbarer Siedlungsbauten und der steigenden Vulnerabilität von Besitztümern stetig zu. Es existieren zwar bereits Ansätze zur Beurteilung von Wasserschäden an Gebäuden und Infrastrukturknotenpunkten. Doch ist es bisher schwierig, diese Methoden großräumig anzuwenden, da es an einer präzisen Klassifizierung und Charakterisierung von Gebäuden und anderen baulichen Anlagen fehlt. Zu diesem Zweck sollen in dieser Arbeit erstens ein Konzept für ein genaueres Verständnis der physischen Vulnerabilität von Gebäuden gegenüber Hochwasser dargelegt, zweitens ein methodisches Verfahren zur Kombination der bestehenden Methoden und Hilfsmittel mit dem Ziel einer großräumigen und hochauflösenden Analyse erarbeitet und drittens diese Methode an drei Pilotstandorten mit unterschiedlichem Ausbauzustand erprobt werden. Die Rahmenbedingungen des Konzepts grenzen die Begriffe der Vulnerabilität, der physischen Vulnerabilität und der physischen Anfälligkeit gegenüber Hochwasser ein und erörtern deren Beziehung zur sozialen und ökonomischen Vulnerabilität. Es werden die Merkmale der physischen Anfälligkeit von Gebäuden gegenüber Hochwasser als Bestandteil der Vulnerabilität definiert. Das methodische Verfahren umfasst drei Module: (i) Methoden zur Erstellung einer Gebäudetypologie, (ii) Methoden zur Bewertung der Anfälligkeit repräsentativer Gebäude jedes Gebäudetyps und (iii) die Kombination der beiden Module mit Hilfe technologischer Hilfsmittel. Das erste Modul zur Gebäudetypologie basiert auf der Klassifizierung von Fernerkundungsdaten und GIS-Analysen anhand von sieben Gebäudeparametern, die sich für die Klassifizierung von Gebäuden bezüglich ihres Risikopotenzials bei Hochwasser als wichtig erweisen. Daraus ergibt sich ein Ansatz zur Gebäudeklassifizierung. Die anschließende Ermittlung repräsentativer Gebäude beruht auf statistischen Analysen und Zugehörigkeitsfunktionen. Das zweite Modul zur Anfälligkeit repräsentativer Gebäude beruht auf der Ableitung von Funktion von Wasserstand und physischer Einwirkung. Es setzt die relevanten Gebäudemerkmale, darunter Höhe, Maße und Materialien, in Beziehung zum erwartbaren Schaden bei unterschiedlichen Wasserständen. Die Materialanfälligkeit wird aufgrund internationaler Studien zur Festigkeit von Baustoffen sowie durch Anwendung eines Fuzzy-Logic-Expertensystems eingeschätzt. Anschließend werden Wasserstand-Schaden-Funktionen unter Einbeziehung der Hauptgebäudekomponenten berechnet, die durch unterschiedliche Wasserstände in Mitleidenschaft gezogen werden können. Funktion von Wasserstand und physischer Einwirkung dienen hier dazu, den jeweiligen Wasserstand und die physischen Auswirkung in Beziehung zueinander zu setzen. Das dritte Modul stellt die zur Umsetzung der Methoden notwendigen Hilfsmittel vor. Zur Unterstützung des automatisierten Verfahrens dienen Hilfsmittel, die die Gebäudetypologie mit der Funktion von Wasserstand und physischer Einwirkung für Gebäude in Hochwassergebieten kombinieren. Die Methoden wurden anschließend in drei hochwassergefährdeten Pilotstandorten getestet: (i) in den Siedlungsgebieten von Barrio Sur in Magangué und (ii) von La Pena in Cicuco, zwei Überschwemmungsgebiete des Magdalenas in Kolumbien, und (iii) im Stadtgebiet von Dresden, das an der Elbe liegt. Das Testverfahren umfasst die Beschreibung der Datenverfügbarkeit und genauigkeit, die einzelnen Schritte zur Analyse der. Funktion von Wasserstand und physischer Einwirkung repräsentativer Gebäude sowie die Darstellung der räumlichen Verteilung der physischen Anfälligkeit für Hochwasser. In der Diskussion wird der Beitrag dieser Arbeit zur Beurteilung der Erkenntnisse der getesteten Methoden anhand der Ziele dieser Dissertation analysiert. Die Folgerungen beleuchten abschließend die Fortschritte und auch Grenzen der Forschung hinsichtlich methodischer und empirischer Entwicklungen sowie deren allgemeine Anwendbarkeit im Bereich des Hochwasserschutzes.:1 INTRODUCTION 1 1.1 Background 1 1.2 State of the art 2 1.3 Problem statement 6 1.4 Objectives 6 1.5 Approach and outline 6 2 CONCEPTUAL FRAMEWORK 9 2.1 Flood vulnerability 10 2.2 Physical flood vulnerability 12 2.3 Physical flood susceptibility 14 3 METHODOLOGICAL FRAMEWORK 23 3.1 Module 1: Building taxonomy for settlements 24 3.1.1 Extraction of building features 24 3.1.2 Derivation of building parameters for setting up a building taxonomy 38 3.1.3 Selection of representative buildings for a building susceptibility assessment 51 3.2 Module 2: Physical susceptibility of representative buildings 57 3.2.1 Identification of building components 57 3.2.2 Qualification of building material susceptibility 62 3.2.3 Derivation of a depth-physical impact function 71 3.3 Module 3: Technological integration 77 3.3.1 Combination of the depth-physical impact function with the building taxonomic code 77 3.3.2 Tools supporting the physical susceptibility analysis 78 