An automated machine learning methodology for the improved prediction of reference evapotranspiration using minimal input parameters-Reference-Cited by-同舟云学术

An automated machine learning methodology for the improved prediction of reference evapotranspiration using minimal input parameters

Published:2022-05 Issue:5 Volume:36 Page:
ISSN:0885-6087
Container-title:Hydrological Processes
language:en
Short-container-title:Hydrological Processes

Author:

Mangalath Ravindran Sowmya¹^ORCID,Moorakkal Bhaskaran Santosh Kumar²^ORCID,K. Ambat Sooraj³^ORCID,Balakrishnan Kannan¹^ORCID,Manguttathil Gopalakrishnan Manoj⁴^ORCID

Affiliation:

1. Department of Computer Applications Cochin University of Science and Technology Kochi Kerala India

2. Division of Information Technology, School of Engineering Cochin University of Science and Technology Kochi Kerala India

3. Naval Physical and Oceanographic Laboratory Defence R&D Organization Kochi Kerala India

4. Advanced Centre for Atmospheric Radar Research Cochin University of Science and Technology Kochi Kerala India

Publisher

Wiley

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.14571

Reference65 articles.

1. Review: Theory-guided machine learning applied to hydrogeology—state of the art, opportunities and future challenges

2. Crop evapotranspiration‐guidelines for computing crop water requirements‐FAO irrigation and drainage paper 56;Allen R. G.;FAO, United Nations, Rome,1998

3. Toward Urban Water Security: Broadening the Use of Machine Learning Methods for Mitigating Urban Water Hazards

4. Pre-monsoon convective events and thermodynamic features of southwest monsoon onset over Kerala, India – A case study

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