Extracting temporal patterns from smart city data

Mestrado de dupla diplomação com a DULAY UNIVERSITYIn the modern world data and information become a powerful instrument of management, business, safety, medicine and others. The most fashionable sciences are the sciences which allow us to extract valuable knowledge from big volumes of information. Novel data processing techniques remains a trend for the last five years, in a way that continues to provide interesting results. This paper investigates the algorithms and approaches for processing smart city data, in particular, water consumption data for the city of Bragança, Portugal. Data from the last seven years was processed according to a rigorous methodology, that includes five stages: cleaning, preparation, exploratory analysis, identification of patterns and critical interpretation of the results. After understanding the data and choosing the best algorithms, a web-based data visualizing tools was developed, providing dashboards to geospatial data representation, useful in the decision making of municipalities.В современном мире данные и информация стали одним из самых мощных инстру- ментов в управлении, бизнесе, безопасности, медицине, науке и социальной сфере. Са- мыми модными и востребованными науками в настоящий момент являются науки, поз- воляющие извлекать полезные знания из больших объемов информации. Новые методы обработки данных остаются тенденцией последних пяти лет и продолжают генерировать интересные результаты. В данной работе исследуются алгоритмы и подходы для обработ-ки данных "умного города", в частности, данных о потреблении воды в городе Браганса, Португалия. Данные за последние семь лет обрабатывались в соответствии со строгой методологией, включающей пять этапов: очистка, подготовка, исследовательский анализ, выявление закономерностей и критическая интерпретация результатов. Цель исследова-ниия - определение шаблонов поведения в потрблении воды связанных с определенными событиями и построение модели прогнозова на основе найденных закономерностей. В результате исчерпывающего анализа с помощью множества методов было установлено отсутствие систематических зависимостей в рассматриваемом типе данных. На заключи-тельном этапе был создан инструмент визуализации данных, обеспечивающий динами-ческие панели для представления аналитических данных о распределении потребления. Разработанный инструмент управления аналитикой полезен для принятия решений му-ниципалитетом

Novel Computationally Intelligent Machine Learning Algorithms for Data Mining and Knowledge Discovery

This thesis addresses three major issues in data mining regarding feature subset selection in large dimensionality domains, plausible reconstruction of incomplete data in cross-sectional applications, and forecasting univariate time series. For the automated selection of an optimal subset of features in real time, we present an improved hybrid algorithm: SAGA. SAGA combines the ability to avoid being trapped in local minima of Simulated Annealing with the very high convergence rate of the crossover operator of Genetic Algorithms, the strong local search ability of greedy algorithms and the high computational efficiency of generalized regression neural networks (GRNN). For imputing missing values and forecasting univariate time series, we propose a homogeneous neural network ensemble. The proposed ensemble consists of a committee of Generalized Regression Neural Networks (GRNNs) trained on different subsets of features generated by SAGA and the predictions of base classifiers are combined by a fusion rule. This approach makes it possible to discover all important interrelations between the values of the target variable and the input features. The proposed ensemble scheme has two innovative features which make it stand out amongst ensemble learning algorithms: (1) the ensemble makeup is optimized automatically by SAGA; and (2) GRNN is used for both base classifiers and the top level combiner classifier. Because of GRNN, the proposed ensemble is a dynamic weighting scheme. This is in contrast to the existing ensemble approaches which belong to the simple voting and static weighting strategy. The basic idea of the dynamic weighting procedure is to give a higher reliability weight to those scenarios that are similar to the new ones. The simulation results demonstrate the validity of the proposed ensemble model

Assessing Atmospheric Pollution and Its Impacts on the Human Health

This reprint contains articles published in the Special Issue entitled "Assessing Atmospheric Pollution and Its Impacts on the Human Health" in the journal Atmosphere. The research focuses on the evaluation of atmospheric pollution by statistical methods on the one hand, and on the other hand, on the evaluation of the relationship between the level of pollution and the extent of its effect on the population's health, especially on pulmonary diseases

Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

Machine Learning based Forecasting Systems for Worldwide International Tourists Arrival

