NoiseModelling: An Open Source GIS Based Tool to Produce Environmental Noise Maps

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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Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction

Bharadwaj

Dubey

Zafar

et al. 2022

ASI

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Urban planning, noise propagation modelling, viewshed analysis, etc., require determination of routes or supply lines for propagation. A point-to-point routing algorithm is required to determine the best routes for the propagation of noise levels from source to destination. Various optimization algorithms are present in the literature to determine the shortest route, e.g., Dijkstra, Ant-Colony algorithms, etc. However, these algorithms primarily work over 2D maps and multiple routes. The shortest route determination in 3D from unlabeled data (e.g., precise LiDAR terrain point cloud) is very challenging. The prediction of noise data for a place necessitates extraction of all possible principal routes between every source of noise and its destination, e.g., direct route, the route over the top of the building (or obstruction), routes around the sides of the building, and the reflected routes. It is thus required to develop an algorithm that will determine all the possible routes for propagation, using LiDAR data. The algorithm uses the novel cutting plane technique customized to work with LiDAR data to extract all the principal routes between every pair of noise source and destination. Terrain parameters are determined from routes for modeling. The terrain parameters, and noise data when integrated with a sophisticated noise model give an accurate prediction of noise for a place. The novel point-to-point routing algorithm is developed using LiDAR data of the RGIPT campus. All the shortest routes were tested for their spatial accuracy and efficacy to predict the noise levels accurately. Various routes are found to be accurate within ±9 cm, while predicted noise levels are found to be accurate within ±6 dBA at an instantaneous scale. The novel accurate 3D routing algorithm can improve the other urban applications too.

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NoiseModelling: An Open Source GIS Based Tool to Produce Environmental Noise Maps

Cited by 11 publications

References 25 publications

Noise Mapping

Noise Mapping

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

Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction

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