Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

The Optimisation of Elementary and Integrative Content-Based Image Retrieval Techniques

Image retrieval plays a major role in many image processing applications. However, a number of factors (e.g. rotation, non-uniform illumination, noise and lack of spatial information) can disrupt the outputs of image retrieval systems such that they cannot produce the desired results. In recent years, many researchers have introduced different approaches to overcome this problem. Colour-based CBIR (content-based image retrieval) and shape-based CBIR were the most commonly used techniques for obtaining image signatures. Although the colour histogram and shape descriptor have produced satisfactory results for certain applications, they still suffer many theoretical and practical problems. A prominent one among them is the well-known “curse of dimensionality “. In this research, a new Fuzzy Fusion-based Colour and Shape Signature (FFCSS) approach for integrating colour-only and shape-only features has been investigated to produce an effective image feature vector for database retrieval. The proposed technique is based on an optimised fuzzy colour scheme and robust shape descriptors. Experimental tests were carried out to check the behaviour of the FFCSS-based system, including sensitivity and robustness of the proposed signature of the sampled images, especially under varied conditions of, rotation, scaling, noise and light intensity. To further improve retrieval efficiency of the devised signature model, the target image repositories were clustered into several groups using the k-means clustering algorithm at system runtime, where the search begins at the centres of each cluster. The FFCSS-based approach has proven superior to other benchmarked classic CBIR methods, hence this research makes a substantial contribution towards corresponding theoretical and practical fronts

An Information Security Control Assessment Methodology for Organizations

In an era where use and dependence of information systems is significantly high, the threat of incidents related to information security that could jeopardize the information held by organizations is more and more serious. Alarming facts within the literature point to inadequacies in information security practices, particularly the evaluation of information security controls in organizations. Research efforts have resulted in various methodologies developed to deal with the information security controls assessment problem. A closer look at these traditional methodologies highlights various weaknesses that can prevent an effective information security controls assessment in organizations. This dissertation develops a methodology that addresses such weaknesses when evaluating information security controls in organizations. The methodology, created using the Fuzzy Logic Toolbox of MATLAB based on fuzzy theory and fuzzy logic, uses fuzzy set theory which allows for a more accurate assessment of imprecise criteria than traditional methodologies. It is argued and evidenced that evaluating information security controls using fuzzy set theory addresses existing weaknesses found in the literature for traditional evaluation methodologies and, thus, leads to a more thorough and precise assessment. This, in turn, results in a more effective selection of information security controls and enhanced information security in organizations. The main contribution of this research to the information security literature is the development of a fuzzy set theory-based assessment methodology that provides for a thorough evaluation of ISC in organizations. The methodology just created addresses the weaknesses or limitations identified in existing information security control assessment methodologies, resulting in an enhanced information security in organizations. The methodology can also be implemented in a spreadsheet or software tool, and promote usage in practical scenarios where highly complex methodologies for ISC selection are impractical. Moreover, the methodology fuses multiple evaluation criteria to provide a holistic view of the overall quality of information security controls, and it is easily extended to include additional evaluation criteria factor not considered within this dissertation. This is one of the most meaningful contributions from this dissertation. Finally, the methodology provides a mechanism to evaluate the quality of information security controls in various domains. Overall, the methodology presented in this dissertation proved to be a feasible technique for evaluating information security controls in organizations

CFLCA: High Performance based Heart disease Prediction System using Fuzzy Learning with Neural Networks

