Millions of tons of oil are produced in the world every year and over half of it is
transported to the users by means of marine routes. Based on statistics, a best estimate of
oil spill is more than 3 million tons per year. Oil spills cause disastrous impacts on the
environment, ecology and socio-economic activities. The right decision has to be made
in the event of an oil spill to facilitate prompt action, considering the priorities of
protection, to prevent environmental damages. Interest in having modern, technological
management system in semi-structured fields such as disastrous incidents is increasing
rapidly. Response decision support is a mechanism utilizing a knowledge-based plan to choose the most suitable method of response by analyzing the various sensitivity factors,
parameters affecting oil spill impacts, environmental concerns in oil spill response, and
consequence monitoring and clean-up operations in the shortest time. Environmental
sensitivity index (ESI), a traditional scale, is mostly a static scale for evaluation of
coastal situation. It requires calibration along with oil nature and impact in each spill
case to be able of priority displaying in action. This study aimed to develop a semiautomatic
knowledge-based decision support mechanism to retrieve experts’ knowledge
for prioritization in responding to oil spill events. A tool was needed to classify
information about knowledge and expertise in this field and follow the rational logic of
master minds and could be transferable. The knowledge and expertise from
knowledgeable participants were obtained through questionnaires and direct interviews
as well as information from literatures. Three objectives were covered by the study
including ranking of sensitivity-oil-response criteria, development of coastal priority
ranking (CPR) scale, and establishment of a validated computer-based mechanism for
oil spill response (OSR-DSM). Analyses of questions were conducted using Delphi
method, Likert scaling, and repertory grid analysis. The evaluation of knowledge level
provided the normalized weights (from 0.09 to 1.0) for respondents’ knowledge and
these weights were applied to criteria ranking. Considering two objects of environment
and oil, priority ranking matrix was established and CPR scale was calculated based on
the fact that various “low/ medium/ high” impacting scenarios of oil can affect the
corresponding “low/ medium/ high” sensitive resources. One program was designed to
visualize DSM with computation of ESI, coastal sensitivity, oil impact, and CPR values
as well as reporting on response alternatives. The advantage of CPR scale method was
its ability for a more dynamic quantitative evaluation of priorities in application time rather than only explaining sensitivity indices of area. The scale for CPR was evaluated
ranging from 35 to 469 and the values were qualitatively categorized from low priority
to medium, high, very high and extremely high priorities. Three major categories were
renowned for responses alternatives - on-sea response or preventive activities, shoreline
protective activities, and on-coast response or cleanup activities. Results were verified to
present the inclusiveness, accuracy, and system algorithm. The verification activity
involved exploring the knowledge base, coding of reasoning processes / inference
engine, technical performance, ability for development, and interface. A total of 80
percent of users in the verification phase believed that development of such mechanism
was a right approach for supporting the right decision in oil spill responses, either by
increasing the speed and accuracy in evaluation or reducing the cost. Verification
research could attain rates of over 50 percent in all five categories. General rates given
to the mechanism by two groups of users were 82 and 85 percent with a + 3.66 percent
of uncertainty that was issued a high verification value. This study has resulted in two
main products: - coastal priority ranking scale (CPR) and oil spill response decision
supporting mechanism (OSR-DSM). It is intended to facilitate the oil spill response
process while at the same time improves the decision-making quality by applying the
effective knowledge and expertise in oil spill response procedures. Definition of
knowledge criteria leading to classification of knowledgeable participants, as well as
numerical verification frame for qualitative knowledge-base mechanism were two
significant outputs of this study
