# Hybrid LSTM Model to Predict the Level of Air Pollution in Montenegro
## 1 Introduction  
Air pollution is a concerning global issue, with approximately 1.3 million annual deaths attributed to it, according to the World Health Organization (WHO) [1]. Air quality assessment plays a vital role in monitoring and managing pollution levels. WHO data reveal that air pollution exceeding the recommended limits affects nearly the entire global population (99%), with a significant impact in low- and middle-income countries. It is crucial to anticipate and prepare for fluctuations in pollution levels to effectively mitigate the adverse effects of air pollution. Improving air quality not only enhances public health but also contributes to mitigating climate change, as air quality is closely interconnected with our planet's climate and the health of its ecosystems. By reducing air pollution, we can alleviate the burden of diseases associated with air pollution and make long-term contributions to climate change mitigation efforts. Since 2005, the Common Air Quality Index (CAQI) has been employed in Europe as a comprehensive and standardized metric to evaluate and communicate air quality levels to the general public [2]. It provides a simplified and easily understandable representation ofair pollution levels, making it easier for individuals to make informed decisions regarding their health and well-being. The CAQI is based on the measurement of several air pollutants that are known to have detrimental effects on human health, including particulate matter (\(PM_{2.5}\) and \(PM_{10}\)), nitrogen dioxide (\(NO_{2}\)), ozone (\(O_{3}\)), carbon monoxide (\(CO\)), and sulfur dioxide (\(SO_{2}\)) [3]. These pollutants are commonly monitored by air quality monitoring stations located in various regions. The CAQI is designed to provide a numerical value or color-coded scale that corresponds to the air quality level. Typically, the CAQI scale ranges from 0 to 100 and it is divided into several categories, such as very low, low, medium, high, and very high [4], and the visual color scale is presented from green to red. To calculate the CAQI value, individual pollutant concentrations are first converted into indexes using predefined equations that are based on value interpolation. These indexes are then combined, weighted, and transformed into a single CAQI value. The weighting factors assigned to each pollutant are determined based on their relative health impacts. The CAQI is a valuable tool in terms of raising awareness about air pollution and its potential health risks. It enables policymakers, environmental agencies, and the general public to monitor and address air quality issues effectively. Additionally, the CAQI facilitates the comparison of air quality between different locations and allows for long-term trend analysis, aiding in the formulation of targeted strategies for air pollution control and mitigation. The advancement of machine learning (ML) techniques, including deep learning, has opened up new opportunities to enhance air quality research [5]. Among these techniques, the Support Vector Machine (SVM) has demonstrated promising outcomes in diverse domains. As a supervised learning algorithm, SVM is designed in the manner that it can identify optimal hyperplanes to enable the formation of data classes. In the context of air pollution prediction, SVM can learn complex patterns and relationships from historical pollution data and meteorological variables [6]. On the other hand, LSTM represents a type of recurrent neural network known for its effectiveness in modeling sequential data [7]. It can capture long-term dependencies and temporal patterns, making it suitable for time series forecasting tasks such as air pollution prediction. The hybridization of ML algorithms with other techniques yields good results, especially when it comes to metaheuristic algorithms. Hybridization allows the faster convergence of algorithms and increases the prediction accuracy of ML algorithms.