• Register
  • Login

European Journal of Molecular & Clinical Medicine

  • Home
  • Browse
    • Current Issue
    • By Issue
    • By Subject
    • Keyword Index
    • Author Index
    • Indexing Databases XML
  • Journal Info
    • About Journal
    • Aims and Scope
    • Editorial Board
    • Publication Ethics
    • Indexing and Abstracting
    • Peer Review Process
    • News
  • Guide for Authors
  • Submit Manuscript
  • Contact Us
Advanced Search

Notice

As part of Open Journals’ initiatives, we create website for scholarly open access journals. If you are responsible for this journal and would like to know more about how to use the editorial system, please visit our website at https://ejournalplus.com or
send us an email to info@ejournalplus.com

We will contact you soon

  1. Home
  2. Volume 7, Issue 5
  3. Author

Online ISSN: 2515-8260

Volume7, Issue5

PERFORMANCE EVALUATION OF MACHINE LEARNING ALGORITHMS IN FORECASTING WATER QUALITY INDICES: STUDY IN TAMILNADU WATER BODIES

    A.Rama, S Rajakumari, P.Selvamani

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 5, Pages 1892-1900

  • Show Article
  • Download
  • Cite
  • Statistics
  • Share

Abstract

 
Water quality prediction play an essential role in aqua environment management.  The demand for accurate water quality prediction techniques for efficient water resources management. Currently, the Indian pollution control board has set up various monitoring stations to measure water quality frequently. However, the forecast for water quality is currently not being carried out. In this work, machine learning models have been implemented to predict the indices of water quality. The efficiency of logistic Linear regression and AdaBoostRegressor in the prediction of seven major water quality parameters were evaluated. The Tamil Nadu water quality dataset is used in this analysis. The parameters such as pH value, the quantity of oxygen dissolved, total coli form, B.D.O, electric conductivity, the quantity of phosphorus, and nitrate are considered. The assessed error-index value of the applied models showed that the AdaboostRegressor obtains a lesser error-index and it can consider being a more accurate model than the Linear regression model. The entire methodology proposed here is in the context of water quality is based on numerical analysis. While investigating the outcomes of the implemented machine learning models, it is demonstrated that they have nearly over-estimation properties. The proposed models are assessed using the metrics Mean Square Error and R2 score the results reflect that AdaboostRegressor predicts the (Water Quality Indices) WQI rate with a Mean Square Error value of 0.8, and R2 score rate is 0.41, whereas AdaBoostRegressor with a obtains Mean Square Error (MSE) rate as 0.74 and R2 score rate as 0.44.
Keywords:
  • PDF (293 K)
  • XML
(2021). PERFORMANCE EVALUATION OF MACHINE LEARNING ALGORITHMS IN FORECASTING WATER QUALITY INDICES: STUDY IN TAMILNADU WATER BODIES. European Journal of Molecular & Clinical Medicine, 7(5), 1892-1900.
A.Rama, S Rajakumari, P.Selvamani. "PERFORMANCE EVALUATION OF MACHINE LEARNING ALGORITHMS IN FORECASTING WATER QUALITY INDICES: STUDY IN TAMILNADU WATER BODIES". European Journal of Molecular & Clinical Medicine, 7, 5, 2021, 1892-1900.
(2021). 'PERFORMANCE EVALUATION OF MACHINE LEARNING ALGORITHMS IN FORECASTING WATER QUALITY INDICES: STUDY IN TAMILNADU WATER BODIES', European Journal of Molecular & Clinical Medicine, 7(5), pp. 1892-1900.
PERFORMANCE EVALUATION OF MACHINE LEARNING ALGORITHMS IN FORECASTING WATER QUALITY INDICES: STUDY IN TAMILNADU WATER BODIES. European Journal of Molecular & Clinical Medicine, 2021; 7(5): 1892-1900.
  • RIS
  • EndNote
  • BibTeX
  • APA
  • MLA
  • Harvard
  • Vancouver
  • Article View: 33
  • PDF Download: 66
  • LinkedIn
  • Twitter
  • Facebook
  • Google
  • Telegram
Journal Information

Publisher:

Email:  info@ejmcm.com

  • Home
  • Glossary
  • News
  • Aims and Scope
  • Privacy Policy
  • Sitemap

Editorial Team:  editor@ejmcm.com

For Special Issue Proposal : chiefeditor.ejmcm@gmail.com / info@ejmcm.com

This journal is licensed under a Creative Commons Attribution 4.0 International (CC-BY 4.0)

Powered by eJournalPlus