Manuscript Number : SHISRRJ247226

Predictive Model of Water Quality Analysis

Authors(2) :-B. Rupadevi, B. Naveen

In order to provide safe drinking water for people and aquatic life, the water quality analysis and prediction project describes how machine learning algorithms will be employed in this regard. It talks about how important these kinds of predictive models are to preserving water safety and emphasizes how machine learning is becoming more and more popular in this field since it can manage complicated datasets and produce precise forecasts. The research discusses the use of random forests, a machine learning method, in the analysis and prediction of water quality. It focuses on how machine learning models may be taught to predict changes in these characteristics based on historical data and environmental variables. These factors include pH, temperature, dissolved oxygen, and nutrient levels. This paper proposes a method for the Random Forest Regressor-based prediction of water quality. This study investigates various supervised machine learning techniques to estimate the water quality class (WQC), a unique class defined based on the WQI, and the water quality index (WQI), a single index to characterize the overall quality of water. Several tests are carried out with real-world datasets related to water quality to assess the model's efficacy. Performance evaluation metrics include R-squared, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The efficacy of the Random Forest Regressor approach is demonstrated by comparisons with other machine learning algorithms. The outcomes show that the system can accurately predict water quality measures.. The importance of predicting and analyzing water quality as well as the potential benefits of using machine learning techniques are highlighted in the abstract's conclusion. It suggests that these techniques may be used to develop accurate and reliable models that effectively safeguard water resources, ensuring their sustainability and security.

Authors and Affiliations

B. Rupadevi
Associate Professor, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, Andhra Pradesh, India
B. Naveen
Post Graduate, Department of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, Andhra Pradesh, India

Predictive modelling, Machine Learning, Random Forest Regressor, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-squared, Water Quality Index (WQI), Water Quality Class (WQC), Comparative Analysis, Scalability, Real-Time Monitoring

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Publication Details

Published in : Volume 7 | Issue 2 | March-April 2024
Date of Publication : 2024-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 560-567
Manuscript Number : SHISRRJ247226
Publisher : Shauryam Research Institute

ISSN : 2581-6306

Cite This Article :

B. Rupadevi, B. Naveen, "Predictive Model of Water Quality Analysis", Shodhshauryam, International Scientific Refereed Research Journal (SHISRRJ), ISSN : 2581-6306, Volume 7, Issue 2, pp.560-567, March-April.2024
URL : https://shisrrj.com/SHISRRJ247226

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