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基于灰狼优化 - - 在填埋场挖掘过程中支持矢量回归的填埋气体度预测

Zhansheng Liu¹, Zehua Zhang¹, Qingwen Zhang¹

¹The College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China; The Key Laboratory of Urban Security and Disaster Engineering of the Ministry of Education, Beijing University of Technology, Beijing 100124, China.

Waste management (New York, N.Y.)

|March 5, 2025

概括

使用灰狼优化 (GWO-SVR) 优化支向量回归可以提高垃圾填埋气体 (LFG) 预测的准确性. 这种方法可以稳定和准确地预测LFG度,这对于环境管理至关重要.

科学领域:

环境科学环境科学
数据科学数据科学数据科学
化学工程是化学工程的重要组成部分.

背景情况:

垃圾填埋场达到容量,由于基础设施老化,造成环境风险.
挖掘和运输多余的废物会产生大量的垃圾填埋气体 (LFG) 排放,污染空气质量.
准确预测和控制LFG对于减轻环境影响至关重要.

研究的目的:

开发和评估一个优化的模型来预测垃圾填埋场气体度.
将拟议的GWO-SVR模型的性能与传统的SVR和随机森林 (RF) 算法进行比较.
评估模型在预测各种LFG组件方面的有效性,包括气味,氨,硫化,甲和氧化.

主要方法:

使用灰狼优化 (GWO) 算法来提高支持向量回归 (SVR) 模型的性能.
使用历史LFG度数据和实时环境监测数据建立预测模型.
将GWO-SVR与标准的SVR和随机森林 (RF) 进行比较,用于预测气味,氨,硫化,甲和氧化.

主要成果:

与SVR和RF相比,GWO-SVR模型在预测各种LFG度方面表现出卓越的准确性和稳定性.
对于GWO-SVR的确定系数 (R2) 比SVR和RF大约高出10%.
在预测气味,硫化和氧化方面,GWO-SVR实现了明显较低的误差指标,超过了SVR和RF.

结论:

GWO-SVR算法大大提高了垃圾填埋气体度的预测准确度.
这种优化的模型有效地满足了现场垃圾填埋场管理的需求.
这些发现突显了GWO-SVR在精确环境监测和控制LFG排放方面的潜力.

关键词:

灰狼优化算法灰狼优化算法垃圾填埋场气体预测预测垃圾填埋场治理的治理方式支持矢量回归的支持矢量回归

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