Confident learning-based Gaussian mixture model for leakage detection in water distribution networks.

Leakage detection in the water distribution system not only helps to reduce water waste but also decreases the risk of drinking water pollution. To reduce reliance on hardware devices and enable real-time detection, the water utilities are transitioning towards the data-driven based approach that relies on the analysis of the flow and pressure data collected from the supervisory control and data acquisition (SCADA) system. Due to the lack of leakage data, most of these methods are unsupervised methods that rely heavily on assumptions about the distribution of anomalies; whereas, the water utility's repair records contain much valid information about the leakage and normal characteristics.

Aeration strategy based on numerical modelling and the response mechanism of microbial communities under various operating conditions.

Aeration system is the main energy consumer in a wastewater treatment process. In this paper, the Naive Bayes classification (NBC) algorithm and response surface method (RSM) were firstly used to establish a methodology to improve the aeration efficiency and estimate effluent quality. Lab-scale experiments were conducted to verify the model.

The optimization of Low Impact Development placement considering life cycle cost using Genetic Algorithm.

Low Impact Development (LID) is an effective measure in controlling the urban runoff and mitigating the non-point source pollution. The determination of LID facilities and layouts for a sub-catchment is important for designing stormwater management system. However, there are remain large uncertainty and challenge exist in determination of LID facilities when consider budget, land use, soil surface and groundwater as well as local climate etc.