The purpose of this study is to compare the life cycle assessment and reliability of different rainwater harvesting (RWH) systems in residential buildings in Tehran for a period of 50 years. Four main scenarios based on the number of stories (1, 2, 3, and 4) each including five sub-scenarios based on the size of the rainwater storage tank (2.5m³, 5m³, 7.5m³, 10m³, 12.5m³) and one using solely tap water were defined. Simapro software was used for life cycle assessment which was carried out using the endpoint and midpoint methods. The collected rainwater is assumed to be merely used to fill flush tanks and if it cannot satisfy this demand, tap water will be used. The results show that in an arid city like Tehran, collecting rainwater does not even come close to meeting non-potable water needs, and in most scenarios, tap water must be used for more than half of the days. Despite the low reliability of RWH systems, they perform better than tap water in most environmental impact categories because the processes used to produce tap water have the highest contribution to environmental damage. Among the sub-scenarios that use rainwater for flush tank demands, in almost all environmental impact categories, sub-scenarios that collect more rainwater (higher storage tank sizes) have better performance. The performance of sub-scenarios that use more rainwater is better in the endpoint environmental categories of ecosystems and human health; however, this trend is the opposite in the impact category of resources. Graphical abstract

This study investigates the capability of Gaussian process regression (GPR) models in the probabilistic forecasting of water flow and depth in a combined sewer system. Traditionally, deterministic methods have been implemented in sewer flow forecasting and anomaly detection, two crucial techniques for a good wastewater network and treatment plant management. However, with the uncertain nature of the factors impacting on sewer flow and depth, a probabilistic approach which takes uncertainties into account is preferred. This research introduces a novel use of GPR in sewer systems for real-time control and forecasting. To this end, a composite kernel is designed to capture flow and depth patterns in dry- and wet-weather periods by considering the underlying physical characteristics of the system. The multi-input, single-output GPR model is evaluated using root mean square error (RMSE), coverage, and differential entropy. The model demonstrates high predictive accuracy for both treatment plant inflow and manhole water levels across various training durations, with coverage values ranging from 87.5% to 99.4%. Finally, the model is used for anomaly detection by identifying deviations from expected ranges, enabling the estimation of surcharge and overflow probabilities under various conditions.

This study investigates the capability of Gaussian process regression (GPR) models in probabilistic forecasting of water flow and depth in a combined sewer system. Traditionally, deterministic methods have been implemented in sewer flow forecasting and anomaly detection which are two crucial techniques for a good wastewater network and treatment plant management. However, with the uncertain nature of the factors impacting on sewer flow and depth, a probabilistic approach which takes uncertainties into account is preferred. Using hydraulic simulation data generated in this study, a composite kernel is designed to take the flow and depth patterns based on the flow characteristics, and the hyperparameters are optimised through maximisation of the log-likelihood function. The GPR model is a multi-input, single-output model taking time and precipitation as inputs. Prediction results show reliable model outputs and are evaluated by three metrics: root mean square error, coverage and differential entropy. The model effectively predicts treatment plant inflow and manhole water levels with different training periods. Finally, the model is used for anomaly detection by identifying deviations from expected ranges, enabling the estimation of surcharge and overflow probabilities under various conditions.

Accurate forecasting of urban drainage flows is critical for mitigating environmental pollution and optimising wastewater treatment. While purely data-driven models are computationally efficient, they often lack physical interpretation and can produce unrealistic predictions. This study proposes a probabilistic framework using physics-informed Gaussian Process Regression (GPR) to forecast Wastewater Treatment Plant inflows and Combined Sewer Overflows (CSOs). Using high-resolution sensor data from the UWO dataset in Switzerland, a naïve baseline GPR was systematically enhanced by integrating domain knowledge through composite kernel engineering, SWMM-derived prior mean functions, and strict physical output constraints. To overcome the significant computational bottleneck of exact GPs, sparsification was implemented. Furthermore, a novel stratified sparsification method was developed specifically for event-based CSO time-series to optimally allocate inducing points during rare wet-weather surges, as these events do not follow a predictable pattern that can be captured by the basic physics of the model. Results demonstrate that integrating physical constraints significantly reduces predictive error and uncertainty. Operational forecasting of upstream tank dynamics provided a robust, probabilistic early-warning trigger for CSOs that mitigates false alarms. Crucially, the stratified sparse GPR framework reduced execution times by an order of magnitude with minimal accuracy loss, offering a scalable, physics-informed tool for real-time urban drainage management.