A DECISION SUPPORT SYSTEM FOR DETERMINING THE SUITABLE FISH SPECIES TO FISH FARMS

Abstract

Fishery industry is one of the main sources of income and most important subsectors fornational economies. Nevertheless, natural fish sources have unfortunately diminished recentlyand not every fish species can be grown in every region due to some reasons. Moreover, manycountries have limited resources to meet the need for fish. Therefore, aquaculture comes intoprominence to eliminate these problems. Also, taking right decisions and selecting the rightfish breeds become crucial for fish farming. In this context, the aim of this study is to develop aDecision Support System (DSS) with intent to enable decision makers to determine mostsuitable fish species according to the features of their farms easily. The system was built basedon Classification and Regression Trees algorithm, one of the data mining techniques. Sixty-twobreeds of fish and thirteen factors affecting their growth was studied to create a database. Theresults show that the suggested DSS functions successfully in terms of not only determiningappropriate and profitable fish species but also using existing resources more efficiently. It isexpected that foreign trade volume will be increased with the raising productivity and; hence,countries will create new business branches which will have reflections in employment figuresin the long run.