Big Data Analytics for Long-term Large Interval Gap-Filling in a Field-scale Water Filtration System toward AI-Powered Performance Predictions

Water quality monitoring with distributed water quality probes in a water filtration system is crucial for public health and environmental protection. However, the Long-term Large Interval Gap-free of environmental data collected from a water filtration system has been a challenge for performance prediction, on-line diagnosis, and process control. This presentation introduces a novel long interval gap-filling method using a Bidirectional Long Short-Term Memory Autoencoder with Multi-head Attention Mechanism (BiLSTM-AAM) in the first stage of the AI-powered big data analytics. The multi-head attention mechanism allows the model to focus on informative regions within the data, even with extensive gaps while the autoencoder module aids in dimensionality reduction. The efficacy of BiLSTM-AAM was evaluated by real-world water quality datasets and further tested in other environmental contexts, demonstrating superior performance compared to three other models. The second stage of the AI-powered big data analytics focused in predicting water quality parameters with complex nonlinear and dynamic nature of the data influenced by various environmental, physicochemical, and microbiological factors. We designed an AI-powered model called Adaptive QUantitative Analysis with GRadient Ascent and Dynamic Hierarchical BiLSTM Learning (AQUA-GRAD- BILL) to help automatic prediction and control of a water filtration system. The model simultaneously processes and integrates multiple datasets, including water quality parameters, microbial species data, and weather forecasting data collected from the field-scale water filtration system. By incorporating reinforcement learning, the model dynamically adapts to changing environmental conditions, enhancing its responsiveness and accuracy. Performance evaluation against traditional models demonstrates superior accuracy and robustness of the proposed prediction system when dealing with differing patterns in big data analytics. The BiLSTM-AAM confirms the ability to accurately reconstruct missing values, capture complex dependencies, and maintain data integrity even with high percentages of missing data in the first stage whereas the AQUA-GRAD-BILL explores the model's adaptability through transfer learning and evaluates its performance under various scenarios in the second stage. This big data analytics study showcases transdisciplinary frontiers in system of systems engineering innovations that are indicative of boundary-breaking implications for environmental engineering, pattern recognition, data science, and machine intelligence. It ultimately contributes to better sustainable design, strategies, and practices for complex large-scale systems and beyond.