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Machine Learning Applications in the Water Domain

This special issue belongs to the section “New Sensors, New Technologies and Machine Learning in Water Sciences“.

Special Issue Information

Dear Colleagues,

Water is an indispensable resource for life, and its management and monitoring are crucial for sustainable development. The advent of Machine Learning (ML) has revolutionized the way we analyze and predict hydrological phenomena and water quality. This Special Issue aims to showcase the latest advancements in the application of ML in the field of water, particularly focusing on the prediction of hydrological processes and water quality, as well as the application of machine vision in water bodies. We invite researchers to submit their latest findings on the following topics:

  1. Applications of machine learning models in time series predictions of hydrology and water quality;
  2. Applications of and key issues in machine learning in water body identification and classification;
  3. Comparative analysis of different machine learning models;
  4. Research on the coupling of knowledge and data;
  5. Related research on the interpretability, performance evaluation, and other aspects of machine learning.

Dr. Yonggui 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 250 words) can be sent to the Editorial Office for assessment.

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 resource
  • machine learning (ML)
  • neural network
  • hydrological phenomena
  • water quality
  • time series prediction
  • water body identification
  • classification
  • comparative analysis
  • interpretability of ML

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Water - ISSN 2073-4441