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Reduced the Risk in Agriculture Management for Government Using Support Vector Regression with Dynamic Optimization Parameters

Chien-Pang Lee


A good agricultural policy can reduce the risk of agricultural management. In the past, the traditional statistical methods were always used for assisting agricultural management. However, the assumptions of traditional methods might not fit for real life data, that would affect the decision of agricultural management. For this reason, this paper uses big data analysis to propose a novel prediction model without any assumption to forecast agricultural output for reducing the risk. According to the result, the proposed model is better than the existing models in terms of prediction accuracy. Accordingly, the proposed model can be suggested for reducing the risk of agricultural management of government.


agricultural policy making, agricultural management, support vector regression model, sampling method, big data analysis


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