Air pollution prediction using LSTM deep learning and metaheuristics algorithms

Abstract

Air pollution is a leading cause of health concerns and climate change, one of humanity's most dangerous problems. This problem has been exacerbated by an overabundance of automobiles, industrial output pollution, transportation fuel consumption, and energy generation. As a result, air pollution forecasting has become vital. As a result of the large amount and variety of data acquired by air pollution monitoring stations, air pollution forecasting has become a popular topic, particularly when applying deep learning models of long short-term memory (LSTM). The ability of these models to learn long-term dependencies in air pollution data sets them apart. However, LSTM models using many other statistical and machine learning approaches may not offer adequate prediction results due to noisy data and improper hyperparameter settings. As a result, to define the pollution levels for a group of contaminants, an ideal representation of the LSTM is required. To address the problem of identifying the best hyperparameters for the LSTM model, In this paper, we propose a model based on the Genetic Algorithm (GA) algorithm as well as the long short-term memory (LSTM) deep learning algorithm. The model aims to find the best hyperparameters for LSTM and the pollution level for the next day using four types of pollutants PM10, PM2.5, CO, and NOX. The proposed model modified by optimization algorithms shows more accurate results with less experience and more speed than machine learning models and LSTM models.