Data-driven disaster management in a smart city

Disasters, both natural and man-made, are extreme and complex events with consequences that translate into a loss of life and/or destruction of properties. The advances in IT and Big Data analysis represent an opportunity for the development of resilient environments once the application of analytical methods allows extracting information from a significant amount of data, optimizing the decision-making processes. This research aims to apply the CRISP-DM methodology to extract information about incidents that occurred in the city of Lisbon with emphasis on occurrences that affected buildings, constituting a tool to assist in the management of the city. Through this research, it was verified that there are temporal and spatial patterns of occurrences that affected the city of Lisbon, with some types of occurrences having a higher incidence in certain periods of the year, such as floods and collapses that occur when there are high levels of precipitation. On the other hand, it was verified that the downtown area of the city is the area most affected by occurrences. Finally, machine learning models were applied to the data and the predictive model Random Forest obtained the best result with an accuracy of 58%.info:eu-repo/semantics/publishedVersio

A self-parametrization framework for meta-heuristics

Even while the scientific community has shown great interest in the analysis of meta-heuristics, the analysis of their parameterization has received little attention. It is the parameterization that will adapt a meta-heuristic to a problem, but it is still performed, mostly, empirically. There are multiple parameterization techniques; however, they are time-consuming, requiring considerable computational effort and they do not take advantage of the meta-heuristics that they parameterize. In order to approach the parameterization of meta-heuristics, in this paper, a self-parameterization framework is proposed. It will automatize the parameterization as an optimization problem, precluding the user from spending too much time on parameterization. The model will automate the parameterization through two meta-heuristics: A meta-heuristic of the solution space and one of the parameter space. To analyze the performance of the framework, a self-parameterization prototype was implemented. The prototype was compared and analyzed in a SP (scheduling problem) and in the TSP (traveling salesman problem). In the SP, the prototype found better solutions than those of the manually parameterized meta-heuristics, although the differences were not statistically significant. In the TSP, the self-parameterization prototype was more effective than the manually parameterized meta-heuristics, this time with statistically significant differences.This work was supported by national funds through the FCT - Fundação para a Ciência e Tecnologia through the R&D Units Project Scopes: UIDB/00319/2020, and EXPL/EME-SIS/1224/2021

Special issue: Innovations in farming systems approaches: Introduction

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PG.SGA (cotada) como indicador de prognóstico em oncologia

[Resumo][Abstract

Evaluation of the Simulated Annealing and the Discrete Artificial Bee Colony in the Weight Tardiness Problem with Taguchi Experiments Parameterization

Meta-Heuristics (MH) are the most used optimization techniques to approach Complex Combinatorial Problems (COPs). Their ability to move beyond the local optimums make them an especially attractive choice to solve complex computational problems, such as most scheduling problems. However, the knowledge of what Meta-Heuristics perform better in certain problems is based on experiments. Classic MH, as the Simulated Annealing (SA) has been deeply studied, but newer MH, as the Discrete Artificial Bee Colony (DABC) still need to be examined in more detail. In this paper DABC has been compared with SA in 30 academic benchmark instances of the weighted tardiness problem (1 parallel to Sigma w(j)T(j)). Both MH parameters were fine-tuned with Taguchi Experiments. In the computational study DABC performed better and the subsequent statistical study demonstrated that DABC is more prone to find near-optimum solutions. On the other hand SA appeared to be more efficient.This work is supported by FEDER Funds through the "Programa Operacional Factores de Competitividade - COMPETE" program and by National Funds through FCT "Fundacao para a Ciencia e a Tecnologia" under the project: PEst-OE/EEI/UI0760/2014, and PEst2015-2020.info:eu-repo/semantics/publishedVersio

Study on the Impact of the NS in the Performance of Meta-Heuristics in the TSP

Meta-heuristics have been applied for a long time to the Travelling Salesman Problem (TSP) but information is still lacking in the determination of the parameters with the best performance. This paper examines the impact of the Simulated Annealing (SA) and Discrete Artificial Bee Colony (DABC) parameters in the TSP. One special consideration of this paper is how the Neighborhood Structure (NS) interact with the other parameters and impacts the performance of the meta-heuristics. NS performance has been the topic of much research, with NS proposed for the best-known problems, which seem to imply that the NS influences the performance of meta-heuristics, more that other parameters. Moreover, a comparative analysis of distinct meta-heuristics is carried out to demonstrate a non-proportional increase in the performance of the NS.This work is supported by FEDER Funds through the "Programa Operacional Factores de Competitividade - COMPETE" program and by National Funds through FCT "FundaqAo para a Ciencia e a Tecnologia" under the project: FCOMP-01-0124-FEDER-PEst-OE/EEl/U10760/2011, PEst-OE/EEI/UI0760/2014, and PEst2015-2020.info:eu-repo/semantics/publishedVersio

Data fusion and visualization towards city disaster management: Lisbon case study

INTRODUCTION: Due to the high level of unpredictability and the complexity of the information requirements, disaster management operations are information demanding. Emergency response planners should organize response operations efficiently and assign rescue teams to particular catastrophe areas with a high possibility of surviving. Making decisions becomes more difficult when the information provided is heterogeneous, out of date, and often fragmented. OBJECTIVES: In this research work a data fusion of different information sources and a data visualization process was applied to provide a big picture about the disruptive events in a city. This high-level knowledge is important for emergency management authorities. This holistic process for managing, processing, and analysing the seven Vs (Volume, Velocity, Variety, Variability, Veracity, Visualization, and Value) in order to generate actionable insights for disaster management. METHODS: A CRISP-DM methodology over smart city-data was applied. The fusion approach was introduced to merge different data sources. RESULTS: A set of visual tools in dashboards were produced to support the city municipality management process. Visualization of big picture based on different data available is the proposed work. CONCLUSION: Through this research, it was verified that there are temporal and spatial patterns of occurrences that affected the city of Lisbon, with some types of occurrences having a higher incidence in certain periods of the year, such as floods and collapses that occur when there are high levels of precipitation. On the other hand, it was verified that the downtown area of the city is the most affected area.info:eu-repo/semantics/publishedVersio

Learning about safety, prevention and quality of life through PBL: implications for teacher education

In Problem-Based Learning (PBL) students learn ‘new’ knowledge by solving problems. Studies focusing on the efficacy of PBL for the learning science content knowledge are rare and their results are not fully consistent. This study aims at: comparing the effectiveness of a transdisciplinary PBL and traditional teaching with regard to students’ learning of science knowledge within the scope of the theme Safety, Prevention and Quality of Life; finding out students’ opinions on transdisciplinary PBL approach. The sample is made of two 9th grade classes of a school located in the north of Portugal. The experimental class (24 students) approached the theme through PBL in an integrated way that is, Natural Sciences and Physical Sciences teachers pooled together the concepts that they were supposed to teach and organized PBL oriented teaching as if those concepts belonged to a single school subject. The control class (25 students) studied the same theme through traditional teaching, with the concepts of each school subject addressed separately by each one of the teachers. Data relative to content learning were collected by means of a pre- and a post-test and data relative to PBL students’ opinions on the new teaching approach were collected through an opinion questionnaire. Results indicate that transdisciplinary PBL led to a bit better results than traditional teaching and that students valued PBL.ERDF -European Regional Development Fund(PTDC/CPE-CED/108197/2008)info:eu-repo/semantics/publishedVersio