A hybrid knowledge discovery system for oil spillage risks pattern classification

Abstract

The complexity and the dynamism of oil spillages make it difficult for planners and responders to produce robust plans towards their management. There is need for an understanding of the nature, sources, impact and responses required to prevent or control their occurrence. This paper develops an intelligent hybrid system driven by Sugeno-Type Adaptive Neuro Fuzzy Inference System (ANFIS) for the identification, extraction and classification of oil spillage risk patterns. Dataset consisting of 1008 records was used for training, validation and testing of the system. Result of sensitivity analysis shows that Cause, Location and Type of spilled oil have cumulative significance of 85.1%. Optimal weights of Neural Network (NN) were determined via Genetic Algorithm with hybrid encoding scheme. The Mean Squared Error (MSE) of NN training is 0.2405. NN training, validation and testing results yielded R > 0.839 in all cases indicating a strong linear relationship between each output and target data. Rule pruning was performed with support (15%) and confidence (10%) minimum thresholds and antecedent-size of 3. The performance of the ANFIS was evaluated with eight different types of membership functions (MFs) and two learning algorithms. The model with triangular MF gave the best performance among all other given models while hybrid-learning algorithm performed better than back propagation algorithm. The ANFIS model reported in the paper adopted triangular MF and hybrid learning algorithm for the predication and classification of oil spillage risk patterns. Average training and testing MSE of the model is 0.414315 and 0.221402 respectively. The knowledge mining results show that ANFIS based systems provide satisfactory results in the prediction and classification of oil spillage risk patterns.

Discrete event based hybrid framework for petroleum products pipeline activities classification

“…The NN tools produced an intelligent system with learning capabilities for detecting the anomalies on PPP but were not capable of dealing with imprecise PPP data. In Akinyokun and Inyang, [12,14] oil spillage risk managment framework is proposed using neurofuzzy-genetic platform. The neuro-fuzzy-genetic system demonstrated optimized training and imprecise data handling capabilities in the task of recognising patterns in complex oil spillage dataset but lacks facility for timing and automatic management of simultaneous oil spillage-induced parameters from multi pipeline locations.…”

“…[7] Different approaches have been used to evolve systems that monitor, detect, classify or respond to emergencies resulting from oil spillages and leakages. [8][9][10][11][12][13][14][15] These systems are limited by lack of a systematic way of tracking the time of activities, high probability of false detection and inefficient localization of detected activities due to non inclusion of intelligent tools for explicit timing of operations, pattern recognition and data imprecision handling. Discrete event system specification (DEVS) offers a plausible solution for the specification of timing and localization need of this problem, while adaptive neuro-fuzzy inference system (ANFIS) proffers solution for pattern recognition and data imprecision.…”

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

“…The details of the data have been presented in [19] . The knowledge mining which is performed with 11Ants Analytics advanced in the following stages as in [26] : a) Selection and dataset pre-processing which involves the identification of the target variable in the dataset and selection of the input variables required for the knowledge discovery process.…”

“…The Time variable was transformed into two categories, namely; AM (12.00am -12.00noon) and PM (12.01pm -11.59pm). The values of the variables are large, hence cannot be recorded in this paper but have been presented [19] . .…”

“…The extracted rules have been presented in ref. 19. The graphical analysis of the rules gives the analysis of the number of rules, the support of rules and the Confidence of rules.…”