规范化和可解释的机器学习框架,用于人类-气候结合预测超干旱城市公用事业的影响废水
1Department of Civil Engineering, College of Engineering and Petroleum (COEP), Kuwait University, Sabah Al Salem University City, P.O. Box 5969, Safat 13060, Shadadiya, Kuwait
概括
预测废水流入对于数据有限的公用事业的高效运营至关重要. 像LASSO这样的处罚线性模型提供可靠,低成本的预测,有助于水资源管理.
科学领域:
- 环境工程 环境工程
- 水资源管理 水资源管理
- 数据科学数据科学数据科学
背景情况:
- 可靠的废水影响预测对于气候敏感,数据有限的水利设施的能源高效运行至关重要.
- 准确的预测支持运营规划和资源管理.
研究的目的:
- 为了对每个月在科威特流入的废水进行各种预测模型的比较.
- 通过使用关键的环境和消费变量来确定最有效的预测方法.
主要方法:
- 逐步线性回归 (SLR),集合树 (ET),支向量机 (SVM),内核近似和处罚线性回归 (LASSO) 的比较分析.
- 利用空气温度,相对湿度,市政用水量和人口作为预测因素.
- 采用五重交叉验证与时间顺序进行的测试集,评估RMSE,MAE,MSE,R2和MAPE的性能.
主要成果:
- 处罚线性回归 (LASSO) 显示了最小的测试误差,并选择了稀疏的模型规范.
- 渐进线性回归 (SLR) 和集合树 (ET) 显示出可比性能,优于内核方法.
- 通过使用SHAP摘要和特征重要性图表,增强了模型的解释性.
结论:
- 低复杂度,透明的模型,特别是处罚线性模型,提供了显著的预测技能,最小的调整.
- 这些模型增强了运营商的信任和可审计性,这对于实际实施至关重要.
- 开发的框架为超干旱地区的负载管理,储存,再利用计划和替代供水决策提供了一个月前的指导.
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