Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16089
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dc.contributor.authorGanga Dhara, Kalakada-
dc.contributor.authorTejashwini, C-
dc.contributor.authorRishika, L-
dc.contributor.authorSingh, Tinka-
dc.date.accessioned2024-07-22T03:50:48Z-
dc.date.available2024-07-22T03:50:48Z-
dc.date.issued2024-05-01-
dc.identifier.citation73p.en_US
dc.identifier.urihttps://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16089-
dc.description.abstractThis research project focuses on the prediction and forecasting of greenhouse gas (GHG) emissions and particulate matters from municipal solid waste landfill and incineration sites. Waste management practices, particularly land filling and incineration, are significant sources of GHG emissions and particulate matter, contributing to environmental degradation and public health concerns. Traditional methods for assessing and mitigating these emissions often rely on retrospective data analysis and simplistic modelling techniques, which may lack the predictive accuracy necessary for effective environmental management. In this study, we propose an innovative approach that integrates advanced statistical methodologies with comprehensive datasets to develop robust predictive models. By leveraging historical data on waste composition, operational parameters, meteorological conditions, and emission factors, our models aim to forecast future emissions trajectories with greater precision. The utilization of detailed datasets allows for a holistic understanding of the complex interactions between various factors influencing emissions dynamics at landfill and incineration sites. Moreover, this research seeks to address the limitations of conventional forecasting techniques by incorporating scenario analysis and sensitivity testing. By simulating different waste management scenarios and evaluating their potential impact on emissions, policymakers and stakeholders can make informed decisions to optimize waste management practices and mitigate environmental impacts. Overall, the outcomes of this research are expected to contribute significantly to the advancement of waste management strategies aimed at reducing GHG emissions and mitigating air pollution. By providing accurate predictions and forecasts, our models can empower decision-makers with valuable insights to promote sustainable waste management practices and safeguard public health and environmental quality.en_US
dc.language.isoenen_US
dc.publisherAlliance College of Engineering and Design, Alliance Universityen_US
dc.relation.ispartofseriesCSE_G19_2024 [20030141CSE042; 20030141CSE043; 20030141CSE049]-
dc.subjectGreenhouse Gas (Ghg) Emissionsen_US
dc.subjectOperational Parametersen_US
dc.subjectMeteorological Conditionsen_US
dc.subjectForecastingen_US
dc.subjectWindrose Graph Discussion.en_US
dc.titleMl – Based Prediction and Forecasting of Green House Gas Emissions and Particulate Matters From Municipal Solid Waste Landfill and Incinerationen_US
dc.typeOtheren_US
Appears in Collections:Dissertations - Alliance College of Engineering & Design

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