Prediction of Chl-a Concentration Based on Time-Lag Effects and LSTM Model: A Case Study of Wuli Lake

Accurately predicting chlorophyll-a (Chl-a) concentration in lakes is critical for water-quality management and ecological protection. This study investigated whether the time-lagged effects of meteorological factors improve Chl-a prediction at one fixed station in Wuli Lake, using hourly observations from May to October 2023. Rank correlations, prior process knowledge and multicollinearity diagnostics were used to identify a nonredundant driver set before interpreting meteorological lags. Cross-wavelet transform and wavelet cross-correlation analyses identified sub-daily lag associations: phase-derived lags were approximately 7.48 ± 4.03 h for air temperature and 6.44 ± 0.84 h for wind speed, while the corresponding cross-correlation maxima occurred at 9 h and 8 h. Incorporating lag-aligned meteorological inputs into a long short-term memory (LSTM) model for 1 h-ahead prediction increased the mean R2 across 100 training runs per scheme from 0.719 to 0.809 and reduced MAE and prediction-bias dispersion; RMSE remained approximately 0.0017 mg/L at the reported precision. Factorial ablations separately evaluated temperature and wind alignment and showed that their gains depend on the input-variable set. These descriptive results suggest that wavelet-derived temporal alignment may improve the accuracy of hourly Chl-a prediction and provide a basis for prospective bloom-warning systems.