Reduced the Risk in Agriculture Management for Government Using Support Vector Regression with Dynamic Optimization Parameters
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.
Ahlburg, D. A. (1982) How Accurate are the U.S. Bureau of the Census Projections of Total Live Births?, Journal of Forecasting,1, pp. 365-374.
Ao, Z., Ou, X-Q. & Zhu, S. (2011) A New Optimization Approach for Grey Model GM(1,1), In: Lin, S. & Huang X (eds) Advanced Research on Computer Education, Simulation and Modeling, vol 175. Communications in Computer and Information Science (Berlin, Heidelberg: Springer), pp 117-122.
Boser, B. E., Guyon, I. M. & Vapnik, V. N. (1992) A training algorithm for optimal margin classifiers. Paper presented at the Proceedings of the fifth annual workshop on Computational learning theory, Pittsburgh, Pennsylvania, USA.
Cervantes, J., Li, X., Yu, W. & Li, K. (2008) Support vector machine classification for large data sets via minimum enclosing ball clustering, Neurocomputing, 71, pp. 611-619.
Chang, T-Y. (2011) The influence of agricultural policies on agriculture structure adjustment in Taiwan: An analysis of off-farm labor movement, China Agricultural Economic Review, 3(1), pp. 67-79
Coshall, J. T. (2009) Combining volatility and smoothing forecasts of UK demand for international tourism, Tourism Management, 30(4), pp. 495-511.
DeLurgio, S.A. (1998) Forecasting Principles and Applications. 1 edn. (New York: Irwin/McGraw-Hill).
Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A. J. & Vapnik, V. (1996) Support Vector Regression Machines. Paper presented at the NIPS'1996.
Huang, S-C. (2008) Online option price forecasting by using unscented Kalman filters and support vector machines, Expert Systems with Applications, 34(4), pp. 2819-2825.
Huang, M. (2015) Agricultural Economic Evaluation Based on Improved Support Vector Regression, 2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA), pp. 118-121.
Hsu, L-C. (2011) Using improved grey forecasting models to forecast the output of opto-electronics industry, Expert Systems with Applications, 38(11), pp. 13879-13885.
Kordos, M. & Rusiecki, A. (2015) Reducing noise impact on MLP training, Soft Computing, 20(1), pp. 49-65.
Lavanya, K., Raguchander, T. & Iyengar, N. C. S. (2013) SVM Regression and SONN based approach for seasonal crop price prediction, International Journal of u-and e-Service, Science and Technology, 6, pp. 155-168.
Lee, C-P. & Leu, Y. (2011) A novel hybrid feature selection method for microarray data analysis, Applied Soft Computing, 11(1), pp. 208-213.
Lee, C-P., Lin, W-C. & Yang, C-C. (2014) A strategy for forecasting option price using fuzzy time series and least square support vector regression with a bootstrap model, Scientia Iranica, 21, pp. 815-825.
Lee, C-P., Shieh, G-J., Yiu, T-J. & Kuo, B-J. (2013) The strategy to simulate the cross-pollination rate for the co-existence of genetically modified (GM) and non-GM crops by using FPSOSVR, Chemometrics and Intelligent Laboratory Systems, 122, pp. 50-57.
Lewis, J. (2014) Bayesian Restricted Likelihood Methods. (Electronic Thesis or Dissertation), available at: https://etd.ohiolink.edu/ (December 12, 2016).
Li, B., Zheng, D., Sun, L. & Yang, S. (2007) Exploiting multi-scale support vector regression for image compression, Neurocomputing, 70(16-18), pp. 3068-3074.
Liang, X., Zhang, H., Xiao, J. & Chen, Y. (2009) Improving option price forecasts with neural networks and support vector regressions, Neurocomputing, 72(13-15), pp. 3055-3065.
Liao, D. G. & Luo, Y. X. (2011) Grey new information GOM(1,1) model and its application based on opposite-direction accumulated generating and background value optimization, Advanced Materials Research, 321, pp. 33-36.
Lin, H-C. & Kao, T-M. (2006) The economic impact from agricultural products loss caused by natural disasters and regional input-output analysis in Taiwa, Taiwanese Agricultural Economic Review, 12, pp.105-138.
Meza, F. J., Hansen, J. W. & Osgood, D. (2008) Economic value of seasonal climate forecasts for agriculture: review of ex-ante assessments and recommendations for future research, Journal of Applied Meteorology and Climatology, 47, pp. 1269-1286.
Nieh, S.-C., Lin, W.-S., Hsu, Y.-H., Shieh, G.-J. & Kuo, B.-J. (2014) The effect of flowering time and distance between pollen source and recipient on Maize, GM Crops & Food: Biotechnology in Agriculture and the Food Chain, 5(4), pp. 21-33.
Ou, S-L. (2012) Forecasting agricultural output with an improved grey forecasting model based on the genetic algorithm, Computers and Electronics in Agriculture, 85, pp. 33-39.
Singh, S. (2009) Global food crisis: magnitude, causes and policy measures, International Journal of Social Economics, 36(1/2), pp. 23-36.
Slabe-Erker, R., Klun, M. & Lampič, B. (2016) Assessment of agricultural sustainability at regional level in Slovenia, Lex Localis-Journal of Local Self-Government, 14(2), pp. 209-223.
Viala, E. (2008) Water for food, water for life a comprehensive assessment of water management in agriculture, Irrigation and Drainage Systems, 22(1), pp. 127-129.
Wu, W-Y., Hsiao, S-W. & Tsai, C-H. (2008) Forecasting and Evaluating the Tourist Hotel Industry Performance in Taiwan Based on Grey Theory, Tourism and Hospitality Research, 8(2), pp. 137-152.
Xia, Z., Zhi, Z., Tong, S., Kun, S. & Yanli, S. (2014) Improved GM (1, 1) model for sea level change prediction, 2014 IEEE Geoscience and Remote Sensing Symposium, pp. 4469-4472.
Xiong, T., Li, C., Bao, Y., Hu, Z. & Zhang, L. (2015) A combination method for interval forecasting of agricultural commodity futures prices, Knowledge-Based Systems, 77, pp. 92-102.
Yousefi, M., Khoshnevisan, B., Shamshirband, S., Motamedi, S., Nasir, M. H. N., Arif, M., & Ahmad, R. (2015) Support vector regression methodology for prediction of output energy in rice production, Stochastic Environmental Research and Risk Assessment, 29(8), pp. 2115-2126.
Zheng, M-C. & Chou, J-H. (2011) A novel nonlinear forecasting model for output of bike industry by Grey model and Taguchi-differential evolution algorithm, African Journal of Business Management, 5(12), pp. 4945-4954.
Zięba, M. & Tomczak, J. M. (2014) Boosted SVM with active learning strategy for imbalanced data, Soft Computing, 19(12), pp. 3357-3368.
It is a condition that the authors assign copyright or license the publication rights in their articles, including abstracts, to Institute for Local Self-Government Maribor. This enables us to ensure full copyright protection and to disseminate the article, and of course Journal, to the widest possible readership in print and electronic formats as appropriate. Authors retain many rights under the Institutes' right policies, which can be found at journal.lex-localis.press. Authors are themselves responsible for obtaining permission to reproduce copyright material from other sources.