Special Issue "Monitoring and Self-Learning Control of Water Systems"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Resources Management, Policy and Governance".

Deadline for manuscript submissions: closed (10 January 2023) | Viewed by 1306

Special Issue Editors

Farm Technology Group, Wageningen University & Research, Wageningen, The Netherlands
Interests: water resource management; water-energy-food nexus; reinforcement learning; autonomous agricultural production; multi-agent agricultural robotic systems
Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Barcelona, Spain
Interests: planning and control of autonomous systems; supervision and advanced control of processes and systems; control of large scale systems
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Special Issue Information

Dear Colleagues,

The control of water systems (e.g., water distribution, irrigation systems) has become an important research topic because of the significance of water for human beings. The optimization of water systems involves complex mathematical equations for hydraulic and hydrology processes, multiple-input/multiple-output characteristics, as well as the possible, additive of parametric uncertainties. Therefore, identify the systems, model the dynamics, diagnose functionality to improve system performance (e.g., optimal water usage, efficient operational cost) is crucially important and challenging.

Due to the development of sensing and communication techniques, a vast amount of data are available which leads to groundbreaking advances in both system identification and automatic control. Self-learning control is an advanced control method which can update current control model through incorporating new data during iterative process under specific objectives. Self-learning control includes but not limited to reinforcement learning, model predictive control (MPC), and data-based MPC. The learning ability and reconfiguration strategy make self-learning control be able to operate water systems autonomously with accuracy and robustness.

This Special Issue aims to gather innovative technologies, methods and practices related to system monitoring (sensing systems of IoT, cloud computing and proximal/remote sensors) and optimal control (RL, MPC, data-based, learning-based) of water systems to overcome the challenges of global water scarcity and climate change. Research and review articles, theoretical or practical works will all be considered as valuable contributions to this Special Issue.

Dr. Congcong Sun
Prof. Dr. Vicenç Puig
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • water distribution system
  • smart irrigation system
  • digital technologies
  • remote and proximal sensing
  • MPC
  • reinforcement learning
  • data-based
  • system identification
  • optimization and automatic control

Published Papers (1 paper)

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Real-Time Leak Diagnosis in Water Distribution Systems Based on a Bank of Observers and a Genetic Algorithm
Water 2022, 14(20), 3289; https://doi.org/10.3390/w14203289 - 18 Oct 2022
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The main contribution of this paper is to present a novel solution for the leak diagnosis problem in branched pipeline systems considering the availability of pressure head and flow rate sensors on the upstream (unobstructed) side and the downstream (constricted) side. This approach [...] Read more.
The main contribution of this paper is to present a novel solution for the leak diagnosis problem in branched pipeline systems considering the availability of pressure head and flow rate sensors on the upstream (unobstructed) side and the downstream (constricted) side. This approach is based on a bank of Kalman filters as state observers designed on the basis of the classical water hammer equations and a related genetic algorithm (GA) which includes a fitness function based on an integral error that helps obtaining a good estimation despite the presence of noise. For solving the leak diagnosis problem, three stages are considered: (a) the leak detection is performed through a mass balance; (b) the region where the leak is occurring is identified by implementing a reduced bank of Kalman filters which localize the leak by sweeping all regions of the branching pipeline through a GA that reduces the computational effort; (c) the leak position is computed through an algebraic equation derived from the water hammer equations in steady-state. To assess this methodology, experimental results are presented by using a test bed built at the Tuxtla Gutiérrez Institute of Technology, Tecnológico Nacional de México (TecNM). The obtained results are then compared with those obtained using a classic extended Kalman filter which is widely used in solving leak diagnosis problems and it is highlighted that the GA approach outperforms the EKF in two cases whereas the EKF is better in one case. Full article
(This article belongs to the Special Issue Monitoring and Self-Learning Control of Water Systems)
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