Deep learning based an effective hybrid model for water quality assessment.

Area of Science:

• Environmental Science
• Data Science
• Water Resource Management

Background:

• Freshwater resources are declining due to climate change, pollution, and population growth.
• Water pollution significantly impacts ecological balance and human use, necessitating stringent quality control.
• Effective water quality assessment is crucial for sustainable water resource management.

Purpose of the Study:

• To develop and evaluate a novel hybrid Convolutional Neural Networks-Long Short-Term Memory (CNN-LSTM) model for accurate water quality assessment.
• To compare the performance of the proposed CNN-LSTM model against established machine learning algorithms.
• To validate the model's capability in ensuring water safety according to established quality criteria.

Main Methods:

• A hybrid model integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) was developed.
• The model's performance was evaluated using metrics including accuracy, precision, recall, F-score, and Area Under the Curve (AUC).
• Comparative analysis was conducted against algorithms such as AdaBoost, Decision Tree (DT), Gaussian Naive Bayes (GNB), k-Nearest Neighbors (kNN), LightGBM (LGBM), and Random Forest (RF).

Main Results:

• The proposed CNN-LSTM model achieved a high classification accuracy of 98.81%.
• Exceptional performance was noted with 99.03% precision, 99.65% recall, and a 99.33% F-score.
• The model demonstrated a strong Area Under the Curve (AUC) of 93%, indicating robust predictive power.

Conclusions:

• The developed CNN-LSTM hybrid model offers a highly accurate and reliable solution for water quality assessment.
• This model significantly outperforms traditional methods, providing a valuable tool for environmental monitoring.
• The findings support the model's application in ensuring the safe and sustainable use of water resources.