Neural network-based metamodelling approach for estimation of air pollutant profiles

University of Technology, Sydney. Faculty of Engineering and Information Technology.The air quality system is a system characterised by non-linear, complex relationships. Among existing air pollutants, the ozone (O3), known as a secondary pollutant gas, involves the most complex chemical reactions in its formation, whereby a number of factors can affect its concentration level. To assess the ozone concentration in a region, a measurement method can be implemented, albeit only at certain points in the region. Thus, a more complicated task is to define the spatial distribution of the ozone level across the region, in which the deterministic air quality model is often used by the authority. Nevertheless, simulation by using a deterministic model typically needs high computational requirements due to the nonlinear nature of chemical reactions involved in the model formulation, which is also subject to uncertainties. In the context of ozone as an air pollutant, the determination of the background ozone level (BOL), independent from human activities, is also important as it could represent one of reliable references to human health risk assessment. The concept of BOL may be easily understood, but practically, it is hard to distinguish between natural and anthropogenic effects. Apart from existing approaches to the BOL determination, a new quantisation method is presented in this work, by evaluating the relationship of ozone versus nitric oxide (O3-NO) to estimate the BOL value, mainly by using night-time and early morning measurement data collected at the monitoring stations. In this thesis, to deal with the challenging problem of air pollutant profile estimation, a metamodel approach is suggested to adequately approximate intrinsically nonlinear and complex input-output relationships with significantly less computation. The intrinsic characteristics of the underlying physics are not assumed to be known, while the system’s input and output behaviours remain essential. A considerable number of metamodels approach have been proposed in the literature, e.g. splines, neural networks, kriging and support vector machine. Here, the radial basis function neural network (RBFNN) is concerned as it is known to offer good estimation performance on accuracy, robustness, versatility, sample size, efficiency, and simplicity as compared to other stochastic approaches. The development requirements are that the proposed metamodels should be capable of estimating the ozone profiles and its background level temporally and spatially with reasonably good accuracies, subject to satisfying some statistical criteria. Academic contributions of this thesis include in a number of performance enhancements of the RBFNN algorithms. Generally, three difficulties involved in the network training, selection of radial basis centres, selection of the basis function variance (i.e. spread parameter), and training of network weights. The selection of those parameters is very crucial, as they directly affect the number of hidden neurons used and also the network overall performance. In this research, some improvements of the typical RBFNN algorithm (i.e. orthogonal least squares) are achieved. First, an adaptively-tuned spread parameter and a pruning algorithm to optimise the network’s size are proposed. Next, a new approach for training the RBFNN is presented, which involves the forward selection method for selecting the radial basis centres. Also, a method for training the network output weights is developed, including some suggestions for estimation of the best possible values of the network parameters by considering the cross-validation approach. For applications, results show that the combination of the proposed paradigm could offer a sub-optimal solution of metamodelling development in the generic sense (by avoiding the iteration process) for a faster computation, which is essential in air pollutant profile estimation

Neural network-based meta-modelling approach for estimating spatial distribution of air pollutant levels

Continuous measurements of the air pollutant concentrations at monitoring stations serve as a reliable basis for air quality regulations. Their availability is however limited only at locations of interest. In most situations, the spatial distribution beyond these locations still remains uncertain as it is highly influenced by other factors such as emission sources, meteorological effects, dispersion and topographical conditions. To overcome this issue, a larger number of monitoring stations could be installed, but it would involve a high investment cost. An alternative solution is via the use of a deterministic air quality model (DAQM), which is mostly adopted by regulatory authorities for prediction in the temporal and spatial domain as well as for policy scenario development. Nevertheless, the results obtained from a model are subject to some uncertainties and it requires, in general, a significant computation time. In this work, a meta-modelling approach based on neural network evaluation is proposed to improve the estimated spatial distribution of the pollutant concentrations. From a dispersion model, it is suggested that the spatially-distributed pollutant levels (i.e. ozone, in this study) across a region under consideration is a function of the grid coordinates, topographical information, solar radiation and the pollutant's precursor emission. Initially, for training the model, the input-output relationship is extracted from a photochemical dispersion model called The Air Pollution Model and Chemical Transport Model (TAPM-CTM), and some of those input-output data are correlated with the ambient measurements collected at monitoring stations. Here, improved radial basis function networks, incorporating a proposed technique for selection of the network centres, will be developed and trained by using the data obtained and the forward selection approach. The methodology is then applied to estimate the ozone concentrations in the Sydney basin, Australia. Once executed, apart from the advantage of inexpensive computation, it provides more reliable results of the estimation and offers better predictions of ozone concentrations than those obtained by using the TAPM-CTM model only, when compared to the measurement data collected at monitoring stations. © 2013 Elsevier B.V. All rights reserved

