Application of Rough Set Theory in Wave Height Prediction

فاقد چكيده فارسي

Wave height prediction in offshore operations can be extremely complex due to availability of vague and uncertain information. Integrated interdisciplinary modeling techniques, providing reliable, efficient, and accurate representation of the complex phenomenon of wave height prediction, have gained attention in recent years. With the ability to express knowledge in a rule-based form, the Rough Set Theory (RST) has been successfully employed in many fields. However the application of RST has not been widely investigated in wave height prediction analysis. In this paper, the basic concept of the rough set theory is introduced and implemented to discover some rules for wave hieght prediction in the Lake Superior. The rules are derived by expressing wave height as functions of wind data gathered by National Data Buoy Center (NDBC). This approach represents a new mathematical tool quit different to other soft computing techniques in the decision rules induction. Comparing results of Rough Set Theory with other soft computing technique namely Artificial Neural Networks (ANNs) indicates that the RST could analyze wave height efficiently and accurately, and provides a promising and helpful scheme for wave height predictions.