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AI, Machine Learning and Digital Twin Applications in Water
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AI, Machine Learning and Digital Twin Applications in Water

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 August 2025 | Viewed by 955

Special Issue Editor

Dr. Bithin Datta

E-Mail Website
Guest Editor
Discipline of Civil Engineering, College of Science & Engineering, James Cook University, Townsville, Australia
Interests: hydrology; groundwater contamination; water resources; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application and integration of Artificial Intelligence (AI), Machine Learning (ML) tools, and Digital Twin (DT) technologies are revolutionizing the wide and complex field of sustainable, long-term water resource management. These tools and technologies are enabling the development and application of efficient and reliable methodologies that are applicable to resource optimization, sustainability, infrastructure resilience, the management of natural disasters, the detection and remediation of contamination, the operation of reservoir systems, and the collection of remote data through smart sensors for optimal real-time resource management. The collection of remote data via UAVs is also able to address various real-world issues such as water table detection, contamination in surface water bodies, the management of aquatic habitats, the management of coastal aquifers, wetlands, efficient and water sensitive irrigation planning, and the prediction and minimization of the impact of tsunamis on surface and subsurface water bodies through AI and ML. Modelling the quality of water at a regional scale without performing costly field measurements or predicting impending droughts and floods represent additional examples. The virtual representation of different components of the water resource system and their integration utilizing smart sensors and automated controls within a Digital Twin (DT) framework also represent advancements in the application of remote sensing, smart sensors, IOT and feedback information. Ensembles of ML-based surrogate models, which are particularly useful in linked simulation and optimization-based decision models, are another rapidly growing area of application.

The development and utilization of innovative digital platforms that incorporate these tools and technologies for different spatial and temporal scales is rapidly gaining momentum. This Special Issue is dedicated to the field of water resource management, including the management of surface and subsurface water, and quantity and quality. It will also serve as a pivotal platform for the dissemination of cutting-edge research and practical applications.

This Special Issue will cover diverse topics related to water management by focusing on both theoretical advancements and real-world deployments that are relevant to digital platforms. This Special Issue will aim to bridge the gap between research and application.

This Special Issue encourges researchers, industry professionals, and policymakers to provide further insights into the challenges and opportunities of adopting these technologies. It will emphasize interdisciplinary collaboration, featuring contributions from hydrologists, data scientists, hydrogeologists, and engineers. Peer-reviewed articles, case studies, and reviews will ensure the dissemination of high-quality, impactful content. By fostering new ideas, dialogue and innovation, this Special Issue aims to catalyze progress in the sustainable and intelligent management of water resources, aligning with global goals to address water security, resiliency, and sustainability.

Dr. Bithin Datta
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

  • artificial intelligence (AI)
  • machine learning (ML)
  • digital twin
  • big data analytics
  • sensor networks
  • ensembles
  • decision models
  • smart sensors
  • digital platform
  • surrogate models
  • water resource management
  • floods and droughts forecasting
  • groundwater systems
  • surface water systems
  • contamination detection
  • linked simulation-optimization

