Faculty of Technology

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Browsing Faculty of Technology by Author "AKHAINE, Victor Emuata"

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    Developement of a Scenario Based Emergency Response Model for Flooding in Nigeria
    (The Department of Computer Science and Engeneering, Faculty of Technology, Obafemi Awolowo University., 2022) AKHAINE, Victor Emuata
    This Study characterized factors and collected data responsible for flooding, formulated a scenario-based model using the characterized factors and data collected, and simulated and evaluated a model. This was with a view to aiding the planning phase of response agencies by reducing uncertainties through synthetic scenarios. Data based on factors responsible for flooding, were elicited by interviewing experts at the Nigerian Hydrological Service Agency (NHSA), combining systems theory with Delphi method and Cross impact analysis the scenario based model was developed and simulated in python programing language. The causal factors elicited in the course of the interview were categorized into Initial Conditions, Dynamic Events and Outcome Events. In total there were 24 events. The response from the experts were then used to create a probability scale to rank relationships between the events which served as input to the model to generate 6 scenarios, these scenarios were then displayed in graphs. The model was evaluated for accuracy, precision, internal consistency and root mean squared error and verified by comparing the output against recurring events from historical analysis. The results showed that the model had a precision and accuracy of 100% which represents the predictive capability and RMSE of 5.44. the dataset gotten from the experts had an internal consistency of 0.53, 0.7381, and 0.82 based on 3 set of questions in the Delphi questionnaire. Out of the 6 scenarios, flooding had severe impact in scenarios 0, 1,3, and 5 which showed high probability of human casualties, huge economic losses and social unrest. Scenario 4 however showed a reduction of the probability of human casualties and social unrest with the exception of economic losses. In order to further verify the scenarios generated by the model, all the recurring events in the study area were equated to a 100% and then compared with the average accuracy of the scenarios generated by the developed model which was 57.50%. xi The study concluded that the model results formed the basis to develop a dashboard to identify future events that are pivotal in disaster response, and because of this, proactive measures could be taken by the appropriate agencies to respond towards mitigating flooding