The complexity and the dynamism of oil spillages make it difficult for planners and responders to produce robust plans towards\ntheir management. There is need for an understanding of the nature, sources, impact and responses required to prevent or control\ntheir occurrence. This paper develops an intelligent hybrid system driven by Sugeno-Type Adaptive Neuro Fuzzy Inference\nSystem (ANFIS) for the identification, extraction and classification of oil spillage risk patterns. Dataset consisting of 1008\nrecords was used for training, validation and testing of the system. Result of sensitivity analysis shows that Cause, Location\nand Type of spilled oil have cumulative significance of 85.1%. Optimal weights of Neural Network (NN) were determined via\nGenetic Algorithm with hybrid encoding scheme. The Mean Squared Error (MSE) of NN training is 0.2405. NN training,\nvalidation and testing results yielded R > 0.839 in all cases indicating a strong linear relationship between each output and\ntarget data. Rule pruning was performed with support (15%) and confidence (10%) minimum thresholds and antecedent-size of\n3. The performance of the ANFIS was evaluated with eight different types of membership functions (MFs) and two learning\nalgorithms. The model with triangular MF gave the best performance among all other given models while hybrid-learning\nalgorithm performed better than back propagation algorithm. The ANFIS model reported in the paper adopted triangular MF\nand hybrid learning algorithm for the predication and classification of oil spillage risk patterns. Average training and testing\nMSE of the model is 0.414315 and 0.221402 respectively. The knowledge mining results show that ANFIS based systems\nprovide satisfactory results in the prediction and classification of oil spillage risk patterns.
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