Adaptive Cooperative Learning Methodology for Oil Spillage Pattern Clustering and Prediction

The serious environmental, economic and social consequences of oil spillages could devastate any nation of the world. Notable aftermath of this effect include loss of (or serious threat to) lives, huge financial losses, and colossal damage to the ecosystem. Hence, understanding the pattern and  making precise predictions in real time is required (as opposed to existing rough and discrete prediction) to give decision makers a more realistic picture of environment. This paper seeks to address this problem by exploiting oil spillage features with sets of collected data of oil spillage scenarios. The proposed system integrates three state-of-the-art tools: self organizing maps, (SOM), ensembles of deep neural network (k-DNN) and adaptive neuro-fuzzy inference system (ANFIS). It begins with unsupervised learning using SOM, where four natural clusters were discovered and used in making the data suitable for classification and prediction (supervised learning) by ensembles of k-DNN and ANFIS. Results obtained showed the significant classification and prediction improvements, which is largely attributed to the hybrid learning approach, ensemble learning and cognitive reasoning capabilities. However, optimization of k-DNN structure and weights would be needed for speed enhancement. The system would provide a means of understanding the nature, type and severity of oil spillages thereby facilitating a rapid response to impending oils spillages. Keywords: SOM, ANFIS, Fuzzy Logic, Neural Network, Oil Spillage, Ensemble Learnin

Comparative Analysis of Neural Network Models for Petroleum Products Pipeline Monitoring

In recent years, Neural Network (NN) has gained popularity in proffering solution to complex nonlinear problems. Monitoring of variations in Petroleum Products Pipeline (PPP) attributes (flow rate, pressure, temperature, viscosity, density, inlet and outlet volume) which changes with time is complex due to existence of non linear interaction amongst the attributes. The existing works on PPP monitoring are limited by lack of capabilities for pattern recognition and learning from previous data. In this paper, NN models with pattern recognition and learning capabilities are compared with a view of selecting the best model for monitoring PPP. Data was collected from Pipelines and Products Marketing Company (PPMC), Port Harcourt, Nigeria. The data was used for NN training, validation and testing with different NN models such as Multilayer Perceptron (MLP), Radial Basis Function (RBF), Generalized Feed Forward (GFF), Support Vector Machine (SVM), Time Delay Network (TDN) and Recurrent Neural Network (RNN). Neuro Solutions 6.0 was used as the front-end-engine for NN training, validation and testing while My Structured Query Language (MySQL) database served as the back-end-engine. Performance of NN models was measured using Mean Squared Error (MSE), Mean Absolute Error (MAE), Correlation Coefficient (r), Akaike Information Criteria (AIC) and Minimum Descriptive Length (MDL). MLP with one hidden layer and three processing elements performed better than other NN models in terms of MSE, MAE, AIC, MDL and r values between the computed and the desired output

Intelligent Vehicular Traffic Control System Using Priority Longest Queue First Model

Traffic congestion of vehicles at road intersections is a growing problem in many developing countries of the world, especially in large urban areas. This stems from a continuous increase in the human population, poor road networks and the proliferation of vehicles for transportation of humans and goods from one location to another towards the performance of civil, social and economic activities. These vehicles often meet at road intersections and desire the Right-of-Way (RoW) towards their destination. This situation always results in race competition, traffic jam and gridlock condition with its attendant effects on time, fuel wastages as well as accident and fire outbreak which often results to loss of lives and property. The conventional traffic light control system which employs a static time cycle for issuance of RoW to each lane at the intersection lacks human-like intelligence and traffic situational awareness

Intelligent Vehicular Traffic Control System Using Priority Longest Queue First Model

Traffic congestion of vehicles at road intersections is a growing problem in many developing countries of the world, especially in large urban areas. This stems from a continuous increase in the human population, poor road networks and the proliferation of vehicles for transportation of humans and goods from one location to another towards the performance of civil, social and economic activities. These vehicles often meet at road intersections and desire the Right-of-Way (RoW) towards their destination. This situation always results in race competition, traffic jam and gridlock condition with its attendant effects on time, fuel wastages as well as accident and fire outbreak which often results to loss of lives and property. The conventional traffic light control system which employs a static time cycle for issuance of RoW to each lane at the intersection lacks human-like intelligence and traffic situational awareness

Human and environmental exposure to hydrocarbon pollution in the Niger Delta:A geospatial approach

