WRR special section on big data and machine learning in water sciences

[Chaopeng Shen]

Dear colleagues,

Water Resources Research is inviting submissions to a special section on big data and machine learning in water sciences. Please consider making a submission. Thanks. 

https://agupubs.onlinelibrary.wiley.com/hub/journal/19447973/features/call-for-papers

Call for Papers for "Big Data & Machine Learning in Water Sciences: Recent Progress and Their Use in Advancing Science"

Submission Open Date: 1 April 2018
Submission Deadline: 30 November 2018

Special Section Organizers:
Chaopeng Shen
Amin Elshorbagy
Hoshin Gupta
Grey Nearing

Various forms of “machine learning” have historically played a valuable role in the prediction of hydrologic events. With the increasing availability of “big data” relevant to the hydrological sciences, and with the rapid advances being made in machine learning and informatics, we now see increasing opportunities for novel methods to aid in both scientific discovery and predictive capability. Beyond the commonly used kinds of hydrologic data (precipitation, streamflow, and groundwater levels, etc.), many novel data sources and technologies have recently become available, including those from satellite-based sensors, embedded sensor networks, ecological networks, drones and internet-based social networks.

This special issue invites contributions that demonstrate the use of “big data” and “machine learning” methods to advance the hydrological sciences, water resources management, and disciplines that interface with water. We particularly invite submissions that a) critically customize and improve machine learning, b) integrate machine learning with process-based models to enhance physical understanding, predictions and decision making, c) utilize machine learning algorithms to produce critically important datasets, d) interpret machine learning research to gain scientific insights, and e) investigate the connections between water and other physical/human systems via a data-driven approach. Applications and/or extensions of deep learning in the water-related sciences are also especially welcome.

Submissions are solicited on all fields related to water. For more information, please contact the special issue organizers with a brief but clear description of the scientific problem and the novel advance under development, plus an indication of data sources and machine learning techniques employed. Submissions to this special issue will go through the same rigorous peer review process as all papers submitted to WRR and must contain novel scientific insights or understanding about the advances. Manuscripts should be submitted through the GEMS website. 

For additional information, please contact: w...@agu.org.

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