IWS Challenge

Prediction Module

Advanced machine learning and deep learning models for accurate tunnel water level prediction with comprehensive data preprocessing and model optimization.

Comprehensive Methodology

The prediction module implements a systematic five-step approach: Data Preprocessing, Exploratory Data Analysis (EDA), Model Development, Hyperparameter Tuning, and Model Evaluation.

Processing Pipeline

• Data Preprocessing: 243 → 42 features through advanced feature selection
• PCA Analysis: Dimensionality reduction and collinearity handling
• Downsampling: Temporal optimization for improved model performance
• Dual Dataset Creation: PCA-based and raw downsampled versions

Model Arsenal

Machine Learning Models: Random Forest, XGBoost, LightGBM

Deep Learning Models: Feed Forward ANN, LSTM

Models selected for proven performance in multivariate time-series forecasting applications.

Prediction Results

Best Model: LSTM

RMSE: 0.087

NSE: 0.94

RSR: 0.25

Figure 1: Comprehensive methodology for tunnel water level prediction showing the five-component pipeline from data preprocessing through model evaluation and selection.

Protection Module

Advanced cyber-physical attack detection and classification system using SMOD dataset and state-of-the-art deep learning architectures.

Cybersecurity Framework

The protection module focuses on detecting and classifying cyber-physical attacks in WWTPs using the SMOD dataset, which includes comprehensive sensor data from Programmable Logic Controllers (PLCs).

Attack Detection Capabilities

• SMOD Dataset: Real-world PLC sensor data for training
• SMOTE Oversampling: Addressing imbalanced class distributions
• LSTM Architecture: Temporal pattern recognition for attacks
• GRU Comparison: Alternative RNN architecture evaluation

Classification Performance

Comprehensive evaluation using accuracy, precision, recall, and F1-score metrics to identify and classify different attack scenarios and intentions.

Protection Results

Best Model: LSTM

Attack Detection: 95%+

Precision: High

Response Time: Real-time

Optimization Module

Genetic Algorithm-based pump operation optimization for reducing wastewater overflow and improving operational efficiency during extreme weather events.

Intelligent Pump Scheduling

The optimization module addresses pump operations in WWTPs to minimize wastewater directed to wet-weather treatment facilities during extreme weather conditions, preventing costly overflow incidents.

Genetic Algorithm Implementation

• Objective Function: Minimize overflow risk and operational costs
• Constraint Handling: Pump capacity and operational limitations
• Multi-scenario Testing: Various weather and load conditions
• Real-time Integration: LSTM prediction-based optimization

Optimization Results

The GA model achieved 23% reduction in influent to wet-weather treatment plants, successfully preventing overflow incidents across five years of test data.

Optimization Results

Influent Reduction: 23%

Overflow Prevention: 100%

Test Period: 5 years

Energy Savings: Significant

Solution Visualization

Integrated AI system architecture

Comprehensive model evaluation results

P₂O real-time monitoring interface

Performance Analysis

Comprehensive performance metrics and comparative analysis

Complete system integration architecture

Figure 2: Methodology for Detecting Cyber Attacks.

Data preprocessing, model development, and evaluation techniques are discussed. The dataset was oversampled using the SMOTE technique to address imbalanced class distributions. LSTM and GRU models were developed and compared for accuracy, precision, recall, and F1-score metrics to identify and classify attack intentions.

Results of Protection Module

Overall, the LSTM model showed higher accuracy for classifying attacks, achieving over 95% accuracy in detecting intentional attacks. The GRU model, however, performed better in terms of misclassification rate for outlier events.

Optimization Module

This module optimizes pump operations in WWTPs to reduce the amount of wastewater directed to the wet-weather treatment plant (WWTP) during extreme weather events. The optimization problem is solved using a Genetic Algorithm (GA), which determines the optimal pump operation schedule to prevent overflow incidents.

Figure 3: Optimization Methodology for Pump Operations.

The GA model was tested with a variety of scenarios and reduced the influent to the wet-weather treatment plant by 23%, preventing overflow incidents over five years of test data. The optimization is based on real-time predictions from the LSTM model.

Results

The results from all three modules (Prediction, Protection, and Optimization) demonstrate the efficacy of the AI-driven solution for managing WWTPs during extreme weather conditions. The LSTM model provided the most accurate predictions for wastewater levels, and the optimization module successfully recommended actionable steps to avoid overflow scenarios. Additionally, the protection module effectively detected and classified cyber-physical attacks, improving the overall security of the system.

Conclusion

The 2022 IWS Challenge AI solution for water systems provides an integrated approach to wastewater management. Combining prediction, protection, and optimization modules helps in making informed decisions, ensuring operational efficiency, and safeguarding water treatment plants against potential threats. The graphical user interface of P₂O offers real-time insights for plant operators, helping them manage both day-to-day operations and emergency situations efficiently.