Air quality forecasting using neural networks

In this thesis project, a special type of neural network: Extreme Learning Machine (ELM) is implemented to predict the air quality based on the air quality time series itself and the external meteorological records. A regularized version of ELM with linear components is chosen to be the main model for prediction. To take full advantage of this model, its hyper-parameters are studied and optimized. Then a set of variables is selected (or constructed) to maximize the performance of ELM, where two different variable selection methods (i.e. wrapper and filtering methods) are evaluated. The wrapper method ELM-based forward selection is chosen for the variable selection. Meanwhile, a feature extraction method (Principal Component Analysis) is implemented in the hope of reducing the candidate meteorological variables for feature selection, which proves to be helpful. At last, with all the parameters being properly optimized, ELM is used for the prediction and generates satisfying results

A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile

Air quality time series consists of complex linear and non-linear patterns and are difficult to forecast. Box-Jenkins Time Series (ARIMA) and multilinear regression (MLR) models have been applied to air quality forecasting in urban areas, but they have limited accuracy owing to their inability to predict extreme events. Artificial neural networks (ANN) can recognize non-linear patterns that include extremes. A novel hybrid model combining ARIMA and ANN to improve forecast accuracy for an area with limited air quality and meteorological data was applied to Temuco, Chile, where residential wood burning is a major pollution source during cold winters, using surface meteorological and PM10 measurements. Experimental results indicated that the hybrid model can be an effective tool to improve the PM10 forecasting accuracy obtained by either of the models used separately, and compared with a deterministic MLR. The hybrid model was able to capture 100% and 80% of alert and pre-emergency episodes, respectively. This approach demonstrates the potential to be applied to air quality forecasting in other cities and countries

Data Mining In the Prediction of Impacts of Ambient Air Quality Data Analysis in Urban and Industrial Area

Air pollution caused due to the introduction of particulate matters, biological molecules and other harmful materials into the Earth's atmosphere. Pollution brings vital diseases, death to humans, damages other living organisms such as vegetations, animals, natural environment and built environment. Data mining concerned with finding hidden patterns inside largely available data, so that the information retrieved can be transformed into usable knowledge. The Air Quality Index is an indicator of air quality standards around Chennai. It is based on air pollutants that have bad effects on human health and the environment. Growing use of vehicles in the city and growing industrial activities on the outskirt of city cause more air pollution. The problem of air pollution is becoming a major concern for the health of the population. The ambient air quality data collected from Central Pollution Control Board and Tamil Nadu Pollution Control Board ambient air quality data available in the websites. Air quality is monitored by air quality monitoring stations in Chennai through the use of wireless sensors deployed in huge numbers around the city and industrial areas. The four years of data from the year 2012 to 2015 are collected from various monitoring stations and processed. Data mining tool is used for the prediction, forecasting and support in making effective decision. Artificial Neural Network model in Data mining techniques analyzed the data using Feed Forward Neural Networks and Multilayer Perceptron neural network models. The pattern obtained from these models could serve as an important reference for the Government policy makers in devising future air pollution standard policies

Air Travel Itinerary Market Share Estimation

In air travel, an itinerary is a direct flight or sequence of connecting flights between two cities. The objective of itinerary market share estimation is to forecast market shares of competing itineraries. This paper examines and compares three different methods for itinerary market share estimation: multinomial logit models, artificial neural networks, and a custom model developed by the authors. Using real-world booking data, each model is constructed and calibrated to best reproduce the given data. The resulting models are applied to test data and the custom model was found to show the best results. Although multinomial logit model are used by many airlines for planning and forecasting purposes, such methods resulted in the lowest forecasting quality

Urban ozone concentration forecasting with artificial neural network in Corsica

Atmospheric pollutants concentration forecasting is an important issue in air quality monitoring. Qualitair Corse, the organization responsible for monitoring air quality in Corsica (France) region, needs to develop a short-term prediction model to lead its mission of information towards the public. Various deterministic models exist for meso-scale or local forecasting, but need powerful large variable sets, a good knowledge of atmospheric processes, and can be inaccurate because of local climatical or geographical particularities, as observed in Corsica, a mountainous island located in a Mediterranean Sea. As a result, we focus in this study on statistical models, and particularly Artificial Neural Networks (ANN) that have shown good results in the prediction of ozone concentration at horizon h+1 with data measured locally. The purpose of this study is to build a predictor to realize predictions of ozone and PM10 at horizon d+1 in Corsica in order to be able to anticipate pollution peak formation and to take appropriated prevention measures. Specific meteorological conditions are known to lead to particular pollution event in Corsica (e.g. Saharan dust event). Therefore, several ANN models will be used, for meteorological conditions clustering and for operational forecasting.Comment: Sustainable Solutions for Energy and Environment. EENVIRO 2013, Buchatrest : Romania (2013

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features