Making Water Smart – Virtual Special Issue

The articles in this Virtual Special Issue highlight the breadth of smart water technologies and applications. From the use of classical machine learning and data transformation methods for process improvement, to data management and assimilation in models for better monitoring control, a selection of these articles describe the acquisition and preparation of data as an initial step in its use as part of a digital framework for smart water applications. Importantly, several articles examine the use of smart tools and contemporary AI technologies, such as neural networks and Internet of Things, that demonstrate value in non-conventional or remote environments. This collection demonstrates the innovation possible through the entire ‘pipeline’ of the process of applying smart water techniques, from data measurement and collection, through initial analysis to the application of machine learning and AI techniques and finally through to system deployment. Each of these steps plays an important role in the application of data science and AI techniques to water problems. Collectively, the Virtual Special Issue showcases the innovation required to leverage modern data science and AI approaches in the water sector and collectively point the way towards a future of new measurement techniques, innovative methodologies, and intuitive human interaction to truly ‘Make Water Smart’.

Predicting failures in electronic water taps in rural sub-Saharan African communities: an LSTM-based approach

N. M. Offiong, Y. Wu, F. A. Memon

Water Science and Technology (15 December 2020) 82 (12): 2776–2785.

DOI: https://doi.org/10.2166/wst.2020.542

A comprehensive review of deep learning applications in hydrology and water resources

Muhammed Sit, Bekir Z. Demiray, Zhongrun Xiang, Gregory J. Ewing, Yusuf Sermet, Ibrahim Demir

Water Science and Technology (15 December 2020) 82 (12): 2635–2670.

DOI: https://doi.org/10.2166/wst.2020.369

Fault detection and diagnosis in water resource recovery facilities using incremental PCA

Pezhman Kazemi, Jaume Giralt, Christophe Bengoa, Armin Masoumian, Jean-Philippe Steyer

Water Science and Technology (15 December 2020) 82 (12): 2711–2724.

DOI: https://doi.org/10.2166/wst.2020.368

Multi-objective optimization of energy and greenhouse gas emissions in water pumping and treatment

Iliana Cardenes, Afreen Siddiqi, Mohammad Mortazavi Naeini, Jim W. Hall

Water Science and Technology (15 December 2020) 82 (12): 2745–2760.

DOI: https://doi.org/10.2166/wst.2020.507

Making urban water smart: the SMART-WATER solution

Gerasimos Antzoulatos , Christos Mourtzios, Panagiota Stournara, Ioannis-Omiros Kouloglou, Nikolaos Papadimitriou, Dimitrios Spyrou, Alexandros Mentes, Efstathios Nikolaidis, Anastasios Karakostas, Stefanos Vrochidis, Ioannis Kompatsiaris

Water Science and Technology (15 December 2020) 82 (12): 2691–2710.

DOI: https://doi.org/10.2166/wst.2020.391

A critical review of the data pipeline: how wastewater system operation flows from data to intelligence

Jean-David Therrien, Niels Nicolaï, Peter A. Vanrolleghem

Water Science and Technology (15 December 2020) 82 (12): 2613–2634.

DOI: https://doi.org/10.2166/wst.2020.393

Development of a biogas distribution model for a wastewater treatment plant: a mixed integer linear programming approach

Harry Laing, Chris O’Malley, Anthony Browne, Tony Rutherford, Tony Baines, Mark J. Willis

Water Science and Technology (15 December 2020) 82 (12): 2761–2775.

DOI: https://doi.org/10.2166/wst.2020.363

Data analytics in control and operation of municipal wastewater treatment plants: qualitative analysis of needs and barriers

S. Eerikäinen, H. Haimi , A. Mikola , R. Vahala

Water Science and Technology (15 December 2020) 82 (12): 2681–2690.

DOI: https://doi.org/10.2166/wst.2020.311

Use of real-time sensors for compliance monitoring of nitrate in finished drinking water

Christopher S. Jones , Tianyi Li, Alex Sukalski, Darrin A. Thompson, David M. Cwiertny

Water Science and Technology (15 December 2020) 82 (12): 2725–2736.

DOI: https://doi.org/10.2166/wst.2020.365

Performance improvement of wastewater treatment processes by application of machine learning

O. Icke, D. M. van Es, M. F. de Koning, J. J. G. Wuister, J. Ng, K. M. Phua, Y. K. K. Koh, W. J. Chan, G. Tao

Water Science and Technology (15 December 2020) 82 (12): 2671–2680.

