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New World: Advancing Water Applications Through Machine Learning and Artificial Intelligence

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: 15 October 2025 | Viewed by 278

Special Issue Editor

Prof. Dr. Jianjun Ni

E-Mail Website
Guest Editor
College of IOT Engineering, Hohai University, Changzhou, China
Interests: artificial intelligence; smart water; internet of things; machine learning; automation; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are excited to invite submissions to a Special Issue titled “New World: Advancing Water Applications Through Machine Learning and Artificial Intelligence”. This Special Issue focuses on the transformative potential of machine learning (ML) and artificial intelligence (AI) technologies in addressing complex water-related challenges. It seeks original research and case studies demonstrating how ML and AI can enhance water-related fields, including the development of predictive models for the supply and demand of water, the application of deep learning for real-time water quality assessments, and the use of AI in smart water networks for efficient resource allocation. This Special Issue serves as a platform for researchers, practitioners, and policymakers to share insights and will foster collaboration to advance the sustainable management of water resources through AI and ML innovations‌. We encourage the submission of original research articles, reviews, and case studies that demonstrate how the application of machine learning and artificial intelligence technologies can solve complex water-related problems.

Prof. Dr. Jianjun Ni
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

  • machine learning
  • artificial intelligence
  • smart water
  • hydrological monitoring
  • flood forecasting
  • water quality as-sessment
  • remote sensing

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

Published Papers (1 paper)

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Research

21 pages, 5418 KiB  
Article
BloomSense: Integrating Automated Buoy Systems and AI to Monitor and Predict Harmful Algal Blooms
by Waheed Ul Asar Rathore, Jianjun Ni, Chunyan Ke and Yingjuan Xie
Water 2025, 17(11), 1691; https://doi.org/10.3390/w17111691 - 3 Jun 2025
Viewed by 151
Abstract
Algal blooms pose significant risks to public health and aquatic ecosystems, highlighting the need for real-time water quality monitoring. Traditional manual methods are often limited by delays in data collection, which can hinder timely response and effective management. This study proposes a solution [...] Read more.
Algal blooms pose significant risks to public health and aquatic ecosystems, highlighting the need for real-time water quality monitoring. Traditional manual methods are often limited by delays in data collection, which can hinder timely response and effective management. This study proposes a solution by integrating automated monitoring systems (AMSs) with advanced machine learning (ML) techniques to predict chlorophyll-a (Chl−a) concentrations. Utilizing low-cost and readily available input variables, we developed energy-efficient ML algorithms optimized for deployment on buoys with a battery and hardware resources. The AMS employs preprocessing methods like the SMOTE and Random Forest (RF) for feature selection and ranking. Deep feature extraction is performed through a ResNet-18 model, while temporal dependencies are captured using a Long Short-Term Memory (LSTM) network. A Softmax output layer then predicts Chl−a concentrations. An alert system is incorporated to warn when Chl−a levels exceed 10 μg/L, signaling potential bloom conditions. The results show that this approach offers a rapid, cost-effective, and scalable solution for real-time water quality monitoring, enhancing manual sampling efforts and improving management of water bodies at risk. Full article
(This article belongs to the Special Issue New World: Advancing Water Applications Through Machine Learning and Artificial Intelligence)
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