Human Diseases are increasing rapidly in today’s generation mainly due to the life style of people like poor diet, lack of exercises, drugs and alcohol consumption etc. But the most spreading disease that is commonly around 80% of people death direct and indirectly heart disease basis. In future (approximately after 10 years) maximum number of people may expire cause of heart diseases. Due to these reasons, many of researchers providing enormous remedy, data analysis in various proposed technologies for diagnosing heart diseases with plenty of medical data which is related to heart disease. In field of Medicine regularly receives very wide range of medical data in the form of text, image, audio, video, signal pockets, etc. This database contains raw dataset which consist of inconsistent and redundant data. The health care system is no doubt very rich in aspect of storing data but at the same time very poor in fetching knowledge. Data mining (DM) methods can help in extracting a valuable knowledge by applying DM terminologies like clustering, regression, segmentation, classification etc. After the collection of data when the dataset becomes larger and more complex than data mining algorithms and clustering algorithms (D-Tree, Neural Networks, K-means, etc.) are used. To get accuracy and precision values improved with proposed method of Cognitive Fuzzy Learning based Clustering Algorithm (CFLCA) method. CFLCA methodology creates advanced meta indexing for n-dimensional unstructured data. The heart disease dataset used after data enrichment and feature engineering with UCI machine learning algorithm, attain high level accurate and prediction rate. Through this proposed CFLCA algorithm is having high accuracy, precision and recall values of data analysis for heart diseases detection

Advanced risk management in offshore terminals and marine ports

This research aims to propose a Risk Management (RM) framework and develop a generic risk-based model for dealing with potential hazards and risk factors associated with offshore terminals' and marine ports' operations and management. Hazard identification was conducted through an appropriate literature review of major risk factors of these logistic infrastructures. As a result in the first phase of this research a Fuzzy Analytical Hierarchal Process (FAHP) method was used for determining the relative weights of the risk factors identified via the literature review. This has led to the development of a generic risk -based model which can help related industrial professionals and risk managers assess the risk factors and develop appropriate strategies to take preventive/corrective actions for mitigation purposes, with a view of maintaining efficient offshore terminals' and marine ports' operations and management. In the second phase of the research the developed risk-based model incorporating Fuzzy Set Theory (FST), an Evidential Reasoning (ER) approach and the IDS software were used to evaluate the risk levels of different ports in real situations using a case study. The IDS software based on an ER approach was used to aggregate the previously determined relative weights of the risk factors with the new evaluation results of risk levels for the real ports. The third phase of the research made use of the Cause and Consequence Analysis (CCA) including the Fault Tree Analysis (FTA) and Event Tree Analysis (ETA) under a fuzzy environment, to analyse in detail the most significant risk factors determined from the first phase of the research, using appropriate case-studies. In the fourth phase of the research an individual RM strategy was tailored and implemented on the most significant risk factor identified previously. In the last phase of the research and in order to complete the RM cycle, the best mitigation strategies were introduced and evaluated in the form of ideal solutions for mitigating the identified risk factors. All methods used in this research have quantitative and qualitative nature. Expert judgements carried out for gathering the required information accounted for the majority of data collected. The proposed RM framework can be a useful method for managers and auditors when conducting their RM programmes in the offshore and marine industries. The novelty of this research can help the Quality, Health, Safety, Environment and Security (QHSES) managers, insurers and risk managers in the offshore and marine industries investigate the potential hazards more appropriately if there is uncertainty of data sources. In this research with considering strategic management approaches to RM development the proposed RM framework and risk based model contribute to knowledge by developing and evaluating an effective methodology for future use of the RM professionals

A transdisciplinary-based coupled approach for vulnerability assessment in the context of natural resource-based conflicts using remote sensing, spatial statistics and fuzzy logic adapted models

