Results Uncertainty of Support Vector Machine and Hybrid of Wavelet Transform-Support Vector Machine Models for Solid Waste Generation Forecasting
Results Uncertainty of Support Vector Machine and Hybrid of Wavelet Transform-Support Vector Machine Models for Solid Waste Generation Forecasting
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DOI：
10.1002/ep.11747
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发表时间：
2014-04-01
影响因子：
2.8
通讯作者：
Baghvand, A.
中科院分区：
文献类型：
--
作者：
Abbasi, M.;Abduli, M. A.;Baghvand, A.
城市生活垃圾产生量预测在城市生活垃圾管理系统中起着重要的作用。然而，对于垃圾的非均匀性和各种失控因素对城市生活垃圾的影响，实现预期的预测精度是相当具有挑战性的。本文采用人工智能技术之一的支持向量机（SVM），结合小波变换（WT）和支持向量机（WT-SVM）的混合方法，对德黑兰和马什哈德市2006年1月至2011年12月的MSWG周时间序列进行了预测。为了提高支持向量机模型的性能，考虑到噪声的影响和传统去噪技术的缺点，采用小波去噪方法对MSWG时间序列进行降噪或去噪。由于支持向量机等数据驱动模型存在难以量化的不确定性，因此不确定性的确定是支持向量机结果分析的重要空白之一。因此，采用蒙特卡罗方法对模型结果的不确定性进行分析。结果表明，这两种模型都能准确预测德黑兰和马什哈德城市的MSWG。然而，通过小波变换对输入变量进行预处理，可以开发出一个更准确的模型来预测这两个城市的每周MSWG。不确定性分析也验证了WT-SVM模型比SVM具有更强的鲁棒性，对输入变量变化的敏感性较低。(3)环境工程学报，2013,33 (3):391 - 398,2014
The prediction of municipal solid waste generation (MSWG) plays an important role in a solid waste management system. However, achieving the anticipated prediction accuracy with regard to the nonhomogeneous nature of waste and effect of various and out of control factors on MSWG is quite challenging. In this article, support vector machine (SVM), one of the artificial intelligence techniques, and hybrid of wavelet transform (WT) and support vector machine (WT-SVM) are used to predict weekly time series of MSWG in Tehran and Mashhad cites during the period of January 2006-December 2011. To improve the performance of SVM model, considering the influence of noise and the disadvantages of traditional noise eliminating technologies, the wavelet denoising method is applied to reduce or eliminate the noise in MSWG time series. Since Data-driven models such as SVM involve potential of uncertainty that is difficult to quantify, uncertainty determination is one of important gaps observed in SVM results analysis. Therefore, Monte Carlo method was used to analyze uncertainty of the model results. Results showed both models could precisely predict MSWG in Tehran and Mashhad cites. However, the preprocessing of input variables by WT led to develop a more accurate model for prediction of weekly MSWG in both cities. The uncertainty analysis also verified that the WT-SVM model had more robustness than SVM and had a lower sensitivity to change of input variables. (c) 2013 American Institute of Chemical Engineers Environ Prog, 33: 220-228, 2014