We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Identifikasi Kelayakan Air Minum Dengan Metode Analisis Komponen Utama Berbasis Entropi
Summary
This study developed an entropy-based principal component analysis (PCA) method to assess drinking water quality in terms of its fitness for human consumption. Physical, chemical, and microbiological parameters including microplastic indicators were analyzed to determine which factors most influence water safety.
The need for clean water is a fundamental requirement that must be met by humans, as water constitutes 60 to 70% of the total human body weight. Therefore, it is important to be able to determine the quality of the water entering the body, as consuming unsafe water will bring various diseases, such as diarrhea, and in severe cases might lead to death. This study aimed to investigate the factors which determine the potability of drinking water. Specifically, this research aims to produce a fault detection algorithm that can detect the potability of water samples based on Principal Component Analysis (PCA) and entropy-based subset selection methods. This paper addresses the linearity problem that commonly occurred in PCA by finding a subset of data that has a good entropy relation among the parameters contained in the subset, thus maintaining linearity in the data. There were 8 parameters considered in this reseach: pH, hardness, total dissolved solids, chloramines, sulfate, conductivity, organics carbon, trihalomethanes and turbidity. The experiment was conducted with 811 water samples, where 645 samples were used to train the model and the rest for validating the model predictive accuracy. Based on experiments conducted, it is confirmed that the proposed algorithm can determine the potability of drinking water samples from synthetic data sourced from India with an accuracy of over 98% for potable water data and 100% for non-potable water data.
Sign in to start a discussion.
More Papers Like This
Assessing water quality in North-East Algeria: a comprehensive study using water quality index (WQI) and PCA
Researchers assessed water quality across multiple drinking water sources in North-East Algeria using a water quality index and principal component analysis. The study provides a comprehensive evaluation of contamination levels in the Cheffia Dam, Oued El Aneb, and Treat boreholes, identifying key pollutant sources affecting these important water supplies.
Machine Learning to Access and Ensure Safe Drinking Water Supply: A Systematic Review
This systematic review examines machine learning applications for monitoring, predicting, and controlling drinking water quality, covering contaminants from disinfection byproducts to biofilms and antimicrobial resistance genes. While not specifically about microplastics, the ML approaches described are directly applicable to detecting and predicting microplastic contamination in engineered water systems.
Hygienic approaches to the safety levels identification of microplastics in water
Researchers developed a program of analytical and toxicological studies to establish safety levels for microplastics in water, addressing the international classification of microplastics as a new health hazard. The study combined literature analysis with sanitary-chemical and sanitary-microbiological experiments to propose indicators and criteria for assessing microplastic danger in water. The findings aim to support the development of regulatory standards for microplastic contamination in drinking water.
Efficient Data-Driven Machine Learning Models for Water Quality Prediction
This study tested machine learning methods for predicting water quality based on physical, chemical, and biological measurements. While focused on water safety testing rather than microplastics specifically, the automated classification tools developed here could help water treatment facilities quickly identify contaminated water. Better monitoring technology is important because current methods for detecting microplastics in water are slow and expensive.
Identifying microplastic contamination in drinking water: analysis and evaluation using spectroscopic methods
Researchers developed analytical methods to identify and quantify microplastic contamination in drinking water, evaluating extraction efficiency and detection accuracy across different water types and plastic particle sizes. The study assessed health implications based on measured plastic loads in treated water.