3.3.3 The users and their requirements 79 4 RESULTS OF THE METHODOLOGY TESTING 83 4.1 Pilot site “Kleinzschachwitz” – Dresden, Germany – Elbe River 83 4.1.1 Module 1: Building taxonomy – “Kleinzschachwitz” 85 4.1.2 Module 2: Physical susceptibility of representative buildings – “Kleinzschachwitz” 97 4.1.3 Module 3: Technological integration – “Kleinzschachwitz” 103 4.2 Pilot site “La Peña” – Cicuco, Colombia – Magdalena River 107 4.2.1 Module 1: Building taxonomy – “La Peña” 108 4.2.2 Module 2: Physical susceptibility of representative buildings – “La Peña” 121 4.2.3 Module 3: Technological integration– “La Peña” 129 4.3 Pilot site “Barrio Sur” – Magangué, Colombia – Magdalena River 133 4.3.1 Module 1: Building taxonomy – “Barrio Sur” 133 4.3.2 Module 2: Physical susceptibility of representative buildings – “Barrio Sur” 141 4.3.3 Module 3: Technological integration – “Barrio Sur” 147 4.4 Empirical findings 151 4.4.1 Empirical findings of Module 1 151 4.4.2 Empirical findings of Module 2 155 4.4.3 Empirical findings of Module 3 157 4.4.4 Guidance of the methodology 157 5 DISCUSSION 161 5.1 Discussion on the conceptual framework 161 5.2 Discussion on the methodological framework 161 5.2.1 Discussion on Module 1: the building taxonomic approach 162 5.2.2 Discussion on Module 2: the depth-physical impact function 164 6 CONCLUSIONS AND OUTLOOK 167 6.1 Conclusions 167 6.2 Outlook 168 REFERENCES 171 INDEX OF FIGURES 199 INDEX OF TABLES 201 APPENDICES 203El impacto de las inundaciones sobre los edificios en zonas urbanas es cada vez mayor debido a la intensificación de los fenómenos meteorológicos extremos, asentamientos no controlados o no planificados y su creciente vulnerabilidad. Hay métodos disponibles para evaluar los daños por inundación en edificios e infraestructuras críticas. Sin embargo, es muy difícil implementar estos métodos sistemáticamente en grandes áreas debido a la falta de clasificación y caracterización de estructuras construidas en resoluciones detalladas. Para superar este obstáculo, este trabajo se enfoca, en primer lugar, en desarrollar un marco conceptual para comprender la vulnerabilidad y susceptibilidad física de edificios por inudaciones, en segundo lugar, en desarrollar un marco metodológico para la combinación de los métodos y herramientas para una análisis de alta resolución y en tercer lugar, la prueba de la metodología en tres sitios experimentales, con distintas condiciones de desarrollo. El marco conceptual se enfoca en comprender la vulnerabilidad y susceptibility de las edificaciones frente a inundaciones, y su relación con la vulnerabilidad social y económica. En él se describen las principales características físicas de la susceptibilidad de edificicaiones como un componente de la vulnerabilidad. El marco metodológico consta de tres módulos: (i) métodos para la derivación de topología de construcciones, (ii) métodos para evaluar la susceptibilidad de edificios representativos y (iii) la integración de los dos módulos a través herramientas tecnológicas. El primer módulo de topología de construcciones se basa en una clasificación de datos de sensoramiento rémoto y procesamiento SIG para la extracción de siete parámetros de las edficaciones. Este módulo parece ser aplicable para una clasificación de los edificios en relación con los posibles impactos de las inundaciones. El resultado es una taxonomía de las edificaciones y una posterior identificación de edificios representativos que se basa en análisis estadísticos y funciones de pertenencia. El segundo módulo consiste en el análisis de susceptibilidad de las construcciones representativas a través de funciones de profundidad del impacto físico. Las cuales relacionan los principales componentes de la construcción, incluyendo sus alturas, dimensiones y materiales con los impactos físicos a diferentes niveles de agua. La susceptibilidad del material se calcula con base a estudios internacionales sobre la resistencia de los materiales y un análisis a través de sistemas expertos difusos. Aquí, las funciones de profundidad de impacto físico son considerados como un medio para la interrelación entre el nivel del agua y los impactos físicos. El tercer módulo proporciona las herramientas necesarias para la aplicación de la metodología. Estas herramientas tecnológicas consisten en la arquitectura para la alimentación de los datos relacionados a la tipología de construcciones con las funciones de profundidad del impacto físico apoyado en procesos automáticos. La metodología es probada en tres sitios piloto: (i) en el Barrio Sur en Magangué y (ii) en la barrio de La Peña en Cicuco situado en la llanura inundable del Río Magdalena, Colombia y (iii) en barrio Kleinzschachwitz de la ciudad de Dresden, situado a orillas del río Elba, en Alemania. Las pruebas de la metodología abarca la descripción de la disponibilidad de los datos y la