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

• Published in: Journal of hydrology (Amsterdam) Vol. 582; p. 124488
• Main Authors: Yu, Zhenyu, Yang, Kun, Luo, Yi, Shang, Chunxue
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2020
• Subjects: chlorophyll; climate change; Continuous wavelet transform; Cyanobacterial bloom; data collection; Geospatial analysis; hydrology; lakes; Long-short term memory network; neural networks; nonpoint source pollution; prediction; urbanization; water management; water pollution; water quality; wavelet; Wavelet threshold denoising; Cyanobacterial bloom; Wavelet threshold denoising; Geospatial analysis; Long-short term memory network; Continuous wavelet transform
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2019.124488

•Chlorophyll-a (Chla) higher than 100 μg/L from 2005 to 2020 have a tendency to spread.•Wavelet mean fusion (WMF) is proposed based on wavelet domain threshold denoising (WDTD)•WDTD, WMF and long-short term memory (LSTM) are combined to a long-term prediction model.•The model is used to predict Chla in Dianchi Lake for 8 years (RMSE = 18.40, MAE = 13.56, R2 = 0.63)•The model has the comprehensive prediction performance of low error and high generalization. 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.