This study undertook an integrated geospatial assessment of human and environmental exposure to oil pollution in the Niger Delta using primary and secondary spatial data. This thesis begins by presenting a clear rationale for the study of extensive oil pollution in the Niger Delta, followed by a critical literature review of the potential application of geospatial techniques for monitoring and managing the problem. Three analytical chapters report on the methodological developments and applications of geospatial techniques that contribute to achieving the aim of the study. Firstly, a quantitative assessment of human and environmental exposure to oil pollution in the Niger Delta was performed using a government spill database. This was carried out using Spatial Analysis along Networks (SANET), a geostatistical tool, since oil spills in the region tend to follow the linear patterns of the pipelines. Spatial data on pipelines, oil spills, population and land cover data were analysed in order to quantify the extent of human and environmental exposure to oil pollution. The major causes of spills and spatial factors potentially reinforcing reported causes were analysed. Results show extensive general exposure and sabotage as the leading cause of oil pollution in the Niger Delta. Secondly, a method of delineating the river network in the Niger Delta using Sentinel-1 SAR data was developed, as a basis for modelling potential flow of pollutants in the distributary pathways of the network. The cloud penetration capabilities of SAR sensing are particularly valuable for this application since the Niger Delta is notorious for cloud cover. Vector and raster-based river networks derived from Sentinel-1 were compared to alternative river map products including those from the USGS and ESA. This demonstrated the superiority of the Sentinel-1 derived river network, which was subsequently used in a flow routing analysis to demonstrate the potential for understanding oil spill dispersion. Thirdly, the study applied optical remote sensing for indirect detection and mapping of oil spill impacts on vegetation. Multi-temporal Landsat data was used to delineate the spill impact footprint of a notable 2008 oil spill incident in Ogoniland and population exposure was evaluated. The optical data was effective in impact area delineation, demonstrating extensive and long-lasting population exposure to oil pollution. Overall, this study has successfully assembled and produced relevant spatial and attribute data sets and applied integrated geostatistical analytical techniques to understand the distribution and impacts of oil spills in the Niger Delta. The study has revealed the extensive level of human and environmental exposure to hydrocarbon pollution in the Niger Delta and introduced new methods that will be valuable fo

Transnational advocacy networks in the international system : lessons from Ecuador.

The transnational advocacy campaign against Ecuador\u27s second oil-transporting pipeline, the Oleoducto de Crudo Pesado, had no impact on that state\u27s endorsement of the project and only a negligible effect on related social and environmental policies. This outcome is at odds with the theoretical formulation advanced by Keck and Sikkink which holds that certain transnational advocacy campaigns can act as agents of state-level policy changes. While Keck and Sikkink locate causal variables of campaign outcome on the levels of the campaign and the state, the Oleoducto de Crudo Pesado case signals the need to further incorporate international-level analysis and to investigate the implications of this third dimension for transnational advocacy campaign outcome. The case study presented herein suggests that theories of transnational advocacy sacrifice predictive power by ignoring the extent to which international economic and political structures can shape the preferences of states

Development of an integrated decision support system for supporting offshore oil spill response in harsh environments

Offshore oil spills can lead to significantly negative impacts on socio-economy and constitute a direct hazard to the marine environment and human health. The response to an oil spill usually consists of a series of dynamic, time-sensitive, multi-faceted and complex processes subject to various constraints and challenges. In the past decades, many models have been developed mainly focusing on individual processes including oil weathering simulation, impact assessment, and clean-up optimization. However, to date, research on integration of offshore oil spill vulnerability analysis, process simulation and operation optimization is still lacking. Such deficiency could be more influential in harsh environments. It becomes noticeably critical and urgent to develop new methodologies and improve technical capacities of offshore oil spill responses. Therefore, this proposed research aims at developing an integrated decision support system for supporting offshore oil spill responses especially in harsh environments (DSS-OSRH). Such a DSS consists of offshore oil spill vulnerability analysis, response technologies screening, and simulation-optimization coupling. The uncertainties and/or dynamics have been quantitatively reflected throughout the modeling processes. First, a Monte Carlo simulation based two-stage adaptive resonance theory mapping (MC-TSAM) approach has been developed. A real-world case study was applied for offshore oil spill vulnerability index (OSVI) classification in the south coast of Newfoundland to demonstrate this approach. Furthermore, a Monte Carlo simulation based integrated rule-based fuzzy adaptive resonance theory mapping (MC-IRFAM) approach has been developed for screening and ranking for spill response and clean-up technologies. The feasibility of the MC-IRFAM was tested with a case of screening and ranking response technologies in an offshore oil spill event. A novel Monte Carlo simulation based dynamic mixed integer nonlinear programming (MC-DMINP) approach has also been developed for the simulation-optimization coupling in offshore oil spill responses. To demonstrate this approach, a case study was conducted in device allocation and oil recovery in an offshore oil spill event. Finally, the DSS-OSRH has been developed based on the integration of MC-TSAM, MC-IRFAM, AND MC-DSINP. To demonstrate its feasibility, a case study was conducted in the decision support during offshore oil spill response in the south coast of Newfoundland. The developed approaches and DSS are the first of their kinds to date targeting offshore oil spill responses. The novelty can be reflected from the following aspects: 1) an innovative MC-TSAM approach for offshore OSVI classification under complexity and uncertainty; 2) a new MC-IRFAM approach for oil spill response technologies classification and ranking with uncertain information; 3) a novel MC-DMINP simulation-optimization coupling approach for offshore oil spill response operation and resource allocation under uncertainty; and 4) an innovational DSS-OSRH which consists of the MC-TSAM, MC-IRFAM, MC-DMINP, supporting decision making throughout the offshore oil spill response processes. These methods are particularly suitable for offshore oil spill responses in harsh environments such as the offshore areas of Newfoundland and Labrador (NL). The research will also promote the understanding of the processes of oil transport and fate and the impacts to the affected offshore and shoreline area. The methodologies will be capable of providing modeling tools for other related areas that require timely and effective decisions under complexity and uncertainty

