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

Yıl 2020, , 373 - 388, 31.12.2020

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

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

Öz

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.

Anahtar Kelimeler

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

ÇİFTLİKLERDE YETİŞTİRİLECEK UYGUN BALIK TÜRLERİNİN BELİRLENMESİNE YÖNELİK BİR KARAR DESTEK SİSTEMİ

Öz

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.

Anahtar Kelimeler

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

Kaynakça

• Aburrous, M., Hossain, M. A., Dahal, K., & Thabtah, F. (2010). Intelligent Phishing Detection System for e-banking Using Fuzzy Data Mining. Expert Systems with Applications, 37(12), 7913-7921.
• Albayrak, A. S., & Yılmaz, Ö. G. Ş. K. (2009). Veri Madenciliği: Karar Ağacı Algoritmaları ve İMKB Verileri Üzerine Bir Uygulama. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(1).
• Angeli, C., Howard, S. K., Ma, J., Yang, J., & Kirschner, P. A. (2017). Data Mining in Educational Technology Classroom Research: Can It Make a Contribution?. Computers & Education, 113, 226-242.
• Alsmadi, M. K. S., Omar, K. B., Noah, S. A., & Almarashdah, I. (2009). Fish Recognition Based on the Combination between Robust Feature Selection, Image Segmentation and Geometrical Parameter Techniques Using Artificial Neural Network and Decision Tree. Journal of Computer Science and Information Security, Vol.6, No. 2.
• Alsmadi, M. K., Omar, K. B., Noah, S. A., & Almarashdeh, I. (2010). Fish Recognition Based on Robust Features Extraction from Size and Shape Measurements Using Neural Network. Journal of Computer Science, 6(10), 1059-1065.
• Alsmadi, M. K., Omar, K. B., & Noah, S. A. (2011). Fish Classification Based on Robust Features Extraction from Color Signature Using Back-Propagation Classifier. Journal of Computer Science, 7(1), 52-58.
• Azadivar, F., Truong, T., & Jiao, Y. (2009). A Decision Support System for Fisheries Management Using Operations Research and Systems Science Approach. Expert Systems with Applications, 36, 2971–2978.
• Bahari, T. F., & Elayidom, M. S. (2015). An Efficient CRM-Data Mining Framework for the Prediction of Customer Behaviour. Procedia Computer Science, 46, 725-731.
• Bhardwaj, B. K., & Pal, S. (2011). Data Mining: A prediction for performance improvement using classification. International Journal of Computer Science and Information Security, 9(4), 136-140.
• Buelens, B., Pauly, T., Williams, R., & Sale, A. (2009). Kernel Methods for the Detection and Classification of Fish Schools in Single-Beam and Multibeam Acoustic Data. ICES Journal of Marine Science, 66(6), 1130-1135.
• Cabreira, A. G., Tripode, M., & Madirolas, A. (2009). Artificial Neural Networks for Fish-Species Identification. ICES Journal of Marine Science, 66(6), 1119-1129.
• Çalış, A., Kayapınar, S., & Çetinyokuş, T. (2014). Veri Madenciliğinde Karar Ağacı Algoritmaları İle Bilgisayar Ve İnternet Güvenliği Üzerine Bir Uygulama. Journal Of Industrial Engineering (Turkish Chamber Of Mechanical Engineers), 25.
• Daniel T. LAROSE, (2005). Discovering Knowledge in Data: An Introduction to Data Mining, John and Wiley Sons Incorporated, USA.
• Daş, B., & Türkoğlu, İ. (2014). DNA Dizilimlerinin Sınıflandırılmasında Karar Ağacı Algoritmalarının Karşılaştırılması. Elektrik–Elektronik–Bilgisayar ve Biyomedikal Mühendisliği Sempozyumu (ELECO 2014), 381-383.
• David, H., Heikki, M. & Padhraic, S. (2001). Principles of Data Mining, The MIT Press, Cambridge.
• Delen, D., Fuller, C., McCann, C., & Ray, D. (2009). Analysis of Healthcare Coverage: A Data Mining Approach. Expert Systems with Applications, 36(2), 995-1003.
• Dutta, I., Dutta, S., & Raahemi, B. (2017). Detecting Financial Restatements Using Data Mining Techniques. Expert Systems with Applications, 90, 374-393.
• Türkiye Cumhuriyeti Ekonomi Bakanlığı, Balıkçılık Sektör Raporu ve Su ürünleri Fuarı. (2015).
