A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Prediction of Noise Pollution of Delhi City Using Machine Learning: A Case Study
This paper discusses the prediction of noise pollution during Deepawali festival of Delhi City using machine learning (ML) algorithms. The spatial noise pollution data of four locations of Delhi, namely Lajpat Nagar, Mayur Vihar-II, Kamla Nagar, and Pitam Pura were collected from the Central Pollution Control Board (CPCB). Seven regression models were used on the Python platform. Algorithms were run using Google Colab. As the data obtained were very little, additional two random data were generated and used in the analysis. It was found that among all models, Quantile Regression is a superior one in the prediction of noise level in the present study as compared to other ML models. It is observed that coefficient of determination with Quantile Regression is 0.792 for original data, 0.803 for 150 random data, and 0.801 for 300 random data. However, at other locations, the suitability of a particular regression model can be determined and recommended.
Prediction of Noise Pollution of Delhi City Using Machine Learning: A Case Study
This paper discusses the prediction of noise pollution during Deepawali festival of Delhi City using machine learning (ML) algorithms. The spatial noise pollution data of four locations of Delhi, namely Lajpat Nagar, Mayur Vihar-II, Kamla Nagar, and Pitam Pura were collected from the Central Pollution Control Board (CPCB). Seven regression models were used on the Python platform. Algorithms were run using Google Colab. As the data obtained were very little, additional two random data were generated and used in the analysis. It was found that among all models, Quantile Regression is a superior one in the prediction of noise level in the present study as compared to other ML models. It is observed that coefficient of determination with Quantile Regression is 0.792 for original data, 0.803 for 150 random data, and 0.801 for 300 random data. However, at other locations, the suitability of a particular regression model can be determined and recommended.
Prediction of Noise Pollution of Delhi City Using Machine Learning: A Case Study
Lecture Notes in Civil Engineering
Sreekeshava, K. S. (editor) / Kolathayar, Sreevalsa (editor) / Vinod Chandra Menon, N. (editor) / Khanai, Rajashri (author) / Raikar, Rajkumar (author) / Uppinmath, Mrutyunjay (author)
International Conference on Interdisciplinary Approaches in Civil Engineering for Sustainable Development ; 2023
2024-03-26
11 pages
Article/Chapter (Book)
Electronic Resource
English
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