The Dynamics of Nigeria’s Oil and Gas Industry’s Environmental Regulation: Revealing/Storying Neglected Voices and Excluded Lives of Environmental Encounters and Affects

The complex interaction of politics, power, economics and ‘subjectivisation’ of the human in natural resource exploration and production has demonstrated their impacts on the environment and ecosystem in anthropogenic and Anthropocenic dimensions. In Nigeria’s Niger Delta, these impacts have constantly materialised in the conflicts in the oil communities. This reality underscores the basis for this research’s narrative/analytical approach: the need to find a different way of narrating and dealing with the decades-long cataclysmic effects of oil and gas exploration on the people, environment, and ecosystem. The methodological approach adopted, autoethnography, will be justified through the view that within the gamut of qualitative methodology, autoethnography presents the most veritable avenue to reflexively create a forum for sharing with the world, the untold stories, and narratives of the people of the Niger Delta who exist in zones I refer to as zones of ‘exclusion’. From these zones, I engage with the voice of an imagined character, ‘O’, whose journey’s narratives as first order observer, rouse my own memory of a difference between system and environment. The narrative’s reality, viewed from systems theory, is a fluctuation between the immersion in, and distance from, the observed, observing, and self-observation, yet with the increasing realisation of the interconnectedness and interaction between man and his natural environment. This folds into an affect that is immanent on the human psyche, particularly in ecological terms. It also results in the search of transcendent justice that will achieve relational and social interaction mechanisms among all stakeholders to minimise and manage environmental incidents that may imply degradation and severe damage to the ecosystem, the socio-economic linkages to the environment, and human health and life

Collaboration-based management of petroleum pipeline rights of way in Nigeria

PhD ThesisThis thesis starts with a conceptual exploration of the challenges in the management of Petroleum Pipeline Rights of Way (ROW), within the context of public infrastructure management. Upon this basis, a holistic understanding has been developed of the concept. This understanding argues the need for Collaboration-Based Management of ROW for Petroleum Pipelines, in particular, involving the communities traversed by the pipelines as stakeholders, in order to enhance long term social, economic and environmental sustainability through their interaction with the other stakeholders: the government and multi-national oil companies. Building upon the theoretical arguments developed, this research has proposed a geographic information system framework for demarcating ROW that is capable of continuous updating in line with new knowledge. By applying this framework, the ROW in the Federal Capital Territory, Abuja, is demarcated; and further analysis is applied that shows widespread encroachment on the ROW by other land uses. A total of 588 structured interview questionnaires were completed, five focus group discussions held and 14 key informant interviews conducted across four case study areas. Analysis of the data revealed that the pipeline project has not improved the economic situation of the people in the communities it traverses. The empirical evidence from the case studies also suggests that vandalism thrives in the pipeline communities, because those geographically closest to the pipeline have no role in its management and the national and multi-national oil companies that have lawful authorisation over the pipelines and the associated ROW do not have the capacity to maintain real-time surveillance. Hence, there is a need for a new approach, based on a collaboration-based framework. This framework will engender the adoption of local knowledge and experience regarding the environment for the greater collective interest of the oil and gas industry, the citizenry and, by extension, the Nigerian national economy