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机器学习技术对水消耗预测的比较分析:来自科凯利省的案例研究
1Faculty of Computer and Information Sciences, Sakarya University, Sakarya 54050, Turkey.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
机器学习模型准确地预测水消耗,这对于资源管理至关重要. 梯度提升机 (GBM) 显示出强的表现,达到0.881.8的R2.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 水资源管理 水资源管理
背景情况:
- 准确的水消耗预测对于有效的水资源管理和规划至关重要.
- 由于COVID-19大流行严重影响了用水模式,因此需要更新预测模型.
- 大规模的数据集对于开发可靠和可概括的水消耗预测模型至关重要.
研究的目的:
- 为了比较分析各种机器学习 (ML) 技术来预测水消耗.
- 评估人工神经网络 (ANN),随机森林 (RF),支持矢量机器 (SVM) 和梯度增强机器 (GBM) 的性能.
- 调查优化技术,如粒子优化 (PSO) 和莱文伯格-马奎特 (LM) 对ML模型性能的影响.
主要方法:
- 利用了土耳其科凯利省5000名用户的综合用水消耗数据集.
- 采用了四种ML模型:ANN,RF,SVM和GBM,包含历史数据以提高准确性.
- 应用优化技术 (PSO,LM) 以提高ML模型性能,并使用交叉验证评估模型.
主要成果:
- 梯度增强机 (GBM) 模型表现出强大的预测能力,达到0.881.1的R2值.
- 对比分析突出了每个评估的ML技术的优点和局限性.
- 优化的ML模型在预测水消耗模式方面显示出更高的准确性和通用性.
结论:
- 机器学习算法,特别是GBM,是准确预测用水量的有效工具.
- 该研究提供了关于将ML应用于水资源管理的宝贵见解,考虑到大流行引起的变化.
- 结果为未来的研究和在水资源管理系统中实施ML提供了实际建议.
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