Addressing the complexity of sustainability-driven structural design: Computational design, optimization, and decision making

Being one of the sectors with the largest environmental burden and high socio-economic impacts sets high requirements on the construction industry. At the same time, this provides the sector with great opportunities to contribute to the globally pursued sustainability transition. To cope with the increasing need for infrastructure and, at the same time, limit their sustainability impacts, changes and innovation in the construction sector are required. The greatest possibility to limit the sustainability impact of construction works is at the early design phase of construction projects, as many of the choices influencing sustainability are made at that point. Traditionally, an early choice of a preferred design is often made based on limited knowledge and past experience, considering only a handful of options. This preferred design is then taken on to the successive stages in the stepwise design process, leading to suboptimization.Alternatively, many different design choices could be considered and evaluated in a more holistic approach in order to find the most sustainable design for a particular application. However, finding design solutions that offer the best sustainability performance and fulfil all structural, performance and buildability requirements, require methods that allow considering different design options, analysing them, and assessing their sustainability. The aim of this thesis is to explore and develop methods enabling structural engineers to take sustainability objectives into account in the design of structures. Throughout this thesis, a number of methods have been explored to take sustainability aspects into account in the structural design process. As a first step, highly parameterized computer codes for sustainability-driven design have been developed. These codes interoperate with FE analysis software to automatically model and analyse design concepts over the whole design space and verify compliance with structural design standards. The codes were complemented with a harmonized method for life cycle sustainability performance assessment, in line with the state-of-the-art standards. Here, sustainability criteria were defined covering environmental, social, economic, buildability and structural performance for multi-criteria assessment of design concepts. To identify the most sustainable designs within the set, multi-objective optimization algorithms were used. Algorithms that address the high expense of constraint function evaluations of structural design problems were developed and integrated in the parameterized computer codes for sustainability-driven design. To ensure the applicability and validity of these methods, case studies based on real-world projects and common structural engineering problems were used in this thesis. Case studies for bridges and wind turbine foundations as well as a benchmark case of a reinforced concrete beam were investigated.The case studies highlight the potential of the methods explored to support the design of more sustainable structures, as well as the applicability of the methods in structural engineering practice. It is concluded that it is possible and beneficial to combine computational design, life cycle sustainability assessment, and multi-objective design optimization as a basis for decision making in the design phase of civil engineering projects. A wide adoption of such a sustainability-driven design optimization approach in structural engineering practice can directly improve the sustainability of the construction sector

A review of model designs

The PAEQANN project aims to review current ecological theories which can help identify suited models that predict community structure in aquatic ecosystems, to select and discuss appropriate models, depending on the type of target community (i.e. empirical vs. simulation models) and to examine how results add to ecological water management objectives. To reach these goals a number of classical statistical models, artificial neural networks and dynamic models are presented. An even higher number of techniques within these groups will tested lateron in the project. This report introduces all of them. The techniques are shortly introduced, their algorithms explained, and the advantages and disadvantages discussed

Improving the robustness, accuracy, and utility of chemistry-climate model ensembles

