Prediction of Blue-Green Alage Cells in a City Water Source Based on the LSTM Model

China ranks sixth in the world in terms of freshwater resources, yet its per capita freshwater resources are only one-fourth of the global average. Additionally, freshwater resources exhibit spatial unevenness. Given the rapid pace of urbanization, the safety of China's freshwater resources in terms of water quality is at risk due to contamination. Consequently, the utilization of water quality models for predicting changes in water quality has emerged as a prominent research focus. The data-driven water quality prediction model provides a scientific basis for water resource management departments to provide early warning of algal blooms and formulate effective control measures in advance. In this study, we predicted the dominant algae (blue-green algae) population in a water source based on the Long Short-Term Memory (LSTM) network model. Additionally, we explored the effects of the type of feature combination and the step size of the time window on the prediction performance of the LSTM by establishing different feature combinations and different time windows as input methods. The experimental results show that the performance of multi-feature prediction is consistently superior to single-feature prediction. Simultaneously, increasing the number of features in the input model tends to diminish the model's predictive performance. The time window impacts the performance of LSTM predictions. As the step size increases, the model prediction performance gradually enhances, eventually stabilizing while concurrently incurring significant time overhead. Lastly, this study offers insights into future research directions from three key dimensions: the input indicator, optimization algorithm, and model combination.