American Journal of Water Science and Engineering

Special Issue

Machine Learning for Enhancing Water Science and Engineering

  • Submission Deadline: Mar. 31, 2022
  • Status: Submission Closed
  • Lead Guest Editor: Shailesh Kumar Singh
About This Special Issue
Computation power is not a barrier any more for any discipline of science. Water science is highly nonlinear, complex and depend on various factors in coherent ways. Another major issue of water science is data limitations and various uncertainties in modelling and decision making. To understand and model highly non-linear complex hydroclimatological process, data driven algorithms have gained much interest in water resources science and engineering. Machine learning algorithms have become prominent in water science and engineering in terms of prediction, forecasting, management, adaptation, decision and policy making. Machine learning is also helping solving many problems of water resonances management. Integration of machine learning models with process-based hydrological and climatological models has proven better predictions and forecasting of hydroclimatological extremes. Machine Learning models integrated with data assimilation methods leading to hybrid data driven models have proven to be more promising for limited data, ungauged basins, unprecedented extremes, etc. In this special issue we are looking for contribution related to application of machine learning for enhancing water science and engineering.
The scope of this special issue includes (but is not limited to the) application of Machine Learning in the following domains of water science and engineering:
Hydrology
Drought
Floods
Reservoir Operation
Irrigation
Water Quality
Climate Change

Keywords:

  1. Machine Learning
  2. Deep Learning
  3. Water Science
  4. Water Resource
  5. Climate Change
Lead Guest Editor
  • Shailesh Kumar Singh

    Hydrological Process, National Institute of Water and Atmospheric Research, Christchurch, New Zealand

Guest Editors
  • Shaik Rehana

    Lab for Spatial Informatics, International Institute of Information Technology, Gachibowli, Hyderabad, India