合指数级光滑和灰色模型用于预测中国古河的水质
Manting Shang1, Jiaao Huang1, Peigui Liu2
1College of Civil Engineering, Hefei University of Technology, Hefei 230009, China.
概括
新的指数级光滑灰色模型 (ESGM(1,1)) 显著提高了水质预测的准确性. 这种增强的模型减少了错误,并为环境保护提供了可靠的短期水质预测.
科学领域:
- 环境科学 环境科学
- 水质监测 水质监测
- 预测建模预测建模
背景情况:
- 传统的灰色模型 (GM(1,1)) 由于初始数据序列的影响,其预测准确性较差.
- 准确的水质预测对于有效的环境管理和污染控制至关重要.
研究的目的:
- 开发一个改进的灰色模型,即指数级光滑灰色模型 (ESGM(1,1)),以提高预测准确性.
- 评价ESGM的性能{1,1) 与传统的GM{1,1) 模型对水质预测的性能.
主要方法:
- 这项研究利用了来自安站 (2010-2021) 的水质数据 (氨, permanganate指数).
- 使用模拟和验证时期对GM(1,1) 和ESGM(1,1) 模型进行了比较分析.
- 使用平均相对百分比误差和平均平方误差比率 (C) 评估了模型适配的准确性.
主要成果:
- 与GM相比,ESGM(1,1) 在模拟期间的平均相对百分比错误减少了3.01%,在验证过程中减少了27.41%.
- 对于ESGM的平均平方误差比率 (C) 是0.59,通过了准确性测试,而GM的C值为0.79,失败了.
- ESGM(1,1) 的预测与2010年至2021年的历史监测数据非常相匹配.
结论:
- 与GM相比,ESGM提供了对短期水质预测的卓越准确性.
- 该模型有效地减轻了初始数据序列对预测准确性的影响.
- ESGM ((1,1) 是当地水污染控制和环境保护工作的一个有价值的工具.
关键词:
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