Machine Learning for Hydro-Systems

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 networks. We especially expect to facilitate new DNN models which can effectively and efficiently resolve problems and issues in the domain with unstructured water data. Studies on spatio-temporal hydrological and water demand data processing would be also welcome if an ML technique is used.

We hope 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 right future directions of ML studies for water.

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

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