This thesis presents a new approach for investigating vulnerability assessment in the context of natural resource-based conflicts (NRBCs). It develops SEFLAME-CM (A Spatially Explicit Fuzzy Logic Adapted Modeling for Conflict Management). SEFLAME-CM is an innovative tool that improves the holistic vulnerability assessment (the external and the internal driver components) of NRBCs at a community scale towards co-creating scenarios for future conflict management (CM) strategies. It was perceived specifically that a methodology with the worldviews and the knowledge of the actors is capable of understanding conflicts better than the previous linear models such as the Multiple Linear Regression Model (MLRM) and the Multinomial Logistic Regression Models (MNLR). SEFLAME-CM, an adapted model proved to be a reliable modeling tool for capturing the non-linearity, uncertainty, and ambiguity characteristics of the vulnerability assessments of NRBCs. The spatial extent of the study was limited to selected test sites within Ogoni and Okrika territories of the Niger Delta region of Nigeria. These comprise of LGAs/communities and villages. Despite the uncertainty involved in real-world problems such as the Socio-ecological Systems (SES), the NRBCs, the increase in the computational power in the last decades has enabled the modeling of the complexities involved. Issues that cut across social-economic and biophysical interfaces, such as NRBCs, require both the knowledge of the experts and that of the local actors. This is thus following the recommendation of Seidl et al. (2013) on science with social research in the Anthropocene: “A systems perspective on coupled human-environmental systems (HES) help to address the inherent complexities. Additionally, a thorough interaction between science and society (i.e., transdisciplinarity) is necessary, as sustainable transitions are sometimes contested and can cause conflicts. In order to navigate complexities regarding the delicate interaction of scientific research with societal decisions these processes must proceed in a structured and functional way” (: 5). The main sections of the thesis after the introduction and the study area description began by reconceptualizing NRBCs. Current publications indicate that the study of NRBCs in the era of the Anthropocene needs to be reconceptualized to be able to explore strategies for conflict management which are beyond the hitherto military strategies often employed in the different international interventions on conflicts in the developing countries, particularly in Africa and Asia (Section 3). Multilateral agencies such as the United Nations, the North Atlantic Treaty Organization (NATO) and other peacekeeping international organizations, often embark on the use of military strategies which have proved to be unsustainable. This is because NRBCs are complex and “wicked” in nature (Brauch, 2003a, Spring et al., 2009, Brauch, 2010, Brauch, 2016b, Brauch, 2016a). By reconceptualizing NRBCs, the research firstly clarifies the concepts of risk, risk perception resilience, vulnerability assessment, and the “Vulnerability Cube”. Secondly, the bridging of the gap between the concepts of a holistic vulnerability assessment (HVA) and the NRBCs was discussed. Thirdly, the integration of HVA of NRBCs into fuzzy logic theory was presented. This was implemented in Section 5. The main argument of this Section 3 is that the complex characteristics of vulnerability to NRBCs require the use of a non-linear theoretical model that is adaptable and capable of addressing the complexities of NRBCs research. After the reconceptualization of NRBCs in Section 3, the thesis then followed the three phases of the transdisciplinary research approach proposed by Mauser et al. (2013) (see also Section 1.4 and Section 5.1). This phase dealt with a joint problem framing. This helped to operationalize NRBCs for simulation (see Section 4). The problem of NRBCs was framed by integrating the problem structuring methods (PSMs) (e.g GIS) with the qualitative method (e.g discourse analysis). The results of this joint problem framing showed the different drivers of NRBCs which were selected by the actors. With the aid of GIS, the actors’ mental maps were presented based on the different dimensions of NRBCs vulnerability. The results also show the similarities in the interest of local actors. The joint problem framing equally helped to organize and operationalize the input variables that were used for the modeling phase of the research. Hence the operationalization of the conflict drivers/factors generated from the joint problem framing is seen as a critical step in the transdisciplinary-based coupled approach to NRBCs. The second phase of the research after the joint problem phase is a co-production of knowledge for managing the NRBCs with the integration of knowledge from the actors. Here the overall research methodology and the algorithm of SEFLAME-CM were presented in Section 5. This was validated following a rigorous validation process (see Section 6). Prior to the validation of