This paper proposes a three (3)-step methodology to forecast the future water demands of a water distribution network (WDN) composed of ten (10) district metered areas (DMAs). First, pre-processing of the time-series data was performed through outlier elimination, imputation by K-Nearest Neighbors (KNN), and statistical data scaling. Second, the model hyperparameters were calibrated using Bayesian optimization. Third, Long Short-Term Memory (LSTM) coded as a Multi-Step Multivariate Time-Series forecasting model was implemented. Our results indicate that the proposed model produces accurate future water demands, suggesting that feasible short-term water demand forecasting models require combining engineering judgment and computational tools to achieve reliability.

In urban areas with a flat terrain, pumping stations must be included to elevate wastewater and avoid extreme excavation depths. These systems are characterized by high operational costs due to the pump’s power consumption. The present work presents a methodology for the optimal design of sewer networks including pumping stations, whose objective function is to minimize the construction and operation costs of the system. The methodology was tested on three sewer benchmark networks using two cost functions proposed in the literature. In all the sewer benchmarks, the cost achieved in the present work was compared with the best costs reported in the literature.

Water distribution systems (WDSs) are subject to operational changes due to variability in demands, the availability of flow rates, system maintenance and unexpected events. This study aims to assess the behavior of water quality in a distribution network associated with operational changes that the system typically undergoes. For this work, two black-box models were compared to predict chlorine concentration at different nodes in a small network and a large network. The model was trained with synthetic data from simulations through the EPANET-Python Toolkit. The results show that the black-box model can be implemented to predict water quality in real time.