A Decision Support System for Determining the Suitable Fish Species to Fish Farms

Fishery industry is one of the main sources of income and most important subsectors for national economies. Nevertheless, natural fish sources have unfortunately diminished recently and not every fish species can be grown in every region due to some reasons. Moreover, many countries have limited resources to meet the need for fish. Therefore, aquaculture comes into prominence to eliminate these problems. However, taking right decisions and selecting the right fish breeds become crucial for fish farming. In this context, the aim of this study is to develop a Decision Support System (DSS) with intent to enable decision makers to determine most suitable fish species according to the features of their farms easily. The system was built based on Classification and Regression Trees algorithm, one of the data mining techniques. Sixty-two breeds of fish and thirteen factors affecting their growth was studied to create a database. The results show that the suggested DSS functions successfully in terms of not only determining appropriate and profitable fish species but also using existing resources more efficiently. It is expected 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 figures in the long run.