Open AccessDual Water Choices: The Assessment of the Influential Factors on Water Sources Choices Using Unsupervised Machine Learning Market Basket Analysis
Author(s) -
Tiyasha Tiyasha,
Suraj Kumar Bhagat,
Firaol Fituma,
Tran Minh Tung,
Shamsuddin Shahid,
Zaher Mundher Yaseen
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3124817
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.