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Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands, Leiden University, Leiden and The Netherlands

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4 Papers

Temporal Transfer Learning for Ozone Prediction based on CNN-LSTM Model

Topics: Machine Learning; Big Data; Deep Learning

Authors: Tuo Deng ¹ , Astrid Manders ² , Arjo Segers ² , Yanqin Bai ³ and Hai Xiang Lin ¹

Affiliations: ¹ Delft Institute of Applied Mathematics, Delft University of technology, Mekelweg 4, 2628 CD Delft, The Netherlands , ² TNO, Climate Air and Sustainability, Utrecht, 3584 CB, The Netherlands , ³ Department of Mathematics, Shanghai University, Shanghai 200444, China

Digital Object Identifier: 10.5220/0010301710051012

Proceedings of ICAART 2021, ISBN: 978-989-758-484-8

Quick Abstract

Tropospheric ozone is a secondary pollutant which can affect human health and plant growth. In this paper, we investigated transferred convolutional neural network long short-term memory (TL-CNN-LSTM) model to predict ozone concentration. Hourly CNN-LSTM model is used to extract features and predict ozone for next hour, which is superior to commonly used models in previous studies. In the daily ozone prediction model, prediction over a large time-scale requires more data, however, only limited data are available, which causes the CNN-LSTM model to fail to accurately predict. Network-based transfer learning methods based on hourly models can obtain information from smaller temporal resolution. It can reduce prediction errors and shorten run time for model training. However, for extreme cases where the amount of data is severely insufficient, transfer learning based on smaller time scale cannot improve model prediction accuracy.

Air Quality Forecast through Integrated Data Assimilation and Machine Learning

Topics: Machine Learning; Model-Based Reasoning; Industrial Applications of AI; Big Data; Data Science

Authors: Hai Xiang Lin ¹ , Jianbing Jin ² and Jaap van den Herik ³

Affiliations: ¹ Delft Institute of Applied Mathematics, Delft University of Technology, Delft, The Netherlands, Leiden University, Leiden and The Netherlands , ² Delft Institute of Applied Mathematics, Delft University of Technology, Delft and The Netherlands , ³ Leiden University, Leiden and The Netherlands

Digital Object Identifier: 10.5220/0007555207870793

Proceedings of ICAART 2019, ISBN: 978-989-758-350-6

Quick Abstract

Numerical models of chemical transport have been used to simulate the complex processes involved in the formation and transport of air pollutants. Although these models can predict the spatiotemporal variability of a variety of chemical species, the accuracy of these models is often limited. Therefore, in the past two decades, data assimilation methods have been applied to use the available measurements for improving the forecast. Nowadays, machine learning techniques provide new opportunities for improving the air quality forecast. A case study on PM10 concentrations during a dust storm is performed. It is known that the PM10 concentrations are caused by multiple emission sources, e.g., dust from desert and anthropogenic emissions. An accurate modeling of the PM10 concentration levels owing to the local anthropogenic emissions is essential for an adequate evaluation of the dust level. However, real-time measurement of local emissions is not possible, so no direct data is available. Actually, the lack of in-time emission inventories is one of the main reasons that current numerical chemical transport models cannot produce accurate anthropogenic PM10 simulations. Using machine learning techniques to generate local emissions based on real-time observations is a promising approach. We report how it can be combined with data assimilation to improve the accuracy of air quality forecast considerably.

Related Ontology Subjects/Areas/Topics (13)

Artificial Intelligence	Evolutionary Computing	Machine Learning
Big Data	Industrial Applications of AI	Model-Based Reasoning
Computational Intelligence	Knowledge Discovery and Information Retrieval	Soft Computing
Data Engineering	Knowledge-Based Systems	Symbolic Systems
Data Management and Quality

Visibility Forecast for Airport Operations by LSTM Neural Network

Topics: Neural Networks; Machine Learning; Big Data; Deep Learning

Authors: Tuo Deng ¹ , Aijie Cheng ¹ , Wei Han ² and Hai-Xiang Lin ³

Affiliations: ¹ School of Mathematics, Shandong University, Jinan, Shandong, 250100 and China , ² Numerical Weather Prediction Center of China Meteorological Administration, Beijing, 100081 and China , ³ Delft Institute of Applied Mathematics, Delft University of Technology, Mekelweg 4, 2628 CD Delft and Netherlands

Digital Object Identifier: 10.5220/0007308204660473

Proceedings of ICAART 2019, ISBN: 978-989-758-350-6

Quick Abstract

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.

Related Ontology Subjects/Areas/Topics (24)

Artificial Intelligence	Health Engineering and Technology Applications	Neurotechnology, Electronics and Informatics
Big Data	Human-Computer Interaction	Pattern Recognition
Biomedical Engineering	Knowledge Discovery and Information Retrieval	Physiological Computing Systems
Biomedical Signal Processing	Knowledge-Based Systems	Sensor Networks
Computational Intelligence	Machine Learning	Signal Processing
Data Engineering	Methodologies and Methods	Soft Computing
Data Management and Quality	Neural Networks	Symbolic Systems
Evolutionary Computing	Neurocomputing	Theory and Methods

Nonparametric Bayesian Line Detection - Towards Proper Priors for Robotic Computer Vision

Topics: Bayesian Networks; Vision and Perception; Pattern Recognition; Exact and Approximate Inference; Bayesian Models; Regression

Authors: Anne C. van Rossum ¹ , Hai Xiang Lin ² , Johan Dubbeldam ³ and H. Jaap van den Herik ⁴

Affiliations: ¹ Almende B. V. and Distributed Organisms B. V. (DoBots) and Leiden University, Netherlands , ² Delft University of Technology and Leiden University, Netherlands , ³ Delft University of Technology, Netherlands , ⁴ Leiden University, Netherlands

Digital Object Identifier: 10.5220/0005673301190127

Proceedings of ICPRAM 2016, ISBN: 978-989-758-173-1

Quick Abstract

In computer vision there are many sophisticated methods to perform inference over multiple lines, however they are quite ad-hoc. In this paper a fully Bayesian approach is used to fit multiple lines to a point cloud simultaneously. Our model extends a linear Bayesian regression model to an infinite mixture model and uses a Dirichlet process as a prior for the partition. We perform Gibbs sampling over non-unique parameters as well as over clusters to fit lines of a fixed length, a variety of orientations, and a variable number of data points. The performance is measured using the Rand Index, the Adjusted Rand Index, and two other clustering performance indicators. This paper is mainly meant to demonstrate that general Bayesian methods can be used for line estimation. Bayesian methods, namely, given a model and noise, perform optimal inference over the data. Moreover, rather than only demonstrating the concept as such, the first results are promising with respect to the described clustering performance indicators. Further research is required to extend the method to inference over multiple line segments and multiple volumetric objects that will need to be built on the mathematical foundation that has been laid down in this paper.

Related Ontology Subjects/Areas/Topics (21)

Artificial Intelligence	Enterprise Information Systems	Pattern Recognition
Artificial Intelligence and Decision Support Systems	Exact and Approximate Inference	Physiological Computing Systems
Bayesian Models	Health Engineering and Technology Applications	Regression
Bayesian Networks	Human-Computer Interaction	Sensor Networks
Biomedical Engineering	Methodologies and Methods	Soft Computing
Biomedical Signal Processing	Neurocomputing	Theory and Methods
Data Manipulation	Neurotechnology, Electronics and Informatics	Vision and Perception