Acta Scientific Computer Sciences

Review Article Volume 5 Issue 6

Wetland with Emerging Technology: Indian Perspective

Shweta Vikram*

Assistant Professor, Era University, Lucknow, India

*Corresponding Author: Shweta Vikram, Assistant Professor, Era University, Lucknow, India.

Received: March 19, 2023; Published: May 20, 2023

Abstract

Recently, the state government and union territorial administrations across India celebrated World Wetlands Day (WWD) at all 75 Ramsar sites on February 2, 2023. The theme for World Wetlands Day is "Wetland Restoration," highlighting the urgent need to prioritize wetland restoration. Wetlands are among the most beneficial environments in the world, comparable to rainforests and coral reefs. Wetland is important for increasing the water level of the land and increasing the wildlife habitat. Wetlands are an imperative source of nourishment, crude materials, hereditary assets for medications, and hydropower.

Keywords: Ramsar Sites; Water Bodies; Artificial Intelligence; Machine Learning; Graphical Information System (GIS); and Support Vector Machine (SVM)

References

  1. https://www.ramsar.org
  2. https://indianwetlands.in
  3. https://www.epa.gov
  4. Granata Francesco Rudy Gargano and Giovanni de Marinis. "Artificial intelligence based approaches to evaluate actual evapotranspiration in wetlands”. Science of The Total Environment 703 (2020): 135653.
  5. Khatun Rumki., et al. "Integrating remote sensing with swarm intelligence and artificial intelligence for modeling wetland habitat vulnerability in pursuance of damming”. Ecological Informatics 64 (2021): 101349.
  6. Lin Ping., et al. "Artificial intelligence classification of wetland vegetation morphology based on deep convolutional neural network”. Natural Resource Modeling1 (2020): e12248.
  7. Kiiza Christopher., et al. "Predicting pollutant removal in constructed wetlands using artificial neural networks (ANNs)”. Water Science and Engineering1 (2020): 14-23.
  8. Choi Changhyun., et al. "Development of water level prediction models using machine learning in wetlands: A case study of Upo wetland in South Korea”. Water1 (2019): 93.
  9. Nguyen Xuan Cuong., et al. "Developing a new approach for design support of subsurface constructed wetland using machine learning algorithms”. Journal of Environmental Management 301 (2022): 113868.
  10. Gupta Pankaj Kumar., et al. "Machine learning and artificial intelligence application in constructed wetlands for industrial effluent treatment: advances and challenges in assessment and bioremediation modeling”. Bioremediation for Environmental Sustainability (2021): 403-414.
  11. Islam Abu Reza Md., et al. "Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh”. Environmental Science and Pollution Research26 (2021): 34450-34471.
  12. Rapinel Sébastien., et al. "National wetland mapping using remote-sensing-derived environmental variables, archive field data, and artificial intelligence”. Heliyon2 (2023).
  13. Seo Jiyu., et al. “Wetland inflow simulation using artificial intelligence prediction model based on classification for water surface area identification”. No. EGU23-4884. Copernicus Meetings, (2023).
  14. Soti Abhishek., et al. "Assessment of removal rate coefficient in vertical flow constructed wetland employing machine learning for low organic loaded systems”. Bioresource Technology 376 (2023): 128909.
  15. Piaser Erika and Paolo Villa. "Evaluating capabilities of machine learning algorithms for aquatic vegetation classification in temperate wetlands using multi-temporal Sentinel-2 data”. International Journal of Applied Earth Observation and Geoinformation 117 (2023): 103202.
  16. Singha Pankaj and Swades Pal. "Wetland transformation and its impact on the livelihood of the fishing community in a flood plain river basin of India”. Science of The Total Environment 858 (2023): 159547.
  17. Singha Pankaj and Swades Pal. "Predicting wetland area and water depth in Barind plain of India”. Environmental Science and Pollution Research47 (2022): 70933-70949.
  18. Abbasi Tasneem Chirchom Luithui and Shahid Abbas Abbasi. "Modelling methane and nitrous oxide emissions from rice paddy wetlands in India using Artificial Neural Networks (ANNs)”. Water10 (2019): 2169.
  19. Debanshi Sandipta and Swades Pal. "Modelling water richness and habitat suitability of the wetlands and measuring their spatial linkages in mature Ganges delta of India”. Journal of Environmental Management 271 (2020): 110956.

Citation

Citation: Shweta Vikram. “Wetland with Emerging Technology: Indian Perspective". Acta Scientific Computer Sciences 5.6 (2023): 82-85.

Copyright

Copyright: © 2023 Shweta Vikram. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




Metrics

Acceptance rate35%
Acceptance to publication20-30 days

Indexed In




News and Events


  • Certification for Review
    Acta Scientific certifies the Editors/reviewers for their review done towards the assigned articles of the respective journals.
  • Submission Timeline for Upcoming Issue
    The last date for submission of articles for regular Issues is December 25, 2024.
  • Publication Certificate
    Authors will be issued a "Publication Certificate" as a mark of appreciation for publishing their work.
  • Best Article of the Issue
    The Editors will elect one Best Article after each issue release. The authors of this article will be provided with a certificate of "Best Article of the Issue"

Contact US