Current Volume 7
Trend Analysis of Water Physio-parameters of Mumbai, Pune, and Nagpur Using Machine Learning
Abstract
Rapid urbanization in cities like Mumbai, Pune, and Nagpur has engendered a myriad of challenges, chief among them being the preservation of water quality and sustainability. As urban centers burgeon, the demand for water resources escalates. However, this surge in demand is often juxtaposed with a deleterious rise in industrialization, infrastructural expansion, and the rampant discharge of sewage and pollutants into water bodies. These factors, in conjunction, have contributed to a progressive deterioration of water quality in urban areas, necessitating a paradigm shift in the management of this vital resource. The efficient management of water resources and the assurance of a clean drinking water supply have become pivotal issues confronted by municipal authorities. This research undertakes a comprehensive analysis of water quality trends in Mumbai, Pune, and Nagpur, employing advanced machine learning techniques to fathom complex datasets. These datasets encompass an array of physio-chemical parameters, such as pH, turbidity, electrical conductivity, and more, all of which are critical indicators of water pollution. The study requisitions historical water quality data procured from a multitude of sources, including rivers, lakes, and reservoirs. Subsequent preprocessing of this raw data precedes the application of machine learning algorithms like support vector machines, random forests, and k-nearest neighbors to unearth latent trends and patterns. The research stands upon a dual pedestal: first, it seeks to underscore the potential of machine learning as an indispensable tool for environmental monitoring and water quality analysis; second, it aspires to furnish urban planners and policymakers with actionable insights, paving the way for the mitigation of water pollution in these burgeoning cities. By aligning growth demands with environmental sustainability, the research aligns itself with the ultimate objective of fortifying the foundations of urban development with data-driven evidence, thereby orchestrating a harmonious balance between burgeoning urbanization and ecological preservation. In summary, this study endeavors to harness the power of data science to champion the cause of sustainable urban development, thereby ensuring the enduring sanctity of water quality in the cities of Mumbai, Pune, and Nagpur.
Keywords
Trend Analysis, Supervised Learning Model, Classification
Citations
IRE Journals:
Siddhesh Prakash Mhatre , Vijay Prajapati , Dr. S. K. Singh
"Trend Analysis of Water Physio-parameters of Mumbai, Pune, and Nagpur Using Machine Learning" Iconic Research And Engineering Journals Volume 7 Issue 8 2024 Page 181-188
IEEE:
Siddhesh Prakash Mhatre , Vijay Prajapati , Dr. S. K. Singh
"Trend Analysis of Water Physio-parameters of Mumbai, Pune, and Nagpur Using Machine Learning" Iconic Research And Engineering Journals, 7(8)
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IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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