Machine Learning and Automation in Remote Sensing Applied in Hydrological Processes

Dear Colleagues,

As a result of the constantly growing computational power that researchers have access to, and the constant development of AI tools and techniques, there is an increasing demand to automate and develop machine learning tools to study geo-spatial phenomena in a better, faster manner. By including more data, parameters, and different types of remote sensing imagery, studies can be more representative and results can be more easily compared and brought into a “bigger picture” in order to more appropriately provide an assessment of the impact of different hydrological processes and phenomena, such as droughts, flash floods, water balance, soil moisture, evapotranspiration, infiltration, coastal erosion and other water-related topics.

Considering the fact that the changes in hydrological processes and phenomena are more frequent, and manifest significantly faster, in the context of climate change, automated tools and analyses can significantly aid in the rapid decision making prior to (or during) such cases, or even for disaster management and impact mitigation after extreme hydrological events.

The aim of this Special Issue is to provide state-of-the-art knowledge in the field of remote sensing for hydrological processes through the means of machine learning, neural networks, deep learning, artificial intelligence, automation techniques, etc., and promote new approaches and techniques in the field.

This Special Issue addresses (but is not limited to) the following topics:

• Machine learning for hydrological processes;
• Neural networks applied in water-related topics;
• Methodological studies;
• Remote sensing applied in hydrology;
• Automation techniques;
• Tools developed in GIS software (such as ArcGIS, QGIS, Snap, etc.);
• Google Earth Engine;
• Drought analyses;
• Flood risk analyses;
• Automated and semi-automated classifications;
• Artificial intelligence in water studies;
• Water budget analyses;
• Deep learning applications in hydrology;
• Morphometric studies;
• WebGIS platforms for online automation, etc.

Dr. Andrei Enea
Dr. Cristian Constantin Stoleriu
Dr. Marina Iosub
Dr. Fiachra O’Loughlin
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 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.

• automation
• artificial intelligence
• flood risk automation
• GIS tools
• Google Earth Engine
• hydrological processes
• machine learning
• neural networks
• remote sensing automation