Drought Forecasting: Application of Ensemble and Advanced Machine Learning Approaches-Reference-Cited by-同舟云学术

Drought Forecasting: Application of Ensemble and Advanced Machine Learning Approaches

Published:2023 Issue: Volume:11 Page:141375-141393
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Hukkeri Geetabai S.¹^ORCID,Naganna Sujay Raghavendra²^ORCID,Pruthviraja Dayananda³^ORCID,Bhat Nagaraj⁴,Goudar R. H.⁵^ORCID

Affiliation:

1. Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India

2. Department of Civil Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India

3. Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, India

4. Department of Computer Science and Engineering, Shri Madhwa Vadiraja Institute of Technology and Management, Bantakal, Udupi, India

5. Department of Computer Science and Engineering, Visvesvaraya Technological University, Belagavi, Karnataka, India

Funder

Manipal Academy of Higher Education

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10355928.pdf?arnumber=10355928

Reference56 articles.

1. A review of drought concepts

2. World drought frequency, duration, and severity for 1951-2010

3. Measuring economic impacts of drought: a review and discussion

4. ANFIS-based soft computing models for forecasting effective drought index over an arid region of India

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