Title :
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
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;
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
DOI :
10.1109/IFIS.1993.324180
