Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Linear Predictive Model for Dissolved Oxygen in a Protected Urban Lake using Machine Learning Techniques

Abu Selim
* ,
Shah Newaz Alam Shuvo
ORCID logo ,
Mohammad Moniruzzaman
,
M. M. Islam
,
Sakifa Shah
,
Md Ohiduzzaman
Version 1 : Received: 5 February 2023 / Approved: 6 February 2023 / Online: 6 February 2023 (07:37:03 CET)
Version 2 : Received: 27 February 2023 / Approved: 27 February 2023 / Online: 27 February 2023 (07:25:06 CET)

A peer-reviewed article of this Preprint also exists.

Selim, A.; Shuvo, S.N.A.; Islam, M.M.; Moniruzzaman, M.; Shah, S.; Ohiduzzaman, M. Predictive Models for Dissolved Oxygen in an Urban Lake by Regression Analysis and Artificial Neural Network. Total Environment Research Themes 2023, 100066, doi:10.1016/j.totert.2023.100066. Selim, A.; Shuvo, S.N.A.; Islam, M.M.; Moniruzzaman, M.; Shah, S.; Ohiduzzaman, M. Predictive Models for Dissolved Oxygen in an Urban Lake by Regression Analysis and Artificial Neural Network. Total Environment Research Themes 2023, 100066, doi:10.1016/j.totert.2023.100066.

Abstract

The paper portrays a linear model for synchronous prediction of the dissolved oxygen (DO) level in a protected urban lake by using independent variables in real-time monitoring of the water quality parameters. Multi-linear regression and machine learning techniques were applied to the Dissolved Oxygen (DO) using the basic four parameters of Water Quality Index (WQI). Three real-time industry standard sensors PHEHT, CTZN, OPTOD were used for sampling, data was then interpolated through ArcGIS kriging method. 25 correlations were checked through the ML algorithm, a correlation heat map was produced and the top five relations were taken under consideration and validated by r-score and root mean squared error (RMSE) to develop the linear regression model. The performance of the model was validated through the RMSE, mean squared error (MSE), and mean absolute error (MAE) the r2 accuracy came to 0.983 when it is checked against the sample data. The suggested model can be used as a technique for unknown or malfunctioned sensor’s null DO data prediction.

Keywords

Multiple Linear Regression; Dissolve Oxygen; Machine Learning; Urban Lake

Subject

Environmental and Earth Sciences, Environmental Science

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Download PDF

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0
Get PDF Cite Share 0
Bookmark BibSonomy Mendeley Reddit Delicious
×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.