Pollutant Level Predicator is a system which helps in predicting the amount of pollutants in a specific region. The system uses historic data in order to predict the value for the new input. The prediction system uses Artificial Neural Networks (ANN) trained with different optimization algorithms to classify the pollution level into several classes. This research paper assesses and analyses various techniques which can be used to predict the level of pollutant in Delhi. This study uses daily mean air temperature, relative humidity, wind speed and concentration of PM2.5 in Anand Vihar area of Delhi for a period of 2 years (2015 to 2016). Experimental results show that a ANN trained with Galactic swarm optimization algorithm produces a more accurate predication compared to other optimization algorithms. © Published under licence by IOP Publishing Ltd.

Geraldine Bessie Amali D

Department of Computational Intelligence

School of Computer Science and Engineering

Vellore Campus

Nigam N

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Pollution level predictor using artificial neural networks trained with galactic swarm optimization algorithms

IOP Conference Series: Materials Science and Engineering

This paper proposes a new global optimization <a href="https://www.sciencedirect.com/topics/computer-science/metaheuristics">metaheuristic</a> called Galactic Swarm Optimization (GSO) inspired by the motion of stars, galaxies and superclusters of galaxies under the influence of gravity. GSO employs multiple cycles of exploration and exploitation phases to strike an optimal trade-off between exploration of new solutions and exploitation of existing solutions. In the explorative phase different subpopulations independently explore the search space and in the exploitative phase the best solutions of different subpopulations are considered as a superswarm and moved towards the best solutions found by the superswarm. In this paper subpopulations as well as the superswarm are updated using the PSO algorithm. However, the GSO approach is quite general and any population based optimization algorithm can be used instead of the PSO algorithm. Statistical test results indicate that the GSO algorithm proposed in this paper significantly outperforms 4 state-of-the-art PSO algorithms and 4 multiswarm PSO algorithms on an overwhelming majority of 15 benchmark <a href="https://www.sciencedirect.com/topics/computer-science/optimisation-problem">optimization problems</a> over 50 independent trials and up to 50 dimensions. Extensive simulation results show that the GSO algorithm proposed in this paper converges faster to a significantly more accurate solution on a wide variety of high dimensional and multimodal benchmark optimization problems.

Applied Soft Computing

Galactic Swarm Optimization: A new global optimization metaheuristic inspired by galactic motion

Journal of Environmental Science, Computer Science and Engineering & Technology

Nonlinear Least Squares Parameter Estimation Problem Using Levenberg-Marquardt Method

Environmental Pollution

Forecasting PM10 in metropolitan areas: Efficacy of neural networks

Atmospheric Environment

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

Environment International

Procedures for estimation of modelling uncertainty in air quality assessment

Journal	Data powered by TypesetIOP Conference Series: Materials Science and Engineering
Publisher	Data powered by TypesetIOP Publishing
ISSN	1757-8981
Open Access	Yes