Dear Colleagues,

Machine learning (ML) is the science of making computers learn and act without explicit instructions and programming but with patterns and inference extracted from data instead. Applied in various science and engineering domains, ML is now pervasive in the field of water engineering. Currently, traditional hydroinformatics methods (regression, classification, and clustering) are being replaced with new ML techniques such as deep neural networks (DNNs), which are mostly accompanied by big data of special features (e.g., unstructured or spatio-temporal) obtained with advances in measurement and sensor technologies.

This Special Issue intends to include papers introducing novel ML approaches for tackling problems in hydro systems, that is, water supply/distribution systems, urban drainage networks, and river systems. This time, we expect to facilitate advanced DNN models such as GAN, R-CNN, YOLO, BERT, etc., which can effectively and efficiently resolve hard and new problems emerging in the water domain.

We hope that this Special Issue can: (1) serve as a reference point from which readers can review progress, recent trends, and emerging issues; and (2) shed light on the future directions of ML studies for water.

Prof. Dr. Joong Hoon Kim
Dr. Donghwi Jung
Guest Editors

Manuscript Submission Information

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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.

• Machine Learning (ML) techniques for water supply/distribution systems, urban drainage networks, and river networks
• Deep Neural Networks (DNNs)
• Generative Adversarial Network (GAN)
• Region-based Convolutional Neural Network (R-CNN)
• You Only Look Once (YOLO)
• Bidirectional Encoder Representations from Transformers (BERT)