SEFLAME-CM, a non-spatially explicit model, Fuzzy Logic Adapted Modeling for Conflict Management (FLAME-CM) was developed, improved and validated following an iterative process using scores like R2, p-values, RMSE. The results of the validated FLAME-CM was conducted at village scale as a test site, but this was transferred to a spatially explicit context using a resolution of 200 x 200m2. The content of the FLAME-CM helped to establish a SEFLAME-CM. The validation of SEFLAME-CM is, therefore, an extension of FLAME-CM validation result (Figure 6.7). As seen in Figure 6.7, the result of the validated SEFLAME-CM is the final output of the model and the process does not have to go back to the FLAME-CM process. Figure 6.7 shows the schematics of the overall validation process. SEFLAME-CM was firstly validated by comparing outputs with spatial multi-criteria evaluation for conflict management (SMCE-CM) and secondly by using satellite remote sensing data. The result of the latter proved that the model result corresponds with the real world data (remote sensing). The result of the former shows that SEFLAME-CM performed better even when compared with the already established model of SMCE-CM. However, the advantage of SEFLAME-CM is that it accepts weighted inputs by the actors or stakeholders right from the problem framing phase. The entire methodological procedure of the research, therefore, shows a blend of methodology from the natural sciences and the social sciences, and integration of integration co-created knowledge with the actors. The third and the last phase of the research process of this thesis is the outlook and conclusion (see Section 7). It dealt with the research proposal for co-construction of scenario pathways for long-term conflict management strategies. The scenario construction, when applied in the future, would address the positive potential of collective natural resource management for longer-term peacebuilding and sustainable peace (Bruch et al., 2009, Ratner et al., 2013). It was conceived that after developing and validating an innovative spatially explicit component of the simulation model, SEFLAME-CM, the next logical step of the thesis is to apply the methodology for future conflict management. The “scenario” proposal for future CM is a period from 2016 to 2060. The justification is that while global scenarios cover time horizons of say 50–100 years, local scenarios focus on shorter periods, 20–30 years (Folhes et al., 2015). The choice of a scenario time frame that is longer than 20–30 years is because the study outcome is considered to be applicable to regional or national governance. When the co-constructed scenarios are implemented, they would help to explore CM options and strategies that can influence policy and decision making over natural resource management (NRM). For example, in the Niger Delta, the investments in CM can be re-channeled from military strategies and the current unsustainable Presidential Amnesty Programmes to achieve both peacebuilding and sustainability. Since social resilience is a “naturally emergent” response to harm or disaster, it is argued that conflict management plans must recognize and build on community adaptive capacities, while the areas of high resilience in terms of peace should be priority areas for future NRM. In a nutshell, the thesis enables the application of a transdisciplinary-based coupled approach that is based on co-creation of knowledge between the experts and the local actors in the management of NRBCs. Both the external and internal vulnerability drivers of NRBCs were assessed. The results demonstrate that environmental degradation, socio-economic and political drivers of resource conflict can be addressed holistically as well as being treated as separate drivers in the interplay of natural resources and conflicts at the community scale. Though there are limitations, relating to cost, time and the complex social processes involved in modeling a real-world process, the results at a fine-grained spatial and temporal scale proved to be very useful and form the basis for supporting integrated coastal zone management (ICZM) strategies for the future management and development of the Niger Delta region. The model remains very adaptable to other NRBCs cases in Africa and other regions of the world. This is especially where both natural resource extraction and conflicts intertwine, and particularly when there is either data scarcity or the available data sets are imprecise.Diese Arbeit demonstriert eine neue Herangehensweise zur Analyse von Vulnerabilität gegenüber Konflikten, die auf natürlichen Resourcen beruhen (Natural Resource Based Conflicts: NRBCs). Gezeigt wird die Entwicklung von SEFLAME-CM-A, ein räumlich explizites Fuzzy Logic Modell für Konfliktmanagement. SEFLAME-CM-A ist ein innovatives Tool, welches an co-konstruierte Klimamodellszenarien unter verschiedenen Bedingungen anpassbar ist. Im Speziellen