硬质和软质集群分析的综合研究,用于检测MSW垃圾填埋场的液,使用地质电气数据
Giorgio De Donno1, Davide Melegari1, Valeria Paoletti2
1Dipartimento di Ingegneria Civile Edile e Ambientale (DICEA), "Sapienza" Università di Roma, Rome, Italy.
Waste management (New York, N.Y.)
|January 30, 2025
概括
这项研究将硬质和软质集群与地电数据相结合,准确地绘制了垃圾填埋场液积聚区域. 机器学习可以提高液检测的准确性,降低监测成本,提高垃圾填埋场的安全性.
科学领域:
- 地质物理学 地质物理学
- 机器学习 机器学习
- 环境工程 环境工程
背景情况:
- 传统的地质物理方法在综合数据分析中扎,以获得准确的水评估.
- 不一致的漏深度估计和不可靠的预测评估是常见的挑战.
- 漏水的积累对地下水和垃圾填埋场的稳定性构成风险.
研究的目的:
- 通过综合数据分析,改进地理电气方法,以识别水积聚区.
- 为了比较硬质和软质聚类方法,以提高液检测准确度.
- 改进出水区的界限,了解垃圾填埋场的流动动力学.
主要方法:
- 应用硬集群 (K-平均) 和软集群 (模糊的C-平均) 到地电数据 (电阻,诱导极化).
- 综合多个地质物理参数,以进行全面的水评估.
- 使用井数据验证结果,在液识别中达到90%以上的准确性.
主要成果:
- 鉴定出液积聚区,占地底面约11%的区域.
- 具体的电阻,可充电性和正常化的可充电性范围被定义为漏区域.
- 软集群精细的边界,使和和不和区域和潜在的流动路径的映射.
结论:
- 与传统方法相比,硬质和软质聚类显著改善了垃圾填埋场的液检测.
- 综合方法提供了更可靠,更准确的液范围和和度的评估.
- 这种方法为降低垃圾填埋场监测和管理成本提供了实际意义.
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