一种基于管理和环境变量的新早期滴灌系统成本估计模型
Masoud Pourgholam-Amiji1, Khaled Ahmadaali2, Abdolmajid Liaghat1
1Department of Irrigation and Reclamation Engineering, Faculty of Agriculture, College of Agriculture and Natural Resources, University of Tehran, P. O. Box 4111, Karaj, 31587-77871, Iran.
Scientific reports
|February 3, 2025
概括
这项研究开发了机器学习模型,以准确估计早期滴灌系统成本. 最好的模型,支持矢量机 (SVM) 和人工神经网络 (ANN),利用环境和管理功能来准确预测成本.
科学领域:
- 农业工程 农业工程
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 压力灌系统,特别是滴灌,对于水资源管理至关重要.
- 对这些系统进行准确的早期成本估计是复杂的,而且还没有得到充分的研究.
- 了解成本驱动因素对于项目规划和投资至关重要.
研究的目的:
- 开发和验证机器学习模型,用于滴灌系统的早期成本估计.
- 确定影响系统成本的关键环境和管理特征.
- 为了比较各种功能选择和机器学习算法的性能.
主要方法:
- 编制了515个滴灌项目数据库,其中包括39个环境和管理特征.
- 使用特征选择算法 (例如LCA,FOA,Wrapper) 来识别影响成本的重要因素.
- 机器学习模型,包括支持矢量机 (SVM) 和人工神经网络 (ANN),进行了训练和测试.
- 模型性能使用R平方 (R2) 和根平均平方误差 (RMSE) 等指标进行评估.
主要成果:
- 在LCA和FOA特征选择算法证明了优秀的估计性能 (R2 ≈ 0.94,RMSE ≈ 0.002).
- 对于易于获得的特征,这些算法实现了0.95的R2和0.0006.6的RMSE.
- 在总体成本估计 (R2 ≈ 0.89-0.92) 方面,SVM模型 (RBF Kernel) 证明最有效.
- 在易于获得的特征 (R2 ≈ 0.88-0.91) 中,ANN模型 (MLP) 的表现最好.
结论:
- 机器学习模型,特别是SVM和ANN,可以准确地预测早期滴灌成本.
- 环境和管理特征是系统成本的重要预测因素.
- 开发的模型为灌项目的准确成本估计提供了有价值的工具.
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