• Emel, G. G., & Taşkın, Ç. (2005). Veri Madenciliğinde Karar Ağaçları ve Bir Satış Analizi Uygulaması. Eskişehir Osmangazi Üniversitesi Sosyal Bilimler Dergisi, 6(2).
• FAO 2016. Assessing Water Availability and Economic, Social and Nutritional Contributions from Inland Capture Fisheries and Aquaculture. FAO Fisheries and Aquaculture Technical Paper. FAO, Rome, 101 pp.
• Gaertner, D., & Dreyfus-Leon, M. (2004). Analysis of Non-Linear Relationships Between Catch Per Unit Effort And Abundance in A Tuna Purse-Seine Fishery Simulated with Artificial Neural Networks. ICES J. Mar. Sci., 61 (5), 812– 820.
• Gerami, M., H., & Rabbaniha, M. (Accepted Manuscript). Forecasting the Anchovy Kilka Fishery in the Caspian Sea Using a Time Series Approach. Turkish Journal of Fisheries and Aquatic Sciences.
• Gutiérrez-Estrada, J. C., Yáñez, E., Pulido-Calvo, I., Silva, C., Plaza, F., & Bórquez, C. (2009). Pacific sardine (Sardinops sagax, Jenyns 1842) landings prediction. A neural network ecosystemic approach. Fisheries Research, 100(2), 116-125.
• Halide, H., Stigebrandt, A., Rehbein, M., & McKinnon, A.D. (2009). Developing A Decision Support System for Sustainable Cage Aquaculture. Environmental Modelling & Software, 24, 694–702.
• Han, J. & Micheline, K. (2006). Data Mining: Concepts and Techniques, Second Edition, Morgan Kaufmann Publishers.
• Haralabous, J. & Georgakarakos, S. (1996). Artificial Neural Networks as a Tool for Species Identification of Fish Schools. ICES Journal of Marine Science, 53, 173–180.
• Hong-Chun, Y., Ying, L., & Ying, C. (2009, August). Fishery Knowledge Discovery Based on SVM and Fuzzy Rule Extraction. In Computer Science and Information Technology, 2009. ICCSIT 2009, 2nd IEEE International Conference on (pp. 167-171). IEEE, Beijing.
• https://www.tarim.gov.tr/sgb/Belgeler/SagMenuVeriler/BSGM.pdf
• http://www.isub.org.tr/assets/rapor_suurunlerivekulturbalikciligiileilgilirevize_3eylul2014.pdf
• Hu, J., Li, D., Duan, Q., Han, Y., Chen, G., & Si, X. (2012). Fish Species Classification by Color, Texture and Multi-Class Support Vector Machine Using Computer Vision. Computers And Electronics In Agriculture, 88, 133-140.
• Joo, R., Bertrand, S., Chaigneau, A., & Niquen, M. (2011). Optimization of an Artificial Neural Network for Identifying Fishing Set Positions from VMS Data: An Example from the Peruvian Anchovy Purse Seine Fishery. Ecological Modelling, 222(4), 1048-1059.
• Joy, M.K., & Death, R.G., (2004). Predictive Modelling and Spatial Mapping of Freshwater Fish and Decapod Assemblages Using GIS and Neural Networks. Freshwater Biology. 49(8), 1036–1052.
• Kaygisiz, F.& Eken, M. (2018). A Research on Determination of Fish Marketing Margins in Istanbul Province of Turkey. Turkish Journal of Fisheries and Aquatic Sciences. 18, 801-807.
• Kim, Y.-H. (2003). New Modelling of Complex Fish Migration By Application of Chaos Theory And Neural Network. Journal of Fish Biology. 63 (1), 234.
• Larsen, R., Olafsdottir, H., & Ersbøll, B. K. (2009, June). Shape and Texture Based Classification of Fish Species. In Scandinavian Conference on Image Analysis (pp. 745-749). Springer, Berlin, Heidelberg.
• Marzuki, M. I., Garello, R., Fablet, R., Kerbaol, V., & Gaspar, P. (2015, May). Fishing Gear Recognition from VMS Data to Identify Illegal Fishing Activities in Indonesia. In OCEANS 2015-Genova (pp. 1-5). IEEE.
• Marzuki, M. I., Gaspar, P., Garello, R., Kerbaol, V., & Fablet, R. (2017). Fishing Gear Identification From Vessel-Monitoring-System-Based Fishing Vessel Trajectories. IEEE Journal of Oceanic Engineering, 99, 1-11.
• Mastrorillo, S., Lek, S., Dauba, F., & Belaud, A. (1997). The Use of Artificial Neural Networks to Predict the Presence of Small-Bodied Dish in A River. Freshwater Biol., 38, 237–246.
• Mendoza, M., García, T., & Baro, J. (2010). Using Classification Trees to Study the Effects of Fisheries Management Plans on the Yield of Merluccius Merluccius (Linnaeus, 1758) in the Alboran Sea (Western Mediterranean). Fisheries Research, 102(1-2), 191-198.
• Network Ecosystemic Approach. Fisheries Research, 100, 116–125.