precisión, los pasos a seguir para obtener las funciones profundidad de impacto físico de edificios representativos y la presentación final de la distribución espacial de la susceptibilidad física frente inundaciones El discusión analiza las aportaciones de este trabajo y evalua los resultados de la metodología con relación a los objetivos. Las conclusiones del trabajo, muestran los aportes y limitaciones de la investigación en términos de avances metodológicos y empíricos y la aplicabilidad general de gestión del riesgo de inundaciones.:1 INTRODUCTION 1 1.1 Background 1 1.2 State of the art 2 1.3 Problem statement 6 1.4 Objectives 6 1.5 Approach and outline 6 2 CONCEPTUAL FRAMEWORK 9 2.1 Flood vulnerability 10 2.2 Physical flood vulnerability 12 2.3 Physical flood susceptibility 14 3 METHODOLOGICAL FRAMEWORK 23 3.1 Module 1: Building taxonomy for settlements 24 3.1.1 Extraction of building features 24 3.1.2 Derivation of building parameters for setting up a building taxonomy 38 3.1.3 Selection of representative buildings for a building susceptibility assessment 51 3.2 Module 2: Physical susceptibility of representative buildings 57 3.2.1 Identification of building components 57 3.2.2 Qualification of building material susceptibility 62 3.2.3 Derivation of a depth-physical impact function 71 3.3 Module 3: Technological integration 77 3.3.1 Combination of the depth-physical impact function with the building taxonomic code 77 3.3.2 Tools supporting the physical susceptibility analysis 78 3.3.3 The users and their requirements 79 4 RESULTS OF THE METHODOLOGY TESTING 83 4.1 Pilot site “Kleinzschachwitz” – Dresden, Germany – Elbe River 83 4.1.1 Module 1: Building taxonomy – “Kleinzschachwitz” 85 4.1.2 Module 2: Physical susceptibility of representative buildings – “Kleinzschachwitz” 97 4.1.3 Module 3: Technological integration – “Kleinzschachwitz” 103 4.2 Pilot site “La Peña” – Cicuco, Colombia – Magdalena River 107 4.2.1 Module 1: Building taxonomy – “La Peña” 108 4.2.2 Module 2: Physical susceptibility of representative buildings – “La Peña” 121 4.2.3 Module 3: Technological integration– “La Peña” 129 4.3 Pilot site “Barrio Sur” – Magangué, Colombia – Magdalena River 133 4.3.1 Module 1: Building taxonomy – “Barrio Sur” 133 4.3.2 Module 2: Physical susceptibility of representative buildings – “Barrio Sur” 141 4.3.3 Module 3: Technological integration – “Barrio Sur” 147 4.4 Empirical findings 151 4.4.1 Empirical findings of Module 1 151 4.4.2 Empirical findings of Module 2 155 4.4.3 Empirical findings of Module 3 157 4.4.4 Guidance of the methodology 157 5 DISCUSSION 161 5.1 Discussion on the conceptual framework 161 5.2 Discussion on the methodological framework 161 5.2.1 Discussion on Module 1: the building taxonomic approach 162 5.2.2 Discussion on Module 2: the depth-physical impact function 164 6 CONCLUSIONS AND OUTLOOK 167 6.1 Conclusions 167 6.2 Outlook 168 REFERENCES 171 INDEX OF FIGURES 199 INDEX OF TABLES 201 APPENDICES 20

Scenario-based analysis of the impacts of lake drying on food production in the Lake Urmia Basin of Northern Iran

In many parts of the world, lake drying is caused by water management failures, while the phenomenon is exacerbated by climate change. Lake Urmia in Northern Iran is drying up at such an alarming rate that it is considered to be a dying lake, which has dire consequences for the whole region. While salinization caused by a dying lake is well understood and known to influence the local and regional food production, other potential impacts by dying lakes are as yet unknown. The food production in the Urmia region is predominantly regional and relies on local water sources. To explore the current and projected impacts of the dying lake on food production, we investigated changes in the climatic conditions, land use, and land degradation for the period 1990–2020. We examined the environmental impacts of lake drought on food production using an integrated scenario-based geoinformation framework. The results show that the lake drought has significantly affected and reduced food production over the past three decades. Based on a combination of cellular automaton and Markov modeling, we project the food production for the next 30 years and predict it will reduce further. The results of this study emphasize the critical environmental impacts of the Urmia Lake drought on food production in the region. We hope that the results will encourage authorities and environmental planners to counteract these issues and take steps to support food production. As our proposed integrated geoinformation approach considers both the extensive impacts of global climate change and the factors associated with dying lakes, we consider it to be suitable to investigate the relationships between environmental degradation and scenario-based food production in other regions with dying lakes around the world