Machine Learning Models for Water Quality Prediction

Machine learning (ML) is highly effective for predicting water quality parameters such as pH, dissolved oxygen, turbidity, and overall Water Quality Index (WQI). Recent comparative studies have evaluated various ML algorithms including Random Forest (RF), Support Vector Machines (SVM), Artificial Neural Networks (ANN), Extreme Gradient Boosting (XGBoost), LightGBM, and Long Short-Term Memory (LSTM) networks.

Best Performing Models

• XGBoost and LSTM consistently outperform other models, achieving accuracy rates up to 99.83% and R² values near 0.9999, respectively. These models handle complex, multivariate data effectively and generalize well to unseen data.
• Random Forest is robust in managing high-dimensional and nonlinear relationships, reducing overfitting by averaging multiple decision trees.
• LightGBM also shows excellent performance, especially in predicting pH values.
• SVM can perform well but is sensitive to feature scaling and may struggle with complex nonlinearities.
• Neural Networks (MLP) are effective but require careful data preprocessing and scaling.

Data Preprocessing and Techniques

• Handling missing data through imputation is critical.
• Feature scaling and encoding categorical variables improve model performance.
• Dimensionality reduction methods like Principal Component Analysis (PCA) can enhance accuracy by simplifying feature space.
• Addressing class imbalance using techniques like SMOTE improves classification outcomes.
• Data augmentation and normalization (e.g., Yeo–Johnson transformation) help in stabilizing model training.

Common Challenges

• Missing or incomplete data.
• High variability and noise in environmental measurements.
• Temporal and spatial dependencies in water quality data.
• Class imbalance in classification tasks.
• Need for robust feature selection to avoid overfitting.

Dataset Sources

• Public repositories such as the UCI Machine Learning Repository and Kaggle offer water quality datasets.
• Governmental and environmental agencies provide monitoring data including parameters like temperature, salinity, dissolved oxygen, pH, and turbidity.
• Research datasets from recent publications often include large, labeled samples suitable for training and benchmarking ML models.

Key References with Links

Thank ypu

Hi, there is not a perfect algorithm, you need to first understand your problem, data and test with different approach, parametrization and approaches.

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