Performance Evaluation of Deep Learning Models for Water Quality Index Prediction: A Comparative Study of LSTM, TCN, ANN, and MLP

This addresses water quality prediction for environmental monitoring, but it is incremental as it applies existing methods to a specific dataset.

The paper tackled the problem of predicting the Water Quality Index (WQI) by comparing the performance of deep learning models including LSTM, TCN, ANN, and MLP, with results showing specific performance metrics such as accuracy or error rates (e.g., LSTM achieved 95% accuracy).