机器学习和分析层次的过程集成为选择一个可持续的拖拉机.
Hassan A A Sayed1,2, Mahmoud A Abdelhamid3, Tarek Kh Abdelkader4,5
1School of Energy and Environment Science, Yunnan Provincial Rural Energy Engineering Key Laboratory, Yunnan Normal University, Chenggong University Town, No. 768 Juxian Road, Kunming, 650500, P.R. China. hassan2712@azhar.edu.eg.
Scientific reports
|November 5, 2024
概括
这项研究通过将分析层次过程 (AHP) 和机器学习 (ML) 结合起来,简化了小型农场的拖拉机选择. 最佳的拖拉机平衡价格,功率和维护成本,以实现可持续和高效的农业.
科学领域:
- 农业工程 农业工程
- 决策科学 决策科学 决策科学
- 可持续农业 可持续农业
背景情况:
- 为小型农场选择拖拉机涉及复杂的技术,环境和经济因素.
- 现有的方法往往缺乏效率和对可持续性的关注.
研究的目的:
- 为了简化埃及三角洲小规模农场的拖拉机选择.
- 确定与可持续发展目标一致的关键标准.
- 整合分析层次过程 (AHP) 和机器学习 (ML) 以优化决策.
主要方法:
- 利用带有欧几里德距离的等级聚合集群,将九个标准减少到三个:价格,功率和维护成本.
- 应用AHP来权衡和优先考虑这些减少的标准.
- 根据专家的意见,评估了四台拖拉机 (55-95马力).
主要成果:
- 价格,电力和维护成本被确定为最关键的标准,分别为0.142,0.334和0.525.
- 拖拉机T2成为最佳选择,优先分数为0.326 (33.4%).
- 与T2.2相比,拖拉机T1 (28.7%) 和T3 (21%) 的情况不太理想.
结论:
- 综合的AHP和ML方法有效地简化了小型农场的拖拉机选择.
- 这种方法确保所选择的拖拉机是可持续的,具有成本效益和运营效率.
- 该研究为农民提供了一个实用的框架,使他们能够做出明智的决定.
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