• Ngai, E. W., Xiu, L., & Chau, D. C. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert Systems with Applications, 36(2), 2592-2602.
• Ogunlana, S. O., Olabode, O., Oluwadare, S. A. A., & Iwasokun, G. B. (2015). Fish Classification Using Support Vector Machine. African Journal of Computing & ICT, 8(2), 75-82.
• Park, Y., Grenouillet, G., Esperance, B., & Lek, S. (2006). Stream Fish Assemblages and Basin Land Cover in A River Network. Sci. Total Environ., 365 (1–3), 140–153.
• Reyjol, Y., Fischer, P., Lek, S., Rosch, R., & Eckmann, R. (2005). Studying The Spatiotemporal Variation of The Littoral Fish Community in A Large Prealpine Lake, Using Self-Organizing Mapping. Can. J. Fish. Aquat. Sci., 62 (10), 2294–2302.
• Song, Y. Y., & Ying, L. U. (2015). Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry, 27(2), 130.
• Storbeck, F., & Daan, B. (2001). Fish Species Recognition Using Computer Cision and a Neural Network. Fisheries Research, 51(1), 11-15.
• Su, Y. Y., & Chang, S. J. (2008, April). Spatial Cluster Detection for the Fishing Vessel Monitoring Systems. In OCEANS 2008-MTS/IEEE Kobe Techno-Ocean (pp. 1-4). IEEE.
• Sucipto, Kusrini, Taufiq, E., L., (2016). Classification method of multi-class on C4.5 algorithm for fish diseases. 2016 2nd International Conference on Science in Information Technology (ICSITech) (pp. 5-9). IEEE, Balikpapan, Indonesia.
• Suryanarayana, I., Braibanti, A., Rao, R., S., Ramam, V., A., Sudarsan, D., & Rao, G., N. (2008). Neural Networks in Fisheries Research. Fisheries Research, 92, 115–139. Tan, P.-N., Steinbach, M. & Kumar, V. (2006). Introduction to Data Mining, First Edition, Pearson, England.
• Taufiq, E. L. (2016, October). Classification Method of Multi-class on C4. 5 Algorithm for Fish Diseases. In Science in Information Technology (ICSITech), 2016 2nd International Conference on (pp. 5-9). IEEE.
• Tsai, W. P., Huang, S. P., Cheng, S. T., Shao, K. T., & Chang, F. J. (2017). A Data-Mining Framework for Exploring the Multi-Relation Between Fish Species and Water Quality Through Self-Organizing Map. Science of the Total Environment, 579, 474-483.
• Uçar, U. U., Balo, F., Eraslan, G., & Çetin, B. (2017). İdeal Hayvan Yetiştiriciliği İçin Veri Madenciliğine Dayalı Bir KDS Çalışması. Gaziosmanpaşa Bilimsel Araştırma Dergisi, 6(Özel Sayı (ISMSIT2017)), 133-141.
• Vazan, P., Janikova, D., Tanuska, P., Kebisek, M., & Cervenanska, Z. (2017). Using Data Mining Methods for Manufacturing Process Control. IFAC-PapersOnLine, 50(1), 6178-6183.
• Wang, K. Y., Ting, I. H., & Wu, H. J. (2013). Discovering Interest Groups for Marketing in Virtual Communities: An Integrated Approach. Journal of Business Research, 66(9), 1360-1366.
• Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H.,… & Zhou, Z. H. (2008). Top 10 Algorithms in Data Mining. Knowledge and Information Systems, 14(1), 1-37.
• Xiaoshuan, Z., Tao, H., Revell, B., & Zetian, F. (2005). A Forecasting Support System for Aquatic Products Price in China. Expert Systems with Applications, 28, 119–126.
• Yang, Y. C. E., Cai, X., & Herricks, E. E. (2008). Identification of Hydrologic Indicators Related to Fish Diversity and Abundance: A Data Mining Approach for Fish Community Analysis. Water Resources Research, 44(4).
• Yılmaz, Y., Dağdeviren, M., Çetinyokuş, T. (2008). İşgören Performansının Değerlendirilmesi İçin Bir Karar Destek Sistemi ve Uygulaması. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 23(1).
• Yuan, H., Li, Y., & Chen, Y. (2009, August). A Novel Knowledge Discovery Model for Fishery Forecasting. In Fuzzy Systems and Knowledge Discovery, 2009. FSKD'09. Sixth International Conference on (Vol. 3, pp. 29-33). IEEE, Tianjin, China.
• Zhou, S. (2003). Application of Artificial Neural Networks for Forecasting Salmon Escapement. North American Journal of Fisheries Management, 23 (1), 48–59.
• Zhong, N., & Zhou, L. (Eds.). (2003). Methodologies for Knowledge Discovery and Data Mining: Third Pacific-Asia Conference, PAKDD'99, April 26-28, 1999, Proceedings. Springer, Beijing, China,