The international tourist movement has overgrown in recent decades, and travelers are considered a significant source of income to the tourism economy. When tourists visit a place, they spend considerable money on their enjoyment, travel, and hotel accommodations. In this research, tourist data from 2010 to 2020 have been extracted and extended with depth analysis of different dimensions to identify valuable features. This research attempts to use machine learning regression techniques such as Support Vector Regression (SVR) and Random Forest Regression (RFR) to forecast and predict worldwide international tourist arrivals and achieved forecasting accuracy using SVR is 99.4% and using RFR is 84.7%. The study also analyzed the forecasting deadlock condition after covid-19 in the sudden drop of international visitors due to lockdown enforcement by all countries.N/

Tourism demand forecasting – a review on the variables and models

With the growth of the world's tourism industry, researchers took advantage to conduct numerous studies in forecasting of tourism demand. The objective of this paper is to review the studies on tourism demand starting from 2010 to 2018 which varies on the explanatory variables, such as tourist income, exchange rate, gross domestic product, and others. In addition, this study also reviewed the models used to forecast and analyse tourism demand which are time-series model, econometric causal model and artificial intelligence model. The result from this review shows it is difficult to conclude which models performed the best for tourism demand. However, in most of the studies, combined models outperformed single model. Furthermore, the authors mentioned about the roles of tourism practitioners in the industry, tourism seasonality and suggestions for further studies in the future

Proceedings of the GIS Research UK 18th Annual Conference GISRUK 2010

This volume holds the papers from the 18th annual GIS Research UK (GISRUK). This year the conference, hosted at University College London (UCL), from Wednesday 14 to Friday 16 April 2010. The conference covered the areas of core geographic information science research as well as applications domains such as crime and health and technological developments in LBS and the geoweb. UCL’s research mission as a global university is based around a series of Grand Challenges that affect us all, and these were accommodated in GISRUK 2010. The overarching theme this year was “Global Challenges”, with specific focus on the following themes: * Crime and Place * Environmental Change * Intelligent Transport * Public Health and Epidemiology * Simulation and Modelling * London as a global city * The geoweb and neo-geography * Open GIS and Volunteered Geographic Information * Human-Computer Interaction and GIS Traditionally, GISRUK has provided a platform for early career researchers as well as those with a significant track record of achievement in the area. As such, the conference provides a welcome blend of innovative thinking and mature reflection. GISRUK is the premier academic GIS conference in the UK and we are keen to maintain its outstanding record of achievement in developing GIS in the UK and beyond

A SWOT analysis for offshore wind energy assessment using remote-sensing potential

The elaboration of a methodology for accurately assessing the potentialities of blue renewable energy sources is a key challenge among the current energy sustainability strategies all over the world. Consequentially, many researchers are currently working to improve the accuracy of marine renewable assessment methods. Nowadays, remote sensing (RSs) satellites are used to observe the environment in many fields and applications. These could also be used to identify regions of interest for future energy converter installations and to accurately identify areas with interesting potentials. Therefore, researchers can dramatically reduce the possibility of significant error. In this paper, a comprehensive SWOT (strengths, weaknesses, opportunities and threats) analysis is elaborated to assess RS satellite potentialities for offshore wind (OW) estimation. Sicily and Sardinia-the two biggest Italian islands with the highest potential for offshore wind energy generation-were selected as pilot areas. Since there is a lack of measuring instruments, such as cup anemometers and buoys in these areas (mainly due to their high economic costs), an accurate analysis was carried out to assess the marine energy potential from offshore wind. Since there are only limited options for further expanding the measurement over large areas, the use of satellites makes it easier to overcome this limitation. Undoubtedly, with the advent of new technologies for measuring renewable energy sources (RESs), there could be a significant energy transition in this area that requires a proper orientation of plans to examine the factors influencing these new technologies that can negatively affect most of the available potential. Satellite technology for identifying suitable areas of wind power plants could be a powerful tool that is constantly increasing in its applications but requires good planning to apply it in various projects. Proper planning is only possible with a better understanding of satellite capabilities and different methods for measuring available wind resources. To this end, a better understanding in interdisciplinary fields with the exchange of updated information between different sectors of development, such as universities and companies, will be most effective. In this context, by reviewing the available satellite technologies, the ability of this tool to measure the marine renewable energies (MREs) sector in large and small areas is considered. Secondly, an attempt is made to identify the strengths and weaknesses of using these types of tools and techniques that can help in various projects. Lastly, specific scenarios related to the application of such systems in existing and new developments are reviewed and discussed