An Intelligent Water Quality Prediction Method Based on Fuzzy Feature-Enhanced BiLSTM Model

This paper proposes an intelligent water quality prediction method combining fuzzy feature enhancement, BiLSTM sequence modeling, and Kalman filtering for real-time correction and prediction of sensor data in water quality monitoring. Initially, fuzzy transformation extracts features from critical indicators such as temperature, dissolved oxygen, pH, and electrical conductivity. Subsequently, these features are integrated with sequence data and predicted using a BiLSTM-based deep learning network. Finally, the Kalman filter smoothes the preliminary predictions. Experimental results indicate that this method has robust adaptability and significantly enhances prediction accuracy and stability, providing a practical calibration strategy for monitoring systems.