Enhancing Artificial Neural Network: DSS Framework Pertaining to Oil Spill Response Management

Abstract

Marine and coastal environments are vulnerable to Oil spills all over the world. Spill disaster event relatively determine in terms of impacts, damages and response challenges. It is necessary to devise to be following up a technique for guiding and fast cleanup process of oil spills. Rapid response is effectively to minimize their environmental impact. So, the effective method has to choose to a response process for the pollution of coastal environment. This paper bring to light about Decision support system (DSS) framework using case based research methodology for Coastal / Marine Oil Spill Management facilitate necessary action for environmental damage. This paper focuses the identification of best solution for the oil pollution problem in Galveston Bay area of Gulf of Mexico study and allow for most effective cleanup possible by attempting artificial neural network (ANN) soft computing technique along with pattern matching algorithm can be used to improve the effectiveness of decision making via contingency planning and solve problems by providing necessary information and analyzing prominent historical oil spill cases, generating, evaluating and suggesting decision alternatives. Thus ANN provides the most proficient selection of appropriate response methods.