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Numerical and Experimental Methods, Data Analyses, Digital Twin, IoT Machine Learning and AI in Water Sciences

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: 20 January 2026 | Viewed by 528

Special Issue Editor

Prof. Dr. Junye Wang

E-Mail Website
Guest Editor
Faculty of Science and Technology, Athabasca University, Athabasca, AB T9S 3A3, Canada
Interests: multidisciplinary modelling; watershed modelling; ecosystem modelling; renewable energy; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water is an important natural resource for our life. However, water scarcity influences more than 40% of the world's population. Many disasters resulting from water account for 70% of all deaths related to natural disasters. Therefore, the traditional approaches are insufficient to understand a river, lake, ocean, and groundwater system in terms of sustainable development and water resource management.

Advanced numerical and experimental methods, such as Data Analyses, Digital Twin, IoT Machine Learning, and AI, are essential for unraveling the mechanisms underlying various water resources and water processes to understand the complex interactions between water processes, such as soil erosion, nutrient cycles, water resources, water quality, biodiversity, climate, soil, and environmental sustainability.

This Special Issue of Water invites innovative scientific contributions to delve into these mechanisms and explore the latest research in this field, including experimental and computational approaches, modelling, simulation, integration, testing, monitoring, data analyses, digital twin, IoT machine learning, and AI, and the development of novel techniques for studying water processes and soil–water–air–plant interactions.

We invite contributions that address these and other challenges with a focus on water science from local, regional, or global perspectives.

Prof. Dr. Junye Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 2600 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.

Keywords

  • water resources
  • water quality
  • biodiversity
  • modelling
  • simulation
  • integration
  • monitoring
  • data analyses
  • digital twin
  • IoT machine learning and AI

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Published Papers (1 paper)

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Research

20 pages, 2071 KiB  
Article
Leakage Break Diagnosis for Water Distribution Network Using LSTM-FCN Neural Network Based on High-Frequency Pressure Data
by Sen Peng, Hongyan Zeng, Xingqi Wu and Guolei Zheng
Water 2025, 17(12), 1823; https://doi.org/10.3390/w17121823 - 18 Jun 2025
Viewed by 297
Abstract
Water distribution is no arguably the most important factor in modern times, and water leak breaks are typically a consequence of failures in water distribution networks. But pipeline leakage breaks have become one of the most frequent consequences affecting the operation of water [...] Read more.
Water distribution is no arguably the most important factor in modern times, and water leak breaks are typically a consequence of failures in water distribution networks. But pipeline leakage breaks have become one of the most frequent consequences affecting the operation of water distribution networks (WDNs) and monitoring their health is often complicated. This paper proposes a leakage break diagnosis method based on an LSTM-FCN neural network model from high-frequency pressure data. Data preprocessing is used to avoid the influence of noise and information redundancy, and the LSTM module and the FCN module are used to extract and concatenate different leakage break features. The leakage break feature is sent to a dense classifier to obtain the predicted result. Two sample sets, steady state and water consumption, were obtained to verify the performance of the proposed leakage break diagnosis method. Three other models, LSTM, FCN, and ANN, were compared using the sample sets. The proposed LSTM-FCN model achieved an overall accuracy of 85% for leakage break detection, illustrating that the model could effectively learn the leakage break features in high-frequency time-series data and had a high accuracy for leakage break detection and leakage break degree prediction of new samples in WDNs. Meanwhile, the proposed method also had good adaptability to the variations in water consumption in actual WDNs. Full article
(This article belongs to the Special Issue Numerical and Experimental Methods, Data Analyses, Digital Twin, IoT Machine Learning and AI in Water Sciences)
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