Dual Water Choices: The Assessment of the Influential Factors on Water Sources Choices Using Unsupervised Machine Learning Market Basket Analysis

An unsupervised machine learning model of association rule known as market basket analysis is proposed in this study to analyze the influence of various socio-economic factors on the choice of the water source. Data of 51 socio-economic factors collected from 295 individuals living in 65 households...

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Autores principales:	Tiyasha Tiyasha, Suraj Kumar Bhagat, Firaol Fituma, Tran Minh Tung, Shamsuddin Shahid, Zaher Mundher Yaseen
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Developing countries socio-economic factors unsupervised machine learning water policy Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/cf2f1979b63447efa7e307f14fc813c6
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spelling	oai:doaj.org-article:cf2f1979b63447efa7e307f14fc813c62021-11-18T00:07:03ZDual Water Choices: The Assessment of the Influential Factors on Water Sources Choices Using Unsupervised Machine Learning Market Basket Analysis2169-353610.1109/ACCESS.2021.3124817https://doaj.org/article/cf2f1979b63447efa7e307f14fc813c62021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9598811/https://doaj.org/toc/2169-3536An unsupervised machine learning model of association rule known as market basket analysis is proposed in this study to analyze the influence of various socio-economic factors on the choice of the water source. Data of 51 socio-economic factors collected from 295 individuals living in 65 households in Ambo city in the Oromia region of Ethiopians were used for this purpose. The results revealed (i) 64% of the family preferred multiple water sources (i.e., public tap and river water), (ii) the water was collected females in 92% of the households, and (iii) majority of people preferred bathing and laundering in the river (support = 32% and confidence = 87%). Direct utilization of river water is not a preferable choice for the user since it may lead to severe health issues and cause water pollution from bathing and laundering. Education and monthly income have a significant impact on the choices of water sources. Local management authorities can improve sanitation and public health management using the results obtained in the study. The paper only gives a glimpse of the important factors that should be considered for improving the way of life for the underdeveloped areas of the world using advanced machine learning techniques.Tiyasha TiyashaSuraj Kumar BhagatFiraol FitumaTran Minh TungShamsuddin ShahidZaher Mundher YaseenIEEEarticleDeveloping countriessocio-economic factorsunsupervised machine learningwater policyElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 150532-150544 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Developing countries socio-economic factors unsupervised machine learning water policy Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Developing countries socio-economic factors unsupervised machine learning water policy Electrical engineering. Electronics. Nuclear engineering TK1-9971 Tiyasha Tiyasha Suraj Kumar Bhagat Firaol Fituma Tran Minh Tung Shamsuddin Shahid Zaher Mundher Yaseen Dual Water Choices: The Assessment of the Influential Factors on Water Sources Choices Using Unsupervised Machine Learning Market Basket Analysis
description	An unsupervised machine learning model of association rule known as market basket analysis is proposed in this study to analyze the influence of various socio-economic factors on the choice of the water source. Data of 51 socio-economic factors collected from 295 individuals living in 65 households in Ambo city in the Oromia region of Ethiopians were used for this purpose. The results revealed (i) 64% of the family preferred multiple water sources (i.e., public tap and river water), (ii) the water was collected females in 92% of the households, and (iii) majority of people preferred bathing and laundering in the river (support = 32% and confidence = 87%). Direct utilization of river water is not a preferable choice for the user since it may lead to severe health issues and cause water pollution from bathing and laundering. Education and monthly income have a significant impact on the choices of water sources. Local management authorities can improve sanitation and public health management using the results obtained in the study. The paper only gives a glimpse of the important factors that should be considered for improving the way of life for the underdeveloped areas of the world using advanced machine learning techniques.
format	article
author	Tiyasha Tiyasha Suraj Kumar Bhagat Firaol Fituma Tran Minh Tung Shamsuddin Shahid Zaher Mundher Yaseen
author_facet	Tiyasha Tiyasha Suraj Kumar Bhagat Firaol Fituma Tran Minh Tung Shamsuddin Shahid Zaher Mundher Yaseen
author_sort	Tiyasha Tiyasha
title	Dual Water Choices: The Assessment of the Influential Factors on Water Sources Choices Using Unsupervised Machine Learning Market Basket Analysis
title_short	Dual Water Choices: The Assessment of the Influential Factors on Water Sources Choices Using Unsupervised Machine Learning Market Basket Analysis
title_full	Dual Water Choices: The Assessment of the Influential Factors on Water Sources Choices Using Unsupervised Machine Learning Market Basket Analysis
title_fullStr	Dual Water Choices: The Assessment of the Influential Factors on Water Sources Choices Using Unsupervised Machine Learning Market Basket Analysis
title_full_unstemmed	Dual Water Choices: The Assessment of the Influential Factors on Water Sources Choices Using Unsupervised Machine Learning Market Basket Analysis
title_sort	dual water choices: the assessment of the influential factors on water sources choices using unsupervised machine learning market basket analysis
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/cf2f1979b63447efa7e307f14fc813c6
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Dual Water Choices: The Assessment of the Influential Factors on Water Sources Choices Using Unsupervised Machine Learning Market Basket Analysis

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