网络科学和可解释的基于AI的可持续性模型的生命周期管理
Ádám Ipkovich1, Tímea Czvetkó1, Lilibeth A Acosta2
1HUN-REN-PE Complex Systems Monitoring Research Group, University of Pannonia, Veszprém, Hungary.
PloS one
|June 13, 2024
概括
本研究引入了一种新的机器学习方法,使用Shapley价值观和网络分析来确定可持续发展目标 (SDGs) 的有效政策干预措施. 再利用水和循环水被确定为提高水效率和减少水压力的关键策略.
科学领域:
- 环境科学与环境政策
- 数据科学和机器学习
- 可持续性研究 可持续性研究
背景情况:
- 基于模型的评估对于理解对可持续发展目标 (SDGs) 的变量影响至关重要.
- 机器学习 (ML) 为可持续性规划和模型开发提供数据驱动的解决方案.
- 持续的模型审查和开发对于在动态环境中有效的决策支持至关重要.
研究的目的:
- 通过机器学习操作 (MLOps) 框架,提出和展示一种新的方法来确定SDG的政策干预点.
- 利用沙普利的价值观和网络分析来理解变量贡献和确定关键驱动因素.
- 通过对匈牙利水模型的案例研究来验证拟议的方法.
主要方法:
- 使用机器学习操作 (MLOps) 生命周期框架进行模型开发.
- 应用沙普利值来量化个别变量的直接和间接贡献.
- 采用网络分析来确定关键驱动因素和潜在的政策干预点.
- 通过使用匈牙利水模型进行了案例研究,重点关注SDG 6.4.1和6.4.2指标.
主要成果:
- 与定价或利用可再生水资源相比,确定了再利用水和水循环是更有效的干预选项.
- 量化了各种变量对水效率和水压力指标的贡献.
- 验证了沙普利价值和网络分析在识别有影响力的政策杆方面的实用性.
结论:
- 提出的基于MLOps的方法有效地确定了关键驱动因素,并验证了可持续发展目标的政策干预措施.
- 水的再利用和循环利用为增强水资源安全和实现可持续发展目标提供了有希望的战略.
- 这种方法为数据驱动的可持续发展规划和决策提供了一个强大的框架.
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