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

Year 2020, Volume: 31 Issue: 3, 373 - 388, 31.12.2020

İnci Elif Hadık , Ukbe Usame Uçar , Mehmet Atak , Selçuk Kürşat İşleyen

https://doi.org/10.46465/endustrimuhendisligi.788918

Abstract

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.

Keywords

Aquaculture, Fish Farming, Data Mining, Classification and Regression Tree

ÇİFTLİKLERDE YETİŞTİRİLECEK UYGUN BALIK TÜRLERİNİN BELİRLENMESİNE YÖNELİK BİR KARAR DESTEK SİSTEMİ

Abstract

Balıkçılık endüstrisi ulusal ekonominin en önemli alt sektörlerinden ve temel gelir kaynaklarından biridir. Bununla birlikte, son zamanlarda doğal balık kaynakları ne yazık ki yok olmakta ve bazı nedenlerden dolayı her bölgede her balık türü yetiştirilememektedir. Ayrıca, birçok ülke balık ihtiyacını karşılamak için sınırlı kaynaklara sahip olmaktadır. Bu nedenlerden dolayı, su ürünleri yetiştiriciliği bu problemlerin çözülmesinde ön plana çıkmaktadır. Bununla birlikte, doğru balık türlerinin seçilmesi ve doğru kararların alınması balık yetiştiriciliği için büyük bir öneme sahip olmaktadır. Bu bağlamda, bu çalışmanın amacı karar vericilerin, ilgili çiftliğin özelliklerine göre en uygun balık türünün kolay bir şekilde belirlenmesini sağlayan bir Karar Destek Sistemi(KDS) geliştirmektir. İlgili sistem veri madenciliği tekniklerinden Sınıflandırma ve Regresyon Ağaçları Algoritması’na dayalı olarak inşa edilmiştir. 62 balık türü ve bu balıkların büyümesine etki eden 13 faktörden oluşan bir veritabanı oluşturulmuştur. Sonuçlar, önerilen KDS yapısının yalnızca uygun ve karlı balık türlerini belirlenmesinde değil aynı zamanda mevcut kaynakların daha verimli bir şekilde kullanması açısından da başarılı bir şekilde çalıştığını göstermiştir. Artan verimlilik ile birlikte dış ticaret hacminin artacağı ve bundan dolayı ülkelerin uzun vadede istihdam rakamlarına yansıyacak yeni iş kolları oluşturacağı beklenmektedir.

Keywords

Su Ürünleri Yetiştiriciliği, Balık Yetiştiriciliği, Veri Madenciliği, Sınıflandırma ve Regresyon Ağacı

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