This paper presents an investigation of the capacity of machine learning methods (ML) to localize leakage in water distribution systems (WDS). This issue is critical because water leakage causes economic losses, damages to the surrounding infrastructures, and soil contamination. Progress in real-time monitoring of WDS and ML has created new opportunities to develop data-based methods for water leak localization. However, the managers of WDS need recommendations for the selection of the appropriate ML methods as well their practical use for leakage localization. This paper contributes to this issue through an investigation of the capacity of ML methods to localize leakage in WDS. The campus of Lille University was used as support for this research. The paper is presented as follows: First, flow and pressure data were determined using EPANET software; then, the generated data were used to investigate the capacity of six ML methods to localize water leakage. Finally, the results of the investigations were used for leakage localization from offline water flow data. The results showed excellent performance for leakage localization by the artificial neural network, logistic regression, and random forest, but there were low performances for the unsupervised methods because of overlapping clusters.

This paper presents the design and construction of a smart and sustainable fish farm. The farm is based on the hybrid aquaculture concept that combines the advantages of the recirculation aquaculture system (RAS), the zero-water discharge (ZWD), and smart technology. The system’s smartness consists of using the latest technology, including monitoring and automation, ensuring zero water discharge, good water quality, reduced energy consumption, and high biosecurity. In addition, the system does not use chemicals in the aquaculture process. The paper describes the design of this system, including the hydraulic system, the smart monitoring system, the feeding regime, and the control of the biological parameters. Tests conducted in Tabuk, Saudi Arabia, showed high performance of this system with around 90% survival rate. In addition, observations showed the system’s capacity to maintain the water quality parameters within the optimal water quality ranges, reduce energy consumption, and sustain efficient shrimp waste recycling.

Urbanization and an increase in precipitation intensities due to climate change, in addition to limited urban drainage systems (UDS) capacity, are the main causes of combined sewer overflows (CSOs) that cause serious water pollution problems in many cities around the world. Model predictive control (MPC) systems offer a new approach to mitigate the impact of CSOs by generating optimal temporally and spatially varied dynamic control strategies of sewer system actuators. This paper presents a novel MPC based on neural networks for predicting flows, a stormwater management model (SWMM) for flow conveyance, and a genetic algorithm for optimizing the operation of sewer systems and defining the best control strategies. The proposed model was tested on the sewer system of the city of Casablanca in Morocco. The results have shown the efficiency of the developed MPC to reduce CSOs while considering short optimization time thanks to parallel computing.

This paper presents an assessment of the urban water security in a severe water stress area using a semi-quantitative risk-based approach. Water security indicators are selected according to the recommendations of international institutions, the literature review and the opinion of a panel of water experts. Selected indicators cover three fields: water resources, water services and water governance. The field of water resources is described by indicators related to the water resources availability, annual precipitation and the ratio of treated water, while the water services field is described by indicators related to the water service coverage, water losses and the continuity of water supply. Water governance includes three indicators: role and responsibility, access to water information and stakeholder engagement. Water security assessment is conducted in three stages: (i) data collection for five Palestinian cities in the West Bank of Jordan, (ii) determination of the risk score for each indicator using collected data and an expert’s opinion, (iii) determination of the global water security score and water security index using the matrix risk assessment and the wise weight assessment ratio analysis (SWARA) methods. Results show that water risk is ranked as extreme for all cities. Risk related to water resources is a major contributor to global risk, followed by water governance.