Context-driven Deep Learning Forecasting for Wastewater Treatment Plants

Abstract

Wastewater-treatment utilities face various operational challenges that could benefit from embodied AI and other advanced cyber-physical technologies. These challenges include optimizing pump schedules, managing energy and chemical consumption during extreme weather events, and interpreting sensor data for water-quality treatment. Addressing these issues requires accurate short-term, multi-step forecasting tools to provide reliable real-time decision support, particularly during heavy rainfall events that can overwhelm operations. Leading water-system operators and vendors in the United States report that tools capable of forecasting 4–6 hours ahead can significantly enhance resource management, including energy, chemicals, and manpower. However, accurate short-term forecasting is particularly difficult because of the non-linearities and seasonal variations inherent in plant data, which limit effective decision-making. To address these challenges, we propose cP2O, a context-driven forecasting solution, a novel hybrid deep-learning architecture integrating dynamic context extraction with hierarchical, dilated long-short-term memory (LSTM) cells. The proposed model utilizes internal water-system data, such as flow rates and tunnel levels, along with exogenous variables including weather, river flow, and demographic information to derive relevant context. It captures both short-term fluctuations and long-term dependencies in water-level data, while an internal attention mechanism dynamically weighs the importance of exogenous information. We validate the model on two full-scale utilities: tunnel-water-level forecasting at DC Water’s Blue Plains facility and nitrate-level prediction at AlexRenew. Relative to strong baselines, cP2O reduces mean absolute percentage error by 22 % and 19 %, respectively, and its 90 % prediction bands cover 90.5 % ± 3.2 % of observations (5.9 % below, 3.6 % above). By dynamically incorporating contextual information, especially under critical conditions, the model delivers reliable real-time forecasts that enhance resource allocation and strengthen the overall resilience of wastewater-treatment operations.