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HYBRID DECISION SUPPORT SYSTEM FRAMEWORK FOR CROP YIELD PREDICTION AND RECOMMENDATION

Alebachew Chiche

Abstract


In this paper, a hybrid decision support system is presented which uses both quantitative and qualitative data to provide effective and efficient decision making for crop yield prediction and suggestion. Our framework integrates KD-DSS and DD-DSS for solving complex problems by complementing the existing gap of individual decision support system in agriculture domain. For analyzing collected quantitative data of agriculture research center, our framework uses artificial neural network as a data mining technique. So, we use ANN for uncovering hidden knowledge in stored dataset. And this knowledge is further integrated with the knowledge base developed by acquiring qualitative data from expertise and represented using an IF-THEN production rule. The integration of knowledge collected from both qualitative and quantitative source of data provides a potential advantage for solving complex problems for decision makers. Finally, we will have the opportunity to enhance the framework coupling the features which can provide a group knowledge sharing among decision makers. So, this feature can present the opportunities to fill the disparity of decisions made by different decision makers.

Keywords


data mining; knowledge base; hybrid DSS; knowledge-driven DSS; data-driven DSS; learning; crop yield prediction.

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References


J. Stienen, W. Bruinsma, F. Neuman, How ICT can Make a Difference in Agricultural Livelihoods Information & Communication Technologies, The Commonwealth Ministers Reference Book, 2007, pp. 2-4.

L. Armstrong, D. Diepeveen, and R. Maddern, “The application of data mining techniques to characterize agricultural soil profiles,” Proceedings of the sixth Australasian Conference on Data mining and analytics AusDM’07, Darlinghurst, Australia, 2007, pp. 85-100.

R. Jain, Decision Support Systems: An Overview, Decision Support System in Agriculture using Quantitative Analysis, Udaipur, Agrotech Publishing Academy, 2016, pp. 42-50.

P. Zarate, S. Liu, “A new trend for knowledge-based decision support systems design,” Int. J. Information and Decision Sciences, vol. 8, no. 3, pp. 305–324, 2016.

B. Manos, Th. Bournaris, J. Papathanasiou, Ch. Moulogianni and K.S. Voudouris, “A DSS for agricultural land use, water management and environmental protection,” Proceedings of the 3rd IASME/WSEAS Int. Conf. on Energy, Environment, Ecosystems and Sustainable Development, Agios Nikolaos, Greece, July 24-26, 2007, pp. 340-345.

A. Szeghegyi, “Investigation of Decision-Making Process by the Use of Knowledge-based System,” Proceedings of the 5th International Conference on Management, Enterprise and Benchmarking, Budapest, Hungary, June 1-2, 2007, pp. 209-222.

S.P. Anan, “Importance of knowledge base in decision making processes,” International Journal of Innovations & Advancement in Computer Science, vol. 6, no. 5, pp. 217-221, 2017.

A. Agnar, “A case-based answer to some problems of knowledge-based systems,” Proceedings of the Scandinavian Conference on Artificial Intelligence. IOS Press, 1993, pp. 168-182.

G.M. Maraka, Decision Support Systems in the Twenty-First Century, Upper Saddle River, London: Prentice Hall, 2003.

S. Haag, Management Information Systems for the Information Age, Arlington: McGraw-Hill, 2006.

C. Kyungyong, B. Raouf, H. Salim, “Knowledge based decision support system,” Inf. Technol. Manag., vol. 17, pp. 1-3, 2016.

D.J. Power, A Brief History of Decision Support Systems, 2007, [Online]. Available: http://DSSResources.COM/history/dsshistory.html

B. Nahim, J.A. Aguilar, A. Perez Pulido, N. Fernandez, “Composition intelligent frameworks,” Proceedings of the Conference on Artificial Intelligence and Agents, 2017, pp. 1-9.

F. Witlox, “Expert systems in land-use planning: An overview,” Expert Systems with Applications, vol. 29, no. 2, pp. 437-445, 2005.

D.J. Power, “Understanding data-driven decision support systems,” Information Systems Management, vol. 25, pp. 149–154, 2008.

D. Laney, 3D Data Management: Controlling Data Volume, Velocity, and Variety, 2001, [Online]. Available: http://blogs.gartner.com/

doug-laney/files/2012/01/ad949-3D-Data-Mana

gement-Controlling-Data-Volume-Velocity-and-Variety.pdf.

C. Brandas, C. Panzaru, F.G. Filip, “Data driven decision support systems: an application case in labour market analysis,” Romanian Journal of Information Science and Technology, vol. 19, no. 1-2, p. 65–77, 2016.

E. Brynjolfsson, L.M. Hitt and H.H. Kim, “Strength in numbers: how does data-driven decision-making affect firm performance?” in Thirty Second International Conference on Information Systems (ICIS), Shanghai, 2011, pp. 1-18.

J. Han, M. Kamber, J. Pei, Data Mining: Concepts and Techniques, San Francisco, CA, itd: Morgan Kaufmann, 2012.

M.D. Odom, R. Sharda, “A neural network model for bankruptcy prediction,” Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN’1990, vol. 2, pp. 163-168.

A. Ultsch, D. Korus, “Integration of neural networks with knowledge-based systems,” Proceedings of the IEEE Int. Conf. Neural Networks, Australia, 1995, pp. 1-6.

R.V. Ramani and K.V.K. Prasad, “Applications of knowledge based systems in mining engineering,” Proceedings of the Twentieth International Symposium on the Application of Computers APCOM’87, Johannesburg, 1987, pp. 17-180.


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