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Alebachew Chiche


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.


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

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