基于机器学习的预测在发展中国家的建筑和拆除废物产生:一个案例研究
1Department, of Construction Science and Management, Clemson University, 1-171 Lee Hall, Clemson, SC, USA. miladj@clemson.edu.
概括
准确预测建筑和拆除废物 (C&DW) 是至关重要的,但数据有限. 这项研究开发了一种人工智能 (AI) 模型,ANFIS,可靠地预测每月的C&DW产量,帮助废物管理决策.
科学领域:
- 环境科学 环境科学
- 土木工程 土木工程是指土木工程.
- 数据科学数据科学数据科学
背景情况:
- 建筑和拆除废物 (C&DW) 生产的有限数据阻碍了有效的管理,特别是在发展中国家.
- 对废物管理的知情决策需要对C&DW数量的可靠预测.
- 建筑和拆除活动产生大量的废物流,需要战略规划.
研究的目的:
- 开发一种可靠的基于人工智能 (AI) 的模型,用于预测每月的C&DW产量.
- 通过预测建模来解决C&DW废物管理中的数据稀缺性挑战.
- 为了为德黑兰的政策和决策提供一个工具,伊朗的C&DW管理.
主要方法:
- 训练并评估了多个AI算法,包括多层感知神经网络,辐射基函数神经网络,支持矢量机器和自适应神经模糊推理系统 (ANFIS).
- 利用历史数据用于训练预测模型,预测C&DW的产生.
- 使用R平方 (R2) 和根平均平方误差 (RMSE) 等指标评估模型性能.
主要成果:
- 所有的人工智能算法都显示出C&DW预测的高预测性能.
- 安菲斯模型获得了最佳性能,R2=0.96和RMSE=0.04209.
- 由于它将模糊逻辑功能与神经网络集成用于建模主观变量,ANFIS脱而出.
结论:
- 开发的基于人工智能的预测模型,特别是ANFIS,为预测C&DW生成提供了可靠的方法.
- 这种预测工具可以大大帮助决策者和利益相关者做出明智的C&DW管理决策.
- 该研究强调了人工智能在解决环境管理挑战的数据限制方面的潜力.
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