wurde festgestellt, dass eine Methode mit weltweitem Blick und Expertenwissen besser dazu in der Lage ist Konflikte zu erklären, als die bisherigen linearen Modelle, wie etwa multivariate lineare Regressionen (MLRM) oder multinomiale logistische Regressionen (MNLR). SEFLAME-CM zeigte sich als verlässliches Tool um die Nicht-Linearitäten, Unsicherheiten, fehlende Präzision und Mehrdeutigkeiten abzufangen, welche Vulnerabilitätsanalysen prinzipiell mit sich bringen. Das räumliche Ausmaß der Studie ist auf ausgewählte Gebiete im Niger Delta begrenzt, die in LGAs/Communitys und Dörfer strukturiert sind. Trotz Unsicherheiten, welche bei realen Anwendung der NRBCs eine Rolle spielen, z.B. sozial-ökonomische Systeme, ermöglichte die zunehmende Leistungsstärke von Computern in den vergangenen Jahrzehnten auch Modellierungen von Sachverhalten höherer Komplexität. Probleme, die sozio-ökonomische und biophysikalische Räume spalten, schaffen eine Notwendigkeit sowohl für Expertenwissen, als auch für Mitwirken der lokal Beteiligten. Seidl et al. (2013) empfehlen für eine Wissenschaft mit Sozialforschung im Anthropozän, dass die Perspektive auf gekoppelte Mensch-Umwelt-Beziehungen dabei helfe, die damit einhergehende Komplixität besser berücksichtigen zu können. Zusätzlich sei eine gewissenhafte Interaktion zwischen Wissenschaft und Gesellschaft (also Interdisziplinarität) notwendig, da nachhaltige Umstellungen manchmal umstritten seien und Konflikte hervorrufen könnten. Um sich in der Komplexität zurechtzufinden, welche die heiklen Interaktionen zwischen wissenschaftlicher Forschung und gesellschaftlichen Entscheidungen mit sich bringen, müssten diese Prozesse auf strukturierte und funktionale Art und Weise ausgeführt werden. Das Hauptkapitel dieser Arbeit, welches sich an die Einleitung und die Beschreibung des Untersuchungsgebiet anschließt, begann mit der Entwicklung eines neuen Denkansatzes bezüglich NRBCs. Aktuelle Veröffentlichungen zeigen, dass die Untersuchung dieser Konflikte neue Strategien des Konfliktmanagements erforderlich macht, die jenseits der bisherigen militärischen Lösungen liegen, wie sie derzeit in Entwicklungsländern vor allem in Afrika und Asien eingesetzt werden. Obwohl sie sich im Anthropozän als nicht nachhaltig erwiesen, da die Ursachen der Konflikte eindeutig in Umweltproblemen zu suchen sind, sind zahlreiche multilaterale Vertretungen wie die Vereinten Nationen, NATO oder internationale Organisationen zur Friedenswahrung auf die militärischen Strategien aufgesprungen (Brauch, 2003a, Spring et al., 2009, Brauch, 2010, Brauch, 2016b, Brauch, 2016a). Aus diesem Grund klärt diese Studie erstens die Konzepte von Risiko, Risikowahrnehmung, Resilienz, Vulnerabilitätsanalysen und Vulnerabilitätswürfel. Zweitens wurde eine Brücke zwischen dem Konzept der holistischen Vulnerabilitätsanalyse (holistic vulnerability assessment HVA) und NRBCs geschlagen. Drittens wurde die Integration der HVA von NRBCs in die Fuzzy Logic Theorie vorgestellt. Dies wurde in Section 5 eingebaut. Dessen Hauptargument war, dass die komplexen Eigenschaften der NRBCs einem nicht-linearen theoretischen Modell bedürfen, welches sowohl anpassungsfähig ist, als auch der Komplexität der NRBC-Forschung gerecht wird. Nach der Neukonzeptionalisierung von NRBCs in Section 3, folgte die Arbeit schließlich dem Ansatz der drei Phasen transdisziplinärer Forschung von Mauser et al. (2013, siehe Section 1.4). Diese Phase verfolgte einen vereinten Problemlösungsansatz. Dieses Framework mit seiner Strukturierung ermöglichte die Operationalisierung von NRBCs für Computer-Simulationen. Dabei werden problemstrukturierende Methoden, wie beispielsweise GIS, mit qualitativen Methoden, z.B. einer Diskursanalyse, kombiniert. Die Ergebnisse der Implementierung von Problemabgrenzung und –strukturierung zeigt die unterschiedlichen Treiber von Konflikten über Naturresourcen, die von den Akteuren genannt wurden. Mithilfe von GIS wurden Mental Maps der Akteure basierend auf den verschiedenen Dimensionen des Konflikts und der Vulnerabilität visualisiert. Die Ergebnisse zeigen die Gemeinsamkeiten des Interesses lokaler Aktuere. Gleichwohl half die gemeinsame Problemabgrenzung dabei, die Eingangsvariablen zu organisieren, die in der Modellierungsphase genutzt wurden. Deshalb wird die Operationalisierung der Konfliktfaktoren, welche bei der gemeinsamen Problemabgrenzung erzeugt wurde, als kritischer Schritt im interdisziplinären Modellansatz naturresourcenbedinger Konflikte gesehen. Die zweite Phase nach der gemeinsamen Problemphase war die Koproduktion zwischen Wissen über Konflikte über Naturresourcen und die Integration des Wissens der Akteure. Die Methodik und der Algorithmus von SEFLAME-CM wurde in Section 5 vorgestellt und anschließend einem strengen Validierungsprozess unterworfen (Section 6). Vor der Entwicklung des disziplinübergreifenden Modellansatzes SEFLAME-CM, welcher validiert und in dieser Arbeit angewandt