Application of knowledge-based expert system model for fishing ground prediction in the tropical area

A Geographical information system (GIS) may be viewed as a database system in which most of the data is spatially indexed, and upon which a set of procedures operate in order to answer queries about spatial entities represented in the database. Geo-study deals with answering “What”, “Where”, and “Why” questions. Despite the fact that GIS is a powerful tool for dealing with the first two questions, GIS is inferior for answering the “Why” question in geo-study (Zhang and Giardino, 1992). One of the possible ways to overcome the inferiority of GIS in answering the “Why” question of Geo-Studies is by integrating an expert system in a GIS to form a Knowledge-Based Expert System GIS Model. In this study, we present the result of the application Knowledge-Based Expert System GIS Model on the prediction of the fishing ground for pelagic fish in the coastal area of Tomini Bay (Central Sulawesi) and South Sulawesi. As input data, we used and applied a series of satellite data of sea surface temperature (SST), sea surface chlorophyll-a (SSC) and turbidity derived from Aqua MODIS in the period of 2003-2005 to understand the temporal and seasonal variability of the marine environment of the study area, and identified the oceanographic phenomena, i.e. upwelling, front or eddy. To generate a spatial configuration of a fishing ground prediction map, we developed and integrated the result of the Knowledge-Based Expert System into the GIS model by using the ERDAS Macro Language (EML) of ERDAS Imagine 9.0 software. To verify this result, a series of the in-situ fishing ground spot data of the study area were collected for similar periods and locations, and they were then analyzed by using a simple statistical method. The result shows that fishing ground prediction derived from the Knowledge-Based Expert System GIS Model has a high accuracy level with a range of 80-90 % against the in-situ data. This result has demonstrated that the Knowledge-Based Expert System GIS Model can be applied to predict, localize and determine fishing ground spot areas in which their accuracy level will be determined by the completeness of spatial knowledge of the domain expertise and the sophistication level of the programming utilities being used.