DOI: https://doi.org/10.2166/wst.2020.382

Data integration for infrastructure asset management in small to medium-sized water utilities

N. Carriço, B. Ferreira, R. Barreira, A. Antunes, C. Grueau, A. Mendes, D. Covas, L. Monteiro, J. Santos, I. S. Brito

Water Science and Technology (15 December 2020) 82 (12): 2737–2744.

DOI: https://doi.org/10.2166/wst.2020.377

Data assimilation in hydrodynamic models for system-wide soft sensing and sensor validation for urban drainage tunnels

Rocco Palmitessa, Peter Steen Mikkelsen, Adrian W. K. Law, Morten Borup

Journal of Hydroinformatics (1 May 2021) 23 (3): 438–452.

DOI: https://doi.org/10.2166/hydro.2020.074

A feasibility study of uninhabited aircraft systems for rapid and cost-effective plant stress monitoring at green stormwater infrastructure facilities

Kery Prettyman, Meghna Babbar-Sebens, Christopher E. Parrish, Jeremy Matthew Babbar-Sebens

Journal of Hydroinformatics (1 May 2021) 23 (3): 417–437.

DOI: https://doi.org/10.2166/hydro.2020.195

An ethical decision-making framework with serious gaming: a smart water case study on flooding

Gregory Ewing, Ibrahim Demir

Journal of Hydroinformatics (1 May 2021) 23 (3): 466–482.

DOI: https://doi.org/10.2166/hydro.2021.097

Interactive decision support methodology for near real-time response to failure events in a water distribution network

E. Nikoloudi, M. Romano, F. A. Memon, Z. Kapelan

Journal of Hydroinformatics (1 May 2021) 23 (3): 483–499.

DOI: https://doi.org/10.2166/hydro.2020.101

Capturing high-resolution water demand data in commercial buildings

Peter Melville-Shreeve, Sarah Cotterill, David Butler

Journal of Hydroinformatics (1 May 2021) 23 (3): 402–416.

DOI: https://doi.org/10.2166/hydro.2021.103

Experiments of an IoT-based wireless sensor network for flood monitoring in Colima, Mexico

O.+Mendoza-Cano, R. Aquino-Santos, J. López-de la Cruz, R. M. Edwards, A. Khouakhi, I. Pattison, V. Rangel-Licea, E. Castellanos-Berjan, M. A. Martinez-Preciado, P. Rincón-Avalos, P. Lepper, A. Gutiérrez-Gómez, J. M. Uribe-Ramos, J. Ibarreche, I. Perez

Journal of Hydroinformatics (1 May 2021) 23 (3): 385–401.

DOI: https://doi.org/10.2166/hydro.2021.126

Taking water efficiency to the next level: digital tools to reduce non-revenue water

J. Cassidy, B. Barbosa, M. Damião, P. Ramalho, A. Ganhão, A. Santos, J. Feliciano

Journal of Hydroinformatics (1 May 2021) 23 (3): 453–465.

DOI: https://doi.org/10.2166/hydro.2020.072

Visualisation of the combinatorial effects within evolutionary algorithms: the compass plot

Qi Wang, Miaoting Guan, Wen Huang, Libing Wang, Zhihong Wang, Shuming Liu, Dragan Savić

Journal of Hydroinformatics (1 May 2021) 23 (3): 517–528.

DOI: https://doi.org/10.2166/hydro.2020.073

Control theory-based data assimilation for hydraulic models as a decision support tool for hydropower systems: sequential, multi-metric tuning of the controllers

Miloš Milašinović, Dušan Prodanović, Budo Zindović, Boban Stojanović, Nikola Milivojević

Journal of Hydroinformatics (1 May 2021) 23 (3): 500–516.

DOI: https://doi.org/10.2166/hydro.2021.078

Flood mitigation in coastal urban catchments using real-time stormwater infrastructure control and reinforcement learning

Benjamin D. Bowes, Arash Tavakoli, Cheng Wang, Arsalan Heydarian, Madhur Behl, Peter A. Beling, Jonathan L. Goodall

Journal of Hydroinformatics (1 May 2021) 23 (3): 529–547.

DOI: https://doi.org/10.2166/hydro.2020.080