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

FENG Jiacheng; LI Yong; LI Na; SHAN Yajie; QIAN Jianing

doi:10.13205/j.hjgc.202201024

Volume 40 Issue 1

Mar. 2022

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2022 > 40(1): 161-168

FENG Jiacheng, LI Yong, LI Na, SHAN Yajie, QIAN Jianing. IMPROVEMENT OF BP MODEL BASED ON METROPOLIS CRITERION AND ITS APPLICATION IN CHLOROPHYLL-A PREDICTION FOR LAKE TAIHU[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(1): 161-168. doi: 10.13205/j.hjgc.202201024

Citation:

FENG Jiacheng, LI Yong, LI Na, SHAN Yajie, QIAN Jianing. IMPROVEMENT OF BP MODEL BASED ON METROPOLIS CRITERION AND ITS APPLICATION IN CHLOROPHYLL-A PREDICTION FOR LAKE TAIHU[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(1): 161-168. doi: 10.13205/j.hjgc.202201024

Citation:

FENG Jiacheng, LI Yong, LI Na, SHAN Yajie, QIAN Jianing. IMPROVEMENT OF BP MODEL BASED ON METROPOLIS CRITERION AND ITS APPLICATION IN CHLOROPHYLL-A PREDICTION FOR LAKE TAIHU[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(1): 161-168. doi: 10.13205/j.hjgc.202201024

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

doi: 10.13205/j.hjgc.202201024

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

Received Date: 2021-03-25
Available Online: 2022-03-30
Publish Date: 2022-03-30

Abstract

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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References(39)

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