基于数据包络分析和扩展置信规则库的交通运输业环境治理成本预测

针对交通运输业中环境治理成本规划问题，提出基于数据包络分析(DEA)和扩展置信规则库(EBRB)的环境治理成本预测模型. 基于DEA模型对环境治理投入产出历史数据进行有效性分析，量化EBRB中每条规则的可靠度，建立考虑规则可靠性的EBRB模型，用于预测交通运输业的环境治理成本. 根据2004-2017 年我国各省份交通运输业环境治理实际数据验证模型. 研究结果表明，本文模型的准确性高于现有环境治理成本预测方法，可为相关决策者提供一个模型支撑和参考依据.

Environmental Management Cost Prediction by Data Envelopment Analysis and Extended Belief Rule-based System for Transportation Industry

To address the environmental cost planning problem in transportation industry, an environmental management cost prediction model by data envelopment analysis (DEA) and extended belief rule-based (EBRB) system is proposed. The historical environmental input-output data is analyzed by the DEA model and applied for the reliability quantization of each rule; and an EBRB model that considers the rule reliability is established for environmental governance cost prediction of transportation industry. Finally, based on the data of transportation industry environmental management from 2004 to 2017 to verify the accuracy of the proposed model, and the results showed that the proposed model has higher accuracy than the existing cost prediction models. The investigation of this study can provide model support and reference for decision-makers.