No 17 (2021)

Weather Forecasting and Monitoring using Machine Learning and Deep Neural Network Models

Authors: Dr. Basudeba Behera, Nitish Kumar, Mukesh Ranjan Mahato, Banoth Krishna Prasad, Dr. Vijay Bhaskar Semwal

Abstract: The exponentially increasing traffic on the road highways is creating safety issues for the vehicles. It is a challenge to make the highways safe and secure against traffic congestion and road accidents, which may happen due to a continuous rise in both traffic density and speed of the vehicles. It is imperative to devise vehicular communication technologies to ensure a reliable and powerful driving support system for the safety and efficiency of road transportation. Vehicular communication technologies are being designed to make early detection of the perilous situation and exchange the information among the vehicles, which may be used for issuing the warnings to the drivers and /or navigational aids. In addition, non-safety applications of vehicular networks are being embedded in the vehicles for the purpose of infotainment and the comfort of the passenger. In this paper, we present a comparative performance evaluation of three technologies- WAVE, WiMAX, and LTE-V2V with the aim to examine their relative strength in a dynamic vehicular environment. The impact of node density, node speed and beacon transmission frequency on the throughput, delay and packet delivery ratio is analyzed. Through numerical results, LTE-V2V is shown to outperform the other two alternatives in terms of throughput and delay.

Keywords: Deep Neural Network, Regression, Monsoon Break, Satellite System.

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