Performance of LEMMO with artificial neural networks for water systems optimisation

Optimisation algorithms could potentially provide extremely valuable guidance towards improved intervention strategies and/or designs for water systems. The application of these algorithms in this domain has historically been hindered by the extreme computational cost of performing hydraulic modelling of water systems. This is because running an optimisation algorithm generally involves running a very large number of simulations of the system being optimised. In this paper, a novel optimisation approach is described, based upon the ‘learning evolution model for multi-objective optimisation’ algorithm. This approach uses deep learning artificial neural network meta-models to reduce the number of simulations of the water system required, without reducing the accuracy of the optimisation results. This is then compared to an industry standard optimisation approach, showing results with increased speed of convergence and equivalent or improved accuracy. Therefore, demonstrating that this approach is suitable for use in highly computationally demanding areas such as water systems optimisation.Accepted Author ManuscriptSanitary Engineerin

Real-time foul sewer hydraulic modelling driven by water consumption data from water distribution systems

Real-time hydraulic modelling can be used to address a wide range of issues in a foul sewer system and hence can help improve its daily operation and maintenance. However, the current bottleneck within real-time FSS modelling is the lack of spatio-temporal inflow data. To address the problem, this paper proposes a new method to develop real-time FSS models driven by water consumption data from associated water distribution systems (WDSs) as they often have a proportionally larger number of sensors. Within the proposed method, the relationship between FSS manholes and WDS water consumption nodes are determined based on their underlying physical connections. An optimization approach is subsequently proposed to identify the transfer factor k between nodal water consumption and FSS manhole inflows based on historical observations. These identified k values combined with the acquired real-time nodal water consumption data drive the FSS real-time modelling. The proposed method is applied to two real FSSs. The results obtained show that it can produce simulated sewer flows and manhole water depths matching well with observations at the monitoring locations. The proposed method achieved high R2, NSE and KGE (Kling-Gupta efficiency) values of 0.99, 0.88 and 0.92 respectively. It is anticipated that real-time models developed by the proposed method can be used for improved FSS management and operation.Accepted Author ManuscriptSanitary Engineerin

An Effective and Efficient Method for Identification of Contamination Sources in Water Distribution Systems Based on Manual Grab-Sampling

Most of the contamination source localization methods for water distribution systems (WDSs) assume the availability of accurate water quality models and multi-parameter online sensors, which are often out of reach of many water utilities. To address this, a novel manual grab-sampling method (MGSM) is developed to effectively and efficiently locate continuous contamination sources in a WDS using a dynamic and cyclical sampling strategy. The grab samples are collected at a pre-specified number of hydrants by the corresponding teams followed by laboratory tests. The MGSM optimizes the sampling plan at each cycle by making the probability of contamination source(s) in each sub-network as equal as possible, where sub-networks are determined by the selected hydrants and current flow pipe directions. The CS's size is reduced at each cycle by exploiting sample testing results obtained in the previous cycle until there are no further hydrants to sample from. Two real-world WDSs are used to demonstrate the effectiveness of the proposed MGSM. The results obtained show that the MGSM can significantly reduce the spatial range of the CS (to about 5% of the entire WDS) for a range of scenarios including multiple contamination sources and pipe flow direction changes. We found that an optimal number of sampling teams exists for a given WDS, representing a balanced trade-off between detection efficiency and sampling/testing budgets. Due to its relative simplicity, the proposed MGSM can be used in engineering practice straightaway and it represents a viable alternative to the methods associated with water quality models and sensors.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Sanitary Engineerin

Hydroinformatics education-the Water Informatics in Science and Engineering (WISE) Centre for Doctoral Training

The Water Informatics in Science and Engineering Centre for Doctoral Training (WISE CDT) offers a postgraduate programme that fosters enhanced levels of innovation and collaboration by training a cohort of engineers and scientists at the boundary of water informatics, science and engineering. The WISE CDT was established in 2014 with funding from the UK Engineering and Physical Sciences Research Council (EPSRC) amongst the universities of Bath, Bristol, Cardiff and Exeter. The WISE CDT will ultimately graduate over 80 PhD candidates trained in a non-traditional 4-year UK doctoral programme that integrates teaching and research elements in close collaboration with a range of industrial partners. WISE focuses on cohort-based education and equips the PhD candidates with a wide range of skills developed through workshops and other activities to maximise candidate abilities and experiences. We discuss the need for, the structure and results of the WISE CDT, which has been ongoing from 2013-2022 (final year of graduation). We conclude with lessons learned and an outlook for PhD training, based on our experience with this programme.Sanitary Engineerin

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing-based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.Water Resource