Ensembles of chemistry-climate models (CCMs) are fundamental for the exploration of the chemistry-climate system. A particular focus of chemistry-climate modelling is stratospheric ozone, whose concentrations have been decreased by anthropogenic releases of ozone depleting substances. In conjunction with observational data, CCM ensembles have been relied upon to simulate historic effects of ozone depletion and to project future ozone recovery. However, many widely used ensemble analysis methods are simplistic and are based upon incorrect assumptions about the design of the ensemble. Multi-model means used to construct future ozone projections do not account for variable model performance or similarity and therefore give biased and inaccurate projections. Similarly, simplistic linear regression methods used to infill historic ozone records underestimate interannual variability and are inaccurate in regions of sparse data coverage. Moreover, given advances in machine learning and data science and their increased use in environmental science, it is timely to apply more advanced tools to CCM ensembles. To address this methodological deficit, this thesis presents a set of novel tools to improve the predictions and projections from CCM ensembles of stratospheric ozone. A process-based weighted mean is developed which accounts for model performance and similarity in CCM ensembles. This improvement over pre-existing methods was used to generate accurate ozone hole recovery projections. This thesis also developed a Bayesian neural network (BNN) which fuses together CCMs with observational data to produce accurate and uncertainty-aware predictions. The BNN framework was used to produce historic continuous datasets of total ozone column and vertically resolved ozone, and represents a significant improvement in methods used to ensemble models. Though designed for CCM ensembles these flexible tools have the potential to be applied to other environmental modelling disciplines to improve the accuracy of projections, better understand uncertainty and to make better use of historic observations

Framework for data quality in knowledge discovery tasks

Actualmente la explosión de datos es tendencia en el universo digital debido a los avances en las tecnologías de la información. En este sentido, el descubrimiento de conocimiento y la minería de datos han ganado mayor importancia debido a la gran cantidad de datos disponibles. Para un exitoso proceso de descubrimiento de conocimiento, es necesario preparar los datos. Expertos afirman que la fase de preprocesamiento de datos toma entre un 50% a 70% del tiempo de un proceso de descubrimiento de conocimiento. Herramientas software basadas en populares metodologías para el descubrimiento de conocimiento ofrecen algoritmos para el preprocesamiento de los datos. Según el cuadrante mágico de Gartner de 2018 para ciencia de datos y plataformas de aprendizaje automático, KNIME, RapidMiner, SAS, Alteryx, y H20.ai son las mejores herramientas para el desucrimiento del conocimiento. Estas herramientas proporcionan diversas técnicas que facilitan la evaluación del conjunto de datos, sin embargo carecen de un proceso orientado al usuario que permita abordar los problemas en la calidad de datos. Adem´as, la selección de las técnicas adecuadas para la limpieza de datos es un problema para usuarios inexpertos, ya que estos no tienen claro cuales son los métodos más confiables. De esta forma, la presente tesis doctoral se enfoca en abordar los problemas antes mencionados mediante: (i) Un marco conceptual que ofrezca un proceso guiado para abordar los problemas de calidad en los datos en tareas de descubrimiento de conocimiento, (ii) un sistema de razonamiento basado en casos que recomiende los algoritmos adecuados para la limpieza de datos y (iii) una ontología que representa el conocimiento de los problemas de calidad en los datos y los algoritmos de limpieza de datos. Adicionalmente, esta ontología contribuye en la representacion formal de los casos y en la fase de adaptación, del sistema de razonamiento basado en casos.The creation and consumption of data continue to grow by leaps and bounds. Due to advances in Information and Communication Technologies (ICT), today the data explosion in the digital universe is a new trend. The Knowledge Discovery in Databases (KDD) gain importance due the abundance of data. For a successful process of knowledge discovery is necessary to make a data treatment. The experts affirm that preprocessing phase take the 50% to 70% of the total time of knowledge discovery process. Software tools based on Knowledge Discovery Methodologies offers algorithms for data preprocessing. According to Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms, KNIME, RapidMiner, SAS, Alteryx and H20.ai are the leader tools for knowledge discovery. These software tools provide different techniques and they facilitate the evaluation of data analysis, however, these software tools lack any kind of guidance as to which techniques can or should be used in which contexts. Consequently, the use of suitable data cleaning techniques is a headache for inexpert users. They have no idea which methods can be confidently used and often resort to trial and error. This thesis presents three contributions to address the mentioned problems: (i) A conceptual framework to provide the user a guidance to address data quality issues in knowledge discovery tasks, (ii) a Case-based reasoning system to recommend the suitable algorithms for data cleaning, and (iii) an Ontology that represent the knowledge in data quality issues and data cleaning methods. Also, this ontology supports the case-based reasoning system for case representation and reuse phase.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Fernando Fernández Rebollo.- Secretario: Gustavo Adolfo Ramírez.- Vocal: Juan Pedro Caraça-Valente Hernánde