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

14th International Conference on Science, Engineering and Technology

自然科学;工业技术

Nigam, Nilay^1 ; Geraldine Bessie Amali, D.^1

School of Computer Science and Engineering, VIT University, Vellore

632014, India^1

关键词: Historic data;    Mean air temperatures;    Optimization algorithms;    Pollutant levels;    Pollution level;    Prediction systems;    Research papers;    Swarm optimization algorithms;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042093/pdf
DOI  :  10.1088/1757-899X/263/4/042093

来源: IOP

【 摘 要 】

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.