Experimental study of neuro-fuzzy-genetic framework for oil spillage risk management

Oluwole Charles Akinyokun, Udoinyang Godwin Inyang


This paper reports the findings from the experimental study of an intelligent system driven by Neural Network (NN), Fuzzy Logic (FL) and Genetic Algorithm (GA) for knowledge discovery and oil spillage risk management. Application software was developed in an environment characterized by 11Ants Analytics, Matrix Laboratory (MatLab), Microsoft Excel, SPSS and GraphPadInstat as frontend engines; Microsoft Access Database Management System as backend engine and Microsoft Windows as platform. 11Ants Analytics served as a tool for oil spillage indicators rank analysis and predictive model building. Matlab served as a tool for the extraction of patterns from 11Ants Analytics Model of oil spillage. Microsoft Excel serves as an interface between 11Ants Analytics and Matlab. Microsoft Excel, SPSS and GraphPadInstat serve as tools for the generation of relevant statistics. Indicators of oil spillage risks serve as input to the NN. GA is used to provide optimal set of parameters for NN training while FL used for modelling imprecise knowledge and provision of membership functions for the GA and NN. Data on Oil Spill incidences associated with oil exploration activities in the Niger Delta Region of Nigeria were collected from National Oil Spill Detection and Response Agency (NOSDRA) and used to assess and evaluate the practical function of the intelligent system. Adaptive Neuro-Fuzzy Inference System (ANFIS) driven by Mamdani’s inference mechanism was used to predict and estimate oil spillage risks. The findings from the experimental study are presented.

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DOI: https://doi.org/10.5430/air.v2n4p13


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Artificial Intelligence Research

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