Abstract:
Good health policy requires that all people have access to safe drinking water as a basic human right. In terms of national, regional, and local health and development, this is critical. Water and sanitation improvements have been found to provide a positive return on investment in certain areas, since the reductions in health risks and medical expenses much surpass the costs of making the improvements. To check whether that water is safe or not we have some parameters which need to be checked like pH value which ranges from 6.52 - 6.83 and Hardness, Chloramines, Sulphate, Conductivity, Organic carbon, Trihalomethanes, Turbidity, and at last portability. When we acquire a result of 1, we know that the water is safe to drink. If we get a portability value of 0 it is not safe for water consumption Before checking the quality of water, we need to collect all water bodies' images from Google earth maps and mask them and check their pot ability.The project involves data analysis of the different parameters which are involved in checking the portability of water with proper dataset using data processing methods. Random Forest, Decision Tree and other machine learning algorithms are used to make predictions. With the use of VGG image Annotator and leakage location estimate algorithms such as cross correlation of sinusoidal waves and water bodies are masked out of water distribution pipes.
Page(s):
5514-5520
DOI:
DOI not available
Published:
Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 19, Year: 2022
Keywords:
deep learning
,
Image Processing
,
Google earth map
,
image masking
,
water portability