# Breathable Cities: Dynamic Machine Learning Modelling Approaches for Advanced Air Pollution Control
## 2 Related Work
### Systematic Reviews  
Existing research has clearly observed the preponderance of certain algorithms in the field of AQP, and it is thought that usage and discovery of other algorithms could create more paths for more accuracy. Moreover, the existing literature suggested that there are limitations associated with including certain pollutants in predictions. Time series forecasting using artificial neural networks methodologies: A systematic review' [2] studied new ANN models (developed during the period 2006-2016) and presented evidence that the predictions of hybrid models were more accurate than those of traditional ANN models (such as back-propagation with single hidden layers), despite the lack of a systematic process for hybrid model development. The study explored some new models in terms of architecture, complexity, relevant variable selection, parameters estimation, and implementation and evaluation, and recommended more research to specify criteria for relevant variable selections (i.e., the basis for selection), methodological development for the selection of ANN architectures, the creation of evaluation models, and a methodology that tests the generalization of models. Machine learning approaches for outdoor air quality modeling: A systematic review' [10] analyzed 46 papers to determine why some algorithms are selected over others in prediction. It addressed the main need for ML-based statistical models to overcome the limitations of deterministic techniques, to model non-linear relationships between concentrations with required accuracy, and provided details about algorithms and how they are applied to enhance accuracy (principles of algorithms). Its main findings showed that estimation problems usually apply ensemble learning and regressions, forecasting problems mostly arise due to NNs and SVMs, and challenges exist for improving peak prediction and contaminants (such as nanoparticles). The authors claimed that ML research is mainly undertaken in Europe and North America and chiefly focuses on the estimation of pollutants' concentration (using ensemble learning and regression analysis) and forecasting problems (using NN and SVM), which gives priority to accuracy over interpretability. Estimation is more precise than forecasting; hence, forecasting is more variable. More complex methods, such as deep learning, are needed to accommodate the complexity of predicting air pollution ahead of time (days or hours), although such complex methods have the drawbacks of being very computationally demanding. The study emphasized the suitability of ML to predict air quality, and traditional deterministic methods showed complexity in modeling fine PM, while ML approaches (estimation and forecasting) showed high accuracy relative to other emission gases; however, lower precision is noted for peak values. The study reported that accuracy is higher for medium and small peaks than high concentrations of pollutants (high peaks), while forecasting for some gases such as CO and NOx is limited in terms of performance. The assessed models showed better performance in peak weather conditions. The study suggested future directions, developing models that enhance pollution peak prediction and models that improve critical pollutants such as CO and NOx. Machine learning algorithms to forecast air quality: A survey' [11] reviewed 155 publications and demonstrated a direct correlation between the most polluted and the most studied countries, noting an increasing trend in the number of ML models used for pollution studies. For the studied pollutant measures, nearly half of the studied papers used AQI, and for air pollutant concentration, 54 papers showed that PM2.5 is the most predicted. The most used pollutant features were weather variables. In terms of ML techniques, DL methods were more widely used than regression algorithms, and hybrid algorithms include both types. Specifically, the most used algorithms were LSTM and MLP, while CNN, RNN, GRU, and auto-encoders were used less commonly.