wurde, wurde ein ähnliches, aber räumlich nicht explizites Modell – FLAME-CM – entwickelt, verbessert und einem iterativen Prozess folgend mit Methoden wie R², p-Values und RMSE getestet. Das Ergebnis des validierten FLAME-CM wurde auf lokaler Skala durchgeführt, aber dann auf räumlich expliziten Kontext mit einer Auflösung von 200x200 Metern übertragen. Wie in Abbildung 6.7 gezeigt, ist der Modell-Output von SEFLAME-CM final und der Prozess muss nicht länger auf FLAME-CM zurückgestuft werden. Abbildung 6.7 skizziert den übergreifenden Validierungsprozess. SEFLAME-CM wurde zunächst validiert, indem die Outputs mit einer räumlich multikriteriellen Evaluierung im Konfliktmanagement (Spatial Multi-Criteria Evaluation for Conflict Management, SMCE-CM) verglichen wurden. Die Ergebnisse des zuletztgenannten Verfahrens belegten, dass die Modellergebnisse korrekt mit echten Daten (Fernerkundung) übereinstimmen. Das Ergebnis des erstgenannten Verfahrens zeigt, dass SEFLAME-CM bessere Resultate erzielt, selbst wenn es mit dem existierenden Modell SMCE-CM verglichen wird. Der Vorteil von SEFLAME-CM ist jedoch, dass es ohne Weiteres gewichtete Inputs durch die Akteure und Stakeholder direkt in der Phase der Problemabgrenzung annimmt. Die gesamte methodologische Wissenschaftsprozedur zeigt daher einen Methodenmix aus Natur- und Sozialwissenschaften, wie beispielsweise eine integrative Kooperation der verschiedenen Akteure. Die dritte und letzte Phase der Arbeit beinhaltet den Ausblick und die Schlussfolgerung (Section 7). Sie behandelt die Anwendung der gekoppelten Informationen. Diese finale Wissenschaftsphase umfasst eine gemeinschaftliche Erarbeitung von Szenarien und eine Simulation von Langzeitstrategien zum Konflikt-Management. Die Erstellung der Szenarien behandelt das Potential eines gemeinschaftlichen Management natürlicher Resourcen. Eine Verbesserung der Zusammenarbeit wird im Konflikt-Management zunehmend als wichtiger Bestandteil dauerhafter Friedensschließung angesehen (Bruch et al., 2009, Ratner et al., 2013). Nach Entwicklung und Validierung einer innovativen, räumlich expliziten Modell-Komponente, SEFRAME-CM, war der nächste logische Schritt dieser ArbAeit die Methoden auf zukünftiges Konflikt-Management anzuwenden, indem das Management natürlicher Resourcen in Klimamodelle integriert wurde. Der Vorschlag für ein „Szenario“ für zukünftiges Konfliktmanagement beinhaltet die Zeitperiode von 2016 bis 2060. Dies liegt darin begründet, dass sich lokale Szenarien mit 20-30 Jahren (Folhes et al., 2015) auf kürzere Zeiträume konzentrieren, während globale Szenarien einen Horizont von 50-100 Jahren umspannen. Die Wahl fällt auf einen Zeitraum von 20-30 Jahren, da die Ergebnisse der Studie auf regionaler und nationaler Regierungsführung anwendbar sind. Eine Implementierung der gemeinschaftlich konstruierten Szenarien würde dabei helfen Optionen und Strategien des Konfliktmanagements zu erkunden, welche die Politik und deren Entscheidungsträger im Bezug auf Ressourcenmanagement beeinflussen. Eine Investition in Konflikt-Management, z.B. im Niger Delta, kann durch Neuausrichtung militärischer Strategien und das derzeit nicht-nachhaltige Presidential Armnesty Programm geschehen um Friedensbildung und Nachhaltigkeit zu erreichen. Da soziale Resilienz eine naturgemäße Antwort auf Unheil und Katastrophen darstellt, wird oft so argumentiert, dass Pläne zum Konflikt-Management die adaptiven Möglichkeiten der Communitys anerkennt und auf ihnen aufbaut, während Gebiete mit hohem Potential an Resilienz prioritär für Naturresourcen-Management der Zukunft angesehen wird Zusammenfassend lässt sich sagen, dass die in dieser Arbeit dargestellte Herangehensweise die Anwendung eines interdisziplinären Modells ermöglicht, das auf gemeinsam entwickeltem Wissen von Experten und lokalen Akteuren im Management von NRBCs beruht. Sowohl die externen als auch internen Vulnerabilitätstreiber der NRBCs wurden bewertet. Die Ergebnisse zeigen, dass die Degradation der Umwelt sowie sozio-ökonomische als auch politische Treiber von Resourcenkonflikten jeweils holistisch, aber auch als separate Treiber im Zusammenspiel natürlicher Resourcen und Konflikten auf kommunaler Ebene behandelt werden können. Trotz allem gibt es Limitierungen, die hauptsächlich auf den komplexen sozialen Prozessen der realen globalen Prozesse beruhen. Die Ergebnisse aus räumlich und zeitlich hoch aufgelösten Daten zeigte sich als sehr nützlich und stellt die Basis für die Unterstützung der Strategien des integrierte Management von Küstenzonen dar, wie sie für ein zukünftiges Management in der Region des Niger Deltas Anwendung finden soll. Das Modell bleibt dabei stark anpassungsfähig für ähnliche Fälle von NRBCs in Afrika und anderen Regionen der Welt, bei denen biophysikalische, sozio-ökonomische und politische Verbindungen entzweit werden. Dies gilt besonders dann, wenn die Datengrundlage knapp und die verfügbaren Datensätze unpräzise