基于Metropolis准则的BP模型改进及在太湖Chl-a预测中的应用

冯家成; 李勇; 李娜; 单雅洁; 钱佳宁

doi:10.13205/j.hjgc.202201024

基于Metropolis准则的BP模型改进及在太湖Chl-a预测中的应用

doi: 10.13205/j.hjgc.202201024

冯家成^1,2,
李勇^1,2, ,,
李娜^1,2,
单雅洁²,
钱佳宁²

1. 河海大学浅水湖泊综合治理与资源开发教育部重点实验室, 南京 210098;
2. 河海大学环境学院, 南京 210098

基金项目:

国家自然科学基金项目(51879081)

国家重点研发计划(2018YFC0407906)

详细信息

作者简介:
冯家成(1997-),男,硕士研究生,主要研究方向为河湖水环境模型。student_fengran@163.com

通讯作者:
李勇(1974-),男,教授,主要从事水体和土壤中污染物环境行为、规划评价及生态修复等方面的教学和研究工作。liyonghh@163.com

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- 文章访问数: 129
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- 被引次数: 0
出版历程
- 收稿日期: 2021-03-25
- 网络出版日期: 2022-03-30
- 刊出日期: 2022-03-30

IMPROVEMENT OF BP MODEL BASED ON METROPOLIS CRITERION AND ITS APPLICATION IN CHLOROPHYLL-A PREDICTION FOR LAKE TAIHU

FENG Jiacheng^1,2,
LI Yong^{1,2
, ,},
LI Na^1,2,
SHAN Yajie²,
QIAN Jianing²

1. Key Laboratory of Shallow Lake Comprehensive Control and Resource Development, Ministry of Education, Hohai University, Nanjing 210098, China;
2. School of Environment, Hohai University, Nanjing 210098, China

摘要

摘要: 水体富营养化及藻华暴发已成为湖泊治理中的主要问题,利用历史监测数据,采用BP神经网络对水体中叶绿素a(Chl-a)浓度进行预测,已成为藻华预警的主要手段。但该方法存在迭代速度慢、易陷入局部极值等局限性,导致产生拟合结果不优或预测误差较大等问题。利用Metropolis接受准则的全局寻优能力,将其与BP神经网络相结合构建MBP模型,并以太湖水体中Chl-a月均浓度为预测对象进行验证模拟。结果表明,基于2009—2016年太湖月平均相关水环境数据训练出的MBP模型,相较于传统BP神经网络具有以下特点:1)权值在迭代过程的初始阶段能更快地收敛于较优值,模型拟合效果与预测精度也有所提升;2)针对不同的数据情况(数据噪声与样本数量),MBP模型的平均预测误差较传统BP神经网络明显降低,具有较强的鲁棒性与稳定性。构建的MBP模型进一步拓展了传统BP神经网络在叶绿素a浓度预测模型中的应用。
- BP神经网络 /
- Metropolis准则 /
- 模型适应性 /
- 叶绿素a浓度 /
- 太湖
Abstract: Eutrophication and algal blooms have become the main problems in lake management. Predicting chlorophyll-a concentration in water based on historical monitoring data using BP neural network is one of the main means for algal bloom early warning. However, traditional BP method has some limitations, such as low iteration speed and being easy to fall into local extremum, leading to a poor fitting result and larger prediction error. In this paper, a new model(MBP) was developed based on BP neural network by combining with the global optimization capability of Metropolis acceptance criterion, and then applied to predict the monthly average chlorophyll-a concentration of Lake Taihu.Resultsshowed that, comparing with traditional BP neural network, the improved MBP model had a relatively faster coverage velocity at the initial iteration stage, and showed lower fitting error and higher accuracy. Average prediction error of the MBP model was significantly lower than that of the traditional BP neural network. Additionally, the MBP model had a stronger robustness and stability for different data noise and smaller number of samples. This model further expanded the application of traditional BP neural network in predicting concentration of chlorophyll-a and provided a new idea for establishing an early warning system of algal bloom.
- BP neural network /
- Metropolis criterion /
- model adaptability /
- chlorophyll-a concentration /
- Lake Taihu

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