Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
February 17, 2026
Neural Networks and Rules-based Systems used to Find Rational and
Article Fingerprint
ReserarchID
9Z21O
Dumping of municipal solid waste in an open dumping site is a big threat to the environment affecting all the natural resources. This threat reaches to groundwater through leachate which even though takes a lot of time to percolate, through the soil profile and rocks beneath, and reach the water table but once contaminated it is very difficult to clean the groundwater. This has given a motto to attempt a study on the groundwater quality analysis of the area in and around Mathuradaspura dumping site which is receiving around 500 to 600 tonnes of solid waste daily from Jaipur Municipal Corporation. The study was taken up during 2020 and about 26 water samples were collected from the dump site and its vicinity. These samples were analysed for basic quality parameters and heavy metals. The variability of water quality in the area was figured out by subjecting the parameters to multivariate statistical analysis including correlation analysis (CA), Principal component analysis (PCA) and Hierarchical Cluster Analysis (HCA). Correlation analysis and PCA helped in establishing the inter-relationship of the quality parameters and major parameters responsible for deciding the groundwater quality of the area and their possible causes, respectively. The PCA confirmed the results of CA by classifying the parameters into four principal components that area responsible for total 76% variance of hydrochemical data of the study area. The application of HCA has resulted into the formation of three clusters each of Q and R modes depending on dominant chemical composition which describes the spatial distribution of samples and the possible causes influencing the groundwater quality of the study area.
Aruna Saini. 2026. \u201cVulnerability Analysis of Ground Water in and around MSW Disposal Site Mathuradaspura, Jaipur using Multivariate Statistical Techniques\u201d. Global Journal of Science Frontier Research - H: Environment & Environmental geology GJSFR-H Volume 22 (GJSFR Volume 22 Issue H3): .
Article file ID not found.
Crossref Journal DOI 10.17406/GJSFR
Print ISSN 0975-5896
e-ISSN 2249-4626
The methods for personal identification and authentication are no exception.
The methods for personal identification and authentication are no exception.
Neural Networks and Rules-based Systems used to Find Rational and
A Comparative Study of the Effeect of Promotion on Employee
The Problem Managing Bicycling Mobility in Latin American Cities: Ciclovias
Impact of Capillarity-Induced Rising Damp on the Energy Performance of
Dumping of municipal solid waste in an open dumping site is a big threat to the environment affecting all the natural resources. This threat reaches to groundwater through leachate which even though takes a lot of time to percolate, through the soil profile and rocks beneath, and reach the water table but once contaminated it is very difficult to clean the groundwater. This has given a motto to attempt a study on the groundwater quality analysis of the area in and around Mathuradaspura dumping site which is receiving around 500 to 600 tonnes of solid waste daily from Jaipur Municipal Corporation. The study was taken up during 2020 and about 26 water samples were collected from the dump site and its vicinity. These samples were analysed for basic quality parameters and heavy metals. The variability of water quality in the area was figured out by subjecting the parameters to multivariate statistical analysis including correlation analysis (CA), Principal component analysis (PCA) and Hierarchical Cluster Analysis (HCA). Correlation analysis and PCA helped in establishing the inter-relationship of the quality parameters and major parameters responsible for deciding the groundwater quality of the area and their possible causes, respectively. The PCA confirmed the results of CA by classifying the parameters into four principal components that area responsible for total 76% variance of hydrochemical data of the study area. The application of HCA has resulted into the formation of three clusters each of Q and R modes depending on dominant chemical composition which describes the spatial distribution of samples and the possible causes influencing the groundwater quality of the study area.
We are currently updating this article page for a better experience.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.
