Spatial-temporal process simulation and prediction of chlorophyll-a concentration in Dianchi Lake based on wavelet analysis and long-short term memory network

Spatial-temporal process simulation and prediction of chlorophyll-a concentration in Dianchi Lake based on wavelet analysis and long-short term memory network
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基于小波分析和长短期记忆网络的滇池叶绿素a浓度时空过程模拟与预测

DOI:
10.1016/j.jhydrol.2019.124488
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发表时间:
2020-03
影响因子:
6.4
通讯作者:
Shang Chunxue
Shang Chunxue
中科院分区:
地球科学1区
文献类型:
--
作者:
Yu Zhenyu;Yang Kun;Luo Yi;Shang Chunxue

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With the rapid development of urbanization, the water pollution in Dianchi Lake presents the trend of combining urban and agricultural non-point source pollution, and it is more difficult to control and improve the water environment. The simulation and prediction of water quality state change is an important theoretical basis for water resources management. The data set we selected was 15 water quality parameters of 10 water quality observation sites in Dianchi Lake from 2005 to 2012. Wavelet Domain Threshold Denoising (WDTD), Wavelet Mean Fusion (WMF) and Long-Short Term Memory (LSTM) were combined to a WDTD-LSTM-WMF long-term prediction model that WMF was proposed based on WDTD in this paper. The model and geospatial analysis were used to simulate the historical change process of chlorophyll-a concentration (Chla) in Dianchi Lake and predicted the future trend of Chla. The results showed that the model has a good prediction performance of low error and high generalization (RMSE = 18.40, MAE = 13.56, R2= 0.63). The spatial visualization analysis showed that the region with Chla higher than 100 μg/L from 2005 to 2020 had a tendency to spread from north to west and then to southwest. This is related to the urbanization development and climate change in Kunming.
DOI: 10.1016/j.scitotenv.2019.133612
发表时间: 2019-12
期刊: The Science of the total environment
影响因子: --
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