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

TL;DR

This paper compares the performance of various deep learning models like LSTM, TCN, ANN, and MLP in predicting the Water Quality Index to identify the most effective approach for environmental monitoring.

Contribution

It provides a comprehensive comparative analysis of deep learning models for WQI prediction, highlighting their strengths and limitations.

Findings

LSTM outperforms other models in accuracy.

TCN offers faster training times.

ANN and MLP show moderate performance.

Abstract

Environmental monitoring and predictive modeling of the Water Quality Index (WQI) through the assessment of the water quality.