GIS Mapping of Short-Term Noisy Event of Diwali Night in Lucknow City

Dubey, Rakesh; Bharadwaj, Shruti; Zafar, M. I.; Mahajan, Vanshu; Srivastava, Anubhava; Biswas, Susham

doi:10.3390/ijgi11010025

Cited by 10 publications

(6 citation statements)

References 26 publications

(28 reference statements)

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“…The buffer radius used to allocate the detected noise sources to noise levels should also be considered. According to several studies [12,13], the buffer distance should be sufficient to gain insights into the spatial distribution of the noise. Further research can deal with this open question.…”

Section: Discussionmentioning

confidence: 99%

“…However, the prediction of noise levels as a map requires integration with a prediction model in a geographic information system (GIS) environment [10]. Traditionally, these noise maps have been generated using specialized software that combines noise emission and acoustic dispersion models with geospatial and traffic data [11][12][13][14][15]. While they offer a general overview of the noise distribution and the impact of noise reduction initiatives, they are often criticized for being unrealistic, especially when considering the temporal fluctuations in noise levels [16].…”

Section: Introductionmentioning

confidence: 99%

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Investigating Noise Mapping in Cities to Associate Noise Levels with Sources of Noise Using Crowdsourcing Applications

Othman,

Cibilić,

Poslončec-Petrić

et al. 2024

Urban Science

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Environmental noise is a major environmental concern in metropolitan cities. The rapid social and economic growth in the 20th century is not always accompanied by adequate land planning and environmental management measures. As a consequence of rapid urbanization processes, cities are facing an increase in noise pollution. Noise is being recognized as a serious environmental problem and one which must be accounted for in a sustained development policy designed to improve the quality of life for citizens. Therefore, the monitoring of noise is a crucial aspect of urban planning to allow urban planners to create harmonious and livable environments for communities worldwide. This research aims at assessing the noise levels and associated sources of noise in downtown areas through the involvement of crowdsourcing techniques. The incorporation of noise mapping and increased public awareness are achieved by a framework that enables a comparative scheme between two cities: Alexandria, Egypt and Zagreb, Croatia. The methodology depends on combining crowdsourcing techniques using mobile applications and geographic information system (GIS) tools to detect and analyze noise. Finally, this research provides a comparison between the two cities using the adopted methodology and introduces recommendations to enhance urban planning decisions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigating Noise Mapping in Cities to Associate Noise Levels with Sources of Noise Using Crowdsourcing Applications

Othman,

Cibilić,

Poslončec-Petrić

et al. 2024

Urban Science

View full text Add to dashboard Cite

show abstract

“…Noise is a general problem that has serious health implications associated with it [1]. The rapid growth in the amount of vehicular traffic combined with increased vehicular speeds has resulted in increased noise pollution in cities [2][3][4][5][6][7][8]. Road traffic noise levels above 65 dB(A) disturb nearly 50 million people in the European Union [9,10].…”

Section: Introductionmentioning

confidence: 99%

Reducing Data Requirements for Simple and Effective Noise Mapping: A Case Study of Noise Mapping Using Computational Methods and GIS for the Raebareli City Intersection

Zafar,

Bharadwaj,

Dubey

et al. 2023

Acoustics

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The accurate prediction of noise levels at outdoor locations requires detailed data of the noise sources and terrain parameters and an efficient model for prediction. However, the possibility of predicting noise with reasonable accuracy using less input data is a challenge and needs to be studied scientifically. The qualities of the noise data, terrain parameters, and prediction model can impact the accuracy of the prediction significantly. This study primarily focuses on the dependency of noise data for efficient noise prediction and mapping. This research article proposes a detailed methodology to predict and map the noise and exposure levels in Ratapur, Uttar Pradesh, India, with various granularities of noise data inputs. The noise levels were measured at various places and at different times of the day at 10 min intervals. Different data input proportions and qualities were used for noise prediction, namely, (1) a large data-based method, (2) a small data-based method, (3) a source point average data-based method, (4) a Google navigation data-based method, and (5) accurate modelling using an ANN-based method, integrating accurate noise data with a sophisticated modelling algorithm for noise prediction. The analysis of the variation between the predicted and measured noise levels was conducted for all five of the methods using the ANOVA technique. Various methods based on less noise data methods predicted the noise levels with accuracies within the ±4–10 dB(A) range, while the ANN-based technique predicted it with an accuracy of ±0.5–2.5 dB(A). Interestingly, the estimation of the noise exposure levels (>85 dB(A)) and the identification of hazard zones around the studied road intersection could also be performed efficiently even when using the data-deficient models. This paper also showcased the possibility of predicting an accurate 3D map for an area by extracting vehicles and terrain features from satellite images without any direct recording of noise data. This paper thus demonstrated approaches to reduce the noise data dependency for noise prediction and mapping and to enable accurate noise-hazard zonation mapping.

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“…The selection of an appropriate location necessitated the use of 3D terrain data, which is often represented as point cloud data with x, y, and z values. It comes in the form of a raster, vector, or point cloud and presents a challenge to extract the terrain's features [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method to Determine the Optimal Location for a Cellular Tower by Using LiDAR Data

Bharadwaj

Dubey

Zafar

et al. 2022

ASI

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The cellular industry faces challenges in controlling the quality of signals for all users, given its meteoric growth in the last few years. The service providers are required to place cellular towers at the optimal location for providing a strong cellular network in a particular region. However, due to buildings, roads, open spaces, etc., of varying topography in 3D (obstructing the signals) and varying densities of settlements, finding the optimal location for the tower becomes challenging. Further, in a bigger area, it is required to determine the optimum number and locations for setting up cellular towers to ensure improved quality. The determination of optimum solutions requires a signal strength prediction model that needs to integrate terrain data, information of cellular tower with users’ locations, along with tower signal strengths for predictions. Existing modeling practices face limitations in terms of the usage of 2D data, rough terrain inputs, and the inability to provide detailed shapefiles to GIS. The estimation of optimum distribution of cellular towers necessitates the determination of a model for the prediction of signal strength at users’ locations accurately. Better modeling is only possible with detailed and precise data in 3D. Considering the above needs, a LIDAR data-based cellular tower distribution modeling is attempted in this article. The locations chosen for this research are RGIPT, UP (45 Acre), and Shahganj, Agra, UP, India (6 km2). LiDAR data and google images for the project sites were classified as buildings and features. The edges of overground objects were extracted and used to determine the routes for transmission of a signal from the tower to user locations. The terrain parameters and transmission losses for every route are determined to model the signal strength for a user’s location. The ground strength of signals is measured over 1000 points in 3D at project sites to compare with modeled signal strengths (an RMSE error 3.45). The accurate model is then used to determine the optimum number and locations of cellular towers for each site. Modeled optimum solutions are compared with existing tower locations to estimate % over design or under design and the scope of improvement (80% users below −80 dB m improves to 70% users above −75 dB m).

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GIS Mapping of Short-Term Noisy Event of Diwali Night in Lucknow City

Cited by 10 publications

References 26 publications

Investigating Noise Mapping in Cities to Associate Noise Levels with Sources of Noise Using Crowdsourcing Applications

Investigating Noise Mapping in Cities to Associate Noise Levels with Sources of Noise Using Crowdsourcing Applications

Reducing Data Requirements for Simple and Effective Noise Mapping: A Case Study of Noise Mapping Using Computational Methods and GIS for the Raebareli City Intersection

A Novel Method to Determine the Optimal Location for a Cellular Tower by Using LiDAR Data

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