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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1985
Title: Greenhouse gas level prediction in shopping mall parking basements an anfis approach
Authors: Vu Hoang Minh Tuan 
Keywords: Greenhouse gas emissions;Adaptive neuro-fuzzy inference system;Neural network
Issue Date: 2024
Abstract: 
Greenhouse gas (GHG) emissions pose a significant threat to the environment, contributing to climate change and air quality deterioration, especially in urban areas. The confined spaces of parking basements in shopping malls can become hotspots for the accumulation of harmful gases such as CO2, CH4, and NO2, leading to potential health hazards. Accurately predicting and managing these emissions is crucial for maintaining air quality and ensuring public safety. In this context, the Adaptive Neuro-Fuzzy Inference System (ANFIS) offers a powerful tool for modeling complex environmental data due to its ability to handle nonlinear relationships and uncertainty. This study investigates the application of ANFIS to predict GHG emissions within the parking basement of Crescent Mall, a busy shopping center in Ho Chi Minh City. By collecting and analyzing CO2, CH4, and NO2 data, the research explores the performance of ANFIS models constructed using grid partitioning and subtractive clustering techniques. The models were evaluated using metrics such as Root Mean Square Error (RMSE), coefficient of determination (R²), and Mean Absolute Percentage Error (MAPE). The results indicate that the ANFIS model, particularly when utilizing subtractive clustering with a small cluster radius, provided highly accurate predictions, especially for evening data. This suggests that ANFIS is a viable tool for predicting GHG emissions in enclosed urban environments, offering potential applications for real-time monitoring and air quality management in similar settings
URI(1): https://epub.vgu.edu.vn/handle/dlibvgu/1985
Rights: Attribution-NonCommercial 4.0 International
Appears in Collections:Mechanical Engineering (MEN)

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