Research on Wastewater Quality Prediction Method Based on DBSCAN Algorithm and Conv1D-LSTM Model

Wastewater treatment and its quality prediction have always been pivotal for environmental protection and public health. Accurate forecasting of wastewater quality parameters is essential for optimizing treatment processes and ensuring water safety. In light of this, our study proposes a hybrid model integrating Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Convolutional Neural Network-Long Short-Term Memory (Conv1D-LSTM) for wastewater quality prediction. DBSCAN enables anomaly detection in the influent data, reducing noise impact. The Conv1D-LSTM model automatically extracts features and captures time series dependencies. Experiments demonstrate superior performance of the proposed model over LSTM alone in both single-step and multi-step forecasting of ammonia nitrogen levels, with lower RMSE, MAE and RMSLE. The integration of DBSCAN and Conv1D-LSTM can effectively leverage their respective strengths for enhanced wastewater quality prediction. Further research will focus on multi-variable prediction and incorporating data smoothing techniques.