A hybrid architecture for predicting oil slick movement

Author

Wang, Haojin ; Wolter, Jan ; Tsao, Jungfu

Author_Institution

Dept. of Comput. Sci., Texas A&M Univ., College Station, TX, USA

fYear

1993

fDate

1-3 Dec 1993

Firstpage

236

Lastpage

242

Abstract

In this paper, we present a hybrid architecture for an intelligent system that can be used to project oil slick movement. The system under construction has the ability to learn from historical weather data and then to incorporate the learned knowledge into its projection of the future movement of oil slick. It employs probabilistic reasoning to deal with uncertainty in the observed data and weather forecast, neural networks to acquire knowledge from historical data and fuzzy logic to deal with imprecision embedded in the available information. This innovative approach to this highly complicated, but very important and practical issue exemplifies the application of advanced AI techniques to the practical problems

Keywords

environmental science computing; knowledge based systems; neural nets; probabilistic logic; water pollution; advanced AI techniques; historical weather data; hybrid architecture; intelligent system; neural networks; oil slick movement prediction; probabilistic reasoning; Artificial intelligence; Computer architecture; Computer science; Economic forecasting; Environmental economics; Fuzzy logic; Hybrid intelligent systems; Neural networks; Petroleum; Weather forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Fuzzy Control and Intelligent Systems, 1993., IFIS '93., Third International Conference on

Conference_Location

Houston, TX

Print_ISBN

0-7803-1485-9

Type

conf

DOI

10.1109/IFIS.1993.324180

Filename

324180