Abstract
An Expert system for Shipboard Obscuration Prediction (AESOP), an artificial intelligence approach to forecasting maritime visibility obscurations, has been designed, developed, and tested. The problem-solving model for AESOP, running within an IBM-PC environment, is rule-based, uses backward chaining, and has meta-rules; a user, in a consultation session, answers questions about certain atmospheric parameters. The current version, AESOP 2.0, has 232 rules and has been designed in terms of nowcasts (0–1 h) and forecasts (1–6 h). An extensive explanation feature allows the user to understand the reasoning process behind a particular forecast. AESOP has been evaluated against 83 test cases, in which clear, hazy, or foggy conditions are predicted. The overall performance of AESOP is 75% correct. This value indicates considerable forecast skill when compared to 47% for persistence and 41% for random chance. When the distinction between clear and haze is ignored, the expert system correctly forecasts 84% of the “Fog/No fog” situations.
