Special Issue "Using Artificial Intelligence for Smart Water Management"

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: 31 January 2022.

Special Issue Editors

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
Prof. Dr. Zhenxiang Xing
E-Mail Website
Guest Editor
School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin, China
Interests: hydrological simulation and water resources analysis

Special Issue Information

Dear Colleagues,

With the rapid development of social economy and the wide application of information technology, the construction of intelligent water resources has become an effective carrier for many countries to improve the scientific management of water resources. The basic idea of smart water management is to improve the effectiveness and efficiency of flood monitoring, water environment monitoring and management, water resources management and allocation, water supply and drainage pipe network monitoring and other related aspects, using artificial intelligence technology and other new information technologies. Enhancing the ability of informationization and intelligence is one of the key directions of water management now and in the future. This Special Issue is focused on providing state-of-the-art understanding and applications of artificial intelligence theory and new information technologies for water management.

Prof. Dr. Jianjun Ni
Prof. Dr. Zhenxiang Xing
Guest Editors

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 papers will be 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 2000 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

  • intelligent models about water management
  • automated monitoring technology
  • big data on water management
  • network technology for water management
  • flood forecasting
  • water environment monitoring
  • monitoring of water supply and drainage network
  • deep learning technology of smart water

Published Papers (1 paper)

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Research

Article
Underwater Biological Detection Algorithm Based on Improved Faster-RCNN
Water 2021, 13(17), 2420; https://doi.org/10.3390/w13172420 - 03 Sep 2021
Viewed by 615
Abstract
Underwater organisms are an important part of the underwater ecological environment. More and more attention has been paid to the perception of underwater ecological environment by intelligent means, such as machine vision. However, many objective reasons affect the accuracy of underwater biological detection, [...] Read more.
Underwater organisms are an important part of the underwater ecological environment. More and more attention has been paid to the perception of underwater ecological environment by intelligent means, such as machine vision. However, many objective reasons affect the accuracy of underwater biological detection, such as the low-quality image, different sizes or shapes, and overlapping or occlusion of underwater organisms. Therefore, this paper proposes an underwater biological detection algorithm based on improved Faster-RCNN. Firstly, the ResNet is used as the backbone feature extraction network of Faster-RCNN. Then, BiFPN (Bidirectional Feature Pyramid Network) is used to build a ResNet–BiFPN structure which can improve the capability of feature extraction and multi-scale feature fusion. Additionally, EIoU (Effective IoU) is used to replace IoU to reduce the proportion of redundant bounding boxes in the training data. Moreover, K-means++ clustering is used to generate more suitable anchor boxes to improve detection accuracy. Finally, the experimental results show that the detection accuracy of underwater biological detection algorithm based on improved Faster-RCNN on URPC2018 dataset is improved to 88.94%, which is 8.26% higher than Faster-RCNN. The results fully prove the effectiveness of the proposed algorithm. Full article
(This article belongs to the Special Issue Using Artificial Intelligence for Smart Water Management)
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