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

18 pages, 2795 KiB  
Article
Multi-Model Collaborative Inversion Method for Natural Gas Pipeline Leakage Sources in Underwater Environments
by Xue Yang, Wei Chen and Zheng Zhang
Water 2025, 17(11), 1562; https://doi.org/10.3390/w17111562 - 22 May 2025
Viewed by 71
Abstract
The identification of leakage sources in underwater natural gas pipelines (UNGPs) remains a critical challenge due to complex environmental conditions. In this study, we propose a novel simulation–optimization method, integrating numerical bubble plume dynamics models with surrogate models to enable accurate leakage parameter [...] Read more.
The identification of leakage sources in underwater natural gas pipelines (UNGPs) remains a critical challenge due to complex environmental conditions. In this study, we propose a novel simulation–optimization method, integrating numerical bubble plume dynamics models with surrogate models to enable accurate leakage parameter inversion. First, a bubble plume underwater motion simulation model was developed based on the actual conditions of the study area to predict the future spatial and temporal variation characteristics of the bubble plumes in certain wave fields. Then, the simulation–optimization method was applied to determine the leakage velocity and offset distance of the underwater gas pipeline leakage source via inversion. To reduce the computational load of the optimization model by repeatedly invoking the simulation model, the Kriging method and a backpropagation (BP) neural network were used to build surrogate models for the numerical model. Finally, the optimized surrogate model was solved using the simulated annealing method, and the inverse identification results were obtained. The experimental results show that both methods can achieve a high inversion accuracy. The relative error of the Kriging model is no more than 12%, and the running time is 13 min. Meanwhile, based on the BP neural network surrogate model, the relative error of the BP neural network model is about 14%, and the running time is 2.5 min. Full article
(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water)
15 pages, 2384 KiB  
Article
A Dissolved Oxygen Prediction Model for the Yangtze River Basin Based on VMD-IFOA-Attention-GRU
by Zhengyu Zhu and Shouqi Cao
Water 2025, 17(9), 1278; https://doi.org/10.3390/w17091278 - 25 Apr 2025
Viewed by 211
Abstract
Water ecological security is one of the key directions of current environmental protection. With the acceleration of urbanization and industrialization, the Shanghai region of the Yangtze River Basin faces various aquatic ecological issues, such as eutrophication and declining benthic biodiversity. Dissolved oxygen (DO), [...] Read more.
Water ecological security is one of the key directions of current environmental protection. With the acceleration of urbanization and industrialization, the Shanghai region of the Yangtze River Basin faces various aquatic ecological issues, such as eutrophication and declining benthic biodiversity. Dissolved oxygen (DO), as a critical indicator for measuring water self-purification capacity and ecological health status, has been widely applied in water quality monitoring and early warning systems. Therefore, accurate prediction of dissolved oxygen concentration is of significant importance for the ecological and environmental protection of river basins. This study introduces a hybrid prediction model combining Variational Mode Decomposition (VMD), Improved Fruit Fly Optimization Algorithm (IFOA), and Attention-based Gated Recurrent Unit (Attention-GRU). The model first decomposes preprocessed dissolved oxygen data through VMD to extract multiple intrinsic mode functions, reducing non-stationarity and high-frequency noise interference. It then utilizes the Improved Fruit Fly Optimization Algorithm to adaptively optimize key parameters of the Attention-GRU network, enhancing the model’s fitting capability. Experiments demonstrate that the VMD-IFOA-Attention-GRU model achieves 0.286, 0.302, and 0.915 for Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R2), respectively, significantly outperforming other comparative models. The results indicate that this method can provide a reference for intelligent water quality prediction in typical regions such as the Yangtze River Basin. Full article
(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water)
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18 pages, 6691 KiB  
Article
Predicting Surface Stokes Drift with Deep Learning
by Xiaoyu Yu, Daling Li Yi and Peng Wang
Water 2025, 17(7), 983; https://doi.org/10.3390/w17070983 - 27 Mar 2025
Viewed by 440
Abstract
Stokes drift refers to the net horizontal displacement of water particles under the influence of wave action, playing a crucial role in the transport of heat, salt, nutrients, and pollutants in the ocean. Accurate estimation of Stokes drift is essential for understanding ocean [...] Read more.
Stokes drift refers to the net horizontal displacement of water particles under the influence of wave action, playing a crucial role in the transport of heat, salt, nutrients, and pollutants in the ocean. Accurate estimation of Stokes drift is essential for understanding ocean dynamics and material transport. This study utilizes two deep learning models (Earthformer and ConvLSTM) to predict surface Stokes drift, using wind and water depth as input variables. We designed three control experiments to evaluate the impact of different training objectives on the experimental results. In Exp. 1, the model used the two Stokes drift components (us, vs) as the training objectives. In Exp. 2, the objectives were the two components (us, vs) plus the direction θ. In Exp. 3, the model employed the magnitude |us| and the direction θ of the Stokes drift as the training objectives. The results indicate that using the magnitude and direction (Exp. 3) significantly reduces the RMSE for magnitude, direction, and each component (us, vs) by up to 33.3%, compared to the other two strategies. Moreover, the approach of choosing magnitude and direction as the training objectives can also be applied to the prediction of other vector variables, such as ocean currents and winds. Full article
(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water)
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