Risk comprehensive evaluation of urban network planning based on fuzzy Bayesian LS_SVM

Reference:

He, Y. X., Tao, W. J., Dai, A. Y., Yang, L. F., Fang, R. and Li, F., 2010. Risk comprehensive evaluation of urban network planning based on fuzzy Bayesian LS_SVM. Kybernetes, 39 (5), pp. 707-722.

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Official URL:

http://dx.doi.org/10.1108/03684921011043206

Abstract

Purpose - The purpose of this paper is to use artificial intelligence to evaluate the risks of urban power network planning. Design/methodology/approach - A fuzzy Bayesian least squares support vector machine (LS_SVM) model is established in this paper, which can learn the risk information of urban power network planning through artificial intelligence and acquire expert knowledge for its risk evaluation. With the advantage of possessing learning analog simulation precision and speed, the proposed model can be effectively applied in conducting a risk evaluation of an urban network planning system. First, fuzzy theory is applied to quantify qualitative risk factors of the planning to determine the fuzzy comprehensive evaluation value of the risk factors. Then, Bayesian evidence framework is utilized in LS_SVM model parameter optimization to automatically adjust the LS_SVM regularization parameters and nuclear parameters to obtain the best parameter values. Based on this, a risk comprehensive evaluation of urban network planning based on artificial intelligence is established. Findings - The fuzzy Bayesian LS_SVM model established in this paper is an effective artificial intelligence method for risk comprehensive evaluation in urban network planning through empirical study. Originality/value - The paper breaks new ground in using artificial intelligence to evaluate urban power network planning risks.

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