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Analysis Study Research Paper | Computer Engineering | India | Volume 13 Issue 8, August 2024 | Popularity: 5.9 / 10

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Optimizing Water Filtration Through Machine Learning Based Water Quality Indexing

Zaeem Farooq

Abstract: Water quality is a critical aspect of public health and environmental sustainability, necessitating efficient and effective water filtration systems. The selection of appropriate water filtration technology, whether Reverse Osmosis (RO), Ultraviolet (UV), or Ultrafiltration (UF), is pivotal to ensuring safe and clean water. This research explores the application of predictive analytics and machine learning techniques to optimize the selection process between these filtration methods based on various water quality parameters. This study explores the application of machine learning techniques for optimizing water filtration strategies based on water quality parameters The research aims to predict the most suitable filtration methods, including Reverse Osmosis RO, Ultraviolet UV, and Ultrafiltration UF derived from water quality indexing (WQI). We collected extensive datasets encompassing a range of water quality indicators, such as pH, turbidity, temperature, dissolved oxygen, total dissolved solids (TDS), and concentrations of various contaminants. Machine learning algorithms, including decision trees, random forests, support vector machines (SVM), and neural networks, were then applied to this dataset to develop predictive models. These models were trained to classify and recommend the most suitable filtration technology based on the input water quality parameters. We leveraged a supervised learning approach in order to design as accurate as possible predictive models from a labelled training dataset for the identification of filtration methods. A set of physiochemical and microbiological parameters as input features help represent the water's status and determine its suitability class namely safe, non-safe, moderate, or excellent. A comparative evaluation of various machine learning models is done to identify the best algorithm and classify the data into labels. These labels are water quality index (WQI) derived from the classifier algorithms for the objective of this study, K-means Clustering, kNN, AdaBoostM1, Random Forest (RF), K-means clustering, Stacking, Voting and Bagging are selected in order to establish the desired filtration approach i.e., RO, UV, UF, TDS with the greatest precision and accuracy. The results demonstrated that machine learning models could predict the optimal filtration method with high accuracy. Decision trees and random forests showed particularly robust performance, with accuracy rates exceeding 90%. These models were able to handle the complex and nonlinear relationships between water quality parameters and the effectiveness of different filtration methods. In addition to predictive accuracy, the models provided insights into the importance of various water quality parameters in the decision-making process. This research underscores the potential of machine learning and predictive analytics in transforming water quality management. Future work will focus on refining the models with larger datasets, incorporating more diverse water quality parameters, and exploring the application of deep learning techniques for even more accurate predictions. The ultimate goal is to develop a comprehensive decision support system that can be deployed in various water treatment facilities to ensure the delivery of safe and clean water to communities worldwide.

Keywords: Water Quality Index, Machine Learning, Filtration Strategies, Predictive Analytics, Environmental Science

Edition: Volume 13 Issue 8, August 2024

Pages: 1583 - 1587

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