Cities foster economic growth. However, growing cities also contribute to air pollution and climate change. The paper provides a perspective regarding the opportunity available in addressing the urban air quality management (UAQM) issues using smart city framework in the context of ‘urban computing’. Traditionally, UAQM has been built on sparse regulatory monitoring, enhanced with satellite data and forecast models. The ‘Fourth Industrial Revolution’ (4IR) technologies such as Internet of Things (IoT), big data, artificial intelligence, smartphones, social and cloud computing are reshaping urban conglomerates, worldwide. Cities can harness these ubiquitous technologies in concert with traditional methods for betterment of air quality governance and to improve quality of life. This paper discusses the role of urban computing in UAQM through a review of scientific publications and ‘grey literature’ from technical reports of governments, international organizations and institutional websites. It provides an interdisciplinary knowledge repository on urban computing applications for air quality functions. It highlights the potential of integrated technologies in enabling data driven, strategic and real-time mitigation governance actions and helping citizens to take informed decisions. It recommends ‘fit for the purpose’ multitechnology framework for UAQM services in emerging smart cities.

Review of urban computing in air quality management as smart city service: An integrated IoT, AI, and cloud technology perspective

Visibility classification and influencing-factors analysis of airport: A deep learning approach

The path planning of a ship requires much information, and one of the essential factors is predicting the ocean environment. Ocean weather can generally be gathered from forecasting information provided by weather centers. However, these data are difficult to obtain when satellite communication is unstable during voyages, or there are cases where forecast data for a more extended period of time are needed for the operation of the fleet. Therefore, shipping companies and classification societies have attempted to establish a model for predicting the ocean weather on its own. Historically, ocean weather has been primarily predicted using empirical and numerical methods. Recently, a method for predicting ocean weather using deep learning has emerged. In this study, a deep learning model combining a denoising AutoEncoder and convolutional long short-term memory (LSTM) was proposed to predict the ocean weather worldwide. The denoising AutoEncoder is effective for removing noise that hinders the training of deep learning models. While the LSTM could be used as time-series inputs at specific points, the convolutional LSTM can use time-series images as inputs, making them suitable for predicting a wide range of ocean weather. Herein, using the proposed model, eight parameters of ocean weather were predicted. The proposed learning model predicted ocean weather after one week, showing an average error of 6.7%. The results show the applicability of the proposed learning model for predicting ocean weather.

https://www.mdpi.com/2077-1312/8/10/805/pdf

Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM

In this study, ground observation data were selected from January 2016 to January 2020. First, six machine learning methods were used to predict visibility. We verified the accuracy of the method with and without principal components analysis (PCA) by combining actual examples with the European Centre for Medium-Range Weather Forecast (ECMWF) data and National Centers for Environmental Prediction (NECP) data. The results show that PCA can improve visibility prediction. Neural networks have high accuracy in machine learning algorithms. The initial visibility data plays an important role in the visibility forecast and can effectively improve forecast accuracy.

/pdf/visibility-prediction-based-on-machine-learning-algorithms-vektujw5.pdf

Visibility Prediction Based on Machine Learning Algorithms

Atmospheric visibility conditions not only affect traffic on roads, but also aviation operations. Poor visibility at the destination site can reduce airport capacity leading to ground delays, flight cancellations, flight diversions, and extra operating costs. Hence, timely forecast of visibility is important for safe operation in both airports and highways. Visibility is affected by meteorological weather variables such as precipitation, temperature, wind speed, humidity, smoke, fog, mist, and Particulate Matter (PM) concentrations in the atmosphere. This paper is an effort to forecast univariate weather variable visibility and explore the effect of highly correlated meteorological weather variables on visibility, using an Auto Regressive Recurrent Neural Network (ARRNN). By adjusting the number of epochs and the regression horizon, i.e. past time steps used in visibility prediction, we showed that ARRNN outperforms long-short term memory (LSTM) networks and vanilla recurrent neural network (Vanilla RNN) in terms of coefficient of determination (R2).

Forecasting Atmospheric Visibility Using Auto Regressive Recurrent Neural Network

Machine learning based bias correction for numerical chemical transport models

Abstract. With the explosive growth of atmospheric data, machine learning models have achieved great success in air pollution forecasting because of their higher computational efficiency than the traditional chemical transport models. However, in previous studies, new prediction algorithms have only been tested at stations or in a small region; a large-scale air quality forecasting model remains lacking to date. Huge dimensionality also means that redundant input data may lead to increased complexity and therefore the over-fitting of machine learning models. Feature selection is a key topic in machine learning development, but it has not yet been explored in atmosphere-related applications. In this work, a regional feature selection-based machine learning (RFSML) system was developed, which is capable of predicting air quality in the short term with high accuracy at the national scale. Ensemble-Shapley additive global importance analysis is combined with the RFSML system to extract significant regional features and eliminate redundant variables at an affordable computational expense. The significance of the regional features is also explained physically. Compared with a standard machine learning system fed with relative features, the RFSML system driven by the selected key features results in superior interpretability, less training time, and more accurate predictions. This study also provides insights into the difference in interpretability among machine learning models (i.e., random forest, gradient boosting, and multi-layer perceptron models).


/pdf/development-of-a-regional-feature-selection-based-machine-2xmdfeod.pdf

Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China

Ground-level ozone is a critical atmospheric pollutant, and high concentrations of ozone can damage human health, affect plant growth and cause ecological harm. Traditional chemical transport models and popular machine learning models have difficulty in predicting ozone concentrations, especially in times with high concentrations. We proposes a clustering-based spatial transfer learning Multilayer Perceptron (SPTL-MLP) to predict ozone concentration at the target observation station for the next three days. We use k-means clustering algorithm to find similar stations and train them together to get a base model for spatial transfer learning. For practical applications, a weighted loss function has been designed with an extra emphasis on reducing prediction errors of high ozone concentrations. Evaluation using historical data of stations in Germany shows that our SPTL-MLP model has a smaller error (reduced by 9.13%) and higher prediction accuracies of ozone exceedances (improved by 8.21% and 16.9%) compared to MLP (without spatial transfer). The results demonstrate the effectiveness of the SPTL-MLP in the short-term ozone forecast. It can be used for timely warning of ozone exceedances and help governments to detect air quality. 

Clustering-based spatial transfer learning for short-term ozone forecasting

Visibility forecast is a meteorological problems which has direct impact to daily lives. For instance, timely prediction of low visibility situations is very important for the safe operation in airports and highways. In this paper, we investigate the use of Long Short-Term Memory(LSTM) model to predict visibility. By adjusting the loss function and network structure, we optimize the original LSTM model to make it more suitable for practical applications, which is superior to previous models in short-term low visibility prediction. In addition, there is a ”time delay problem” when the number of hours time ahead we try to forecast becomes larger, this problem is persistent given the limited amount of available training data. We report our attempt of applying re-sampling to deal with the time delay problem, and we find that this method can improve the accuracy of visibility prediction, especially for the low visibility case.

Visibility Forecast for Airport Operations by LSTM Neural Network

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Papers

Machine learning based bias correction for numerical chemical transport models

Development of a regional feature selection-based machine learning system (RFSML v1.0) for air pollution forecasting over China

Clustering-based spatial transfer learning for short-term ozone forecasting

Visibility Forecast for Airport Operations by LSTM Neural Network