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使用Young的双实验预测溶解的氧气水平基于优化器的权重模型.

Ying Dong1, Yuhuan Sun1, Zhenkun Liu2

  • 1School of Statistics, Dongbei University of Finance and Economics, No. 217, Jianshan Road, Shahekou District, Dalian, Liaoning Province, 116025, China.

Journal of environmental management
|December 15, 2023
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概括
此摘要是机器生成的。

准确的溶氧水平 (DOL) 预测对于水资源管理至关重要. 一个新的加权模型 (PWM) 整合了神经网络和统计方法,大大提高了预测准确度,并优于单个模型.

关键词:
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科学领域:

  • 环境科学 环境科学
  • 水资源管理 水资源管理
  • 计算智能是一种计算智能.

背景情况:

  • 预测溶解氧水平 (DOL) 对环境健康和水资源管理至关重要.
  • DOL的不规则性和波动性给预测带来了挑战,限制了单一模型的性能.
  • 现有的模型往往在准确性,有限的适用性和数据采集困难方面扎.

研究的目的:

  • 开发一种新型加权模型 (PWM),用于增强溶解氧水平预测.
  • 克服单一预测模型在准确性和范围上的局限性.
  • 提高水质预测的准确性,用于环境管理.

主要方法:

  • 提出了一个新的加权模型 (PWM),结合了八个神经网络和一个统计方法.
  • 采用Young的双实验优化器进行智能加权.
  • 使用来自美国俄勒冈州图拉林河流域的真实世界溶解氧数据验证了PWM.

主要成果:

  • 与单个机器学习和统计模型相比,PWM表现优越.
  • 在所有评估的加权模型中,实现了最低的平均绝对百分比误差.
  • 报告的平均绝对百分比误差为1.0216%,1.4630%和1.7087%,分别为一个,两个和三个阶段的预测.

结论:

  • PWM有效地整合了多种不同的建模方法,提高了预测准确度.
  • 这种方法为可持续的区域水环境发展提供了强大的技术支持.
  • 拟议的加权模型为溶解氧预测提供了更可靠的工具.