Modelos Híbridos Aplicados à Construção de Índice de Classificação de Níveis de Risco de Fogo no Brasil

Fire has always exerted a great attraction on humans. Fires generally provide social and environmental impacts at the places where they occur. Several Brazilian localities, especially in the driest months of the year, are more susceptible to this phenomenon. In this paper, an index able of classifying levels of fire risk in areas geographically located in Brazil. This paper presents an index capable of classifying fire risk levels elaborated from neuro-fuzzy systems. Data from the municipality of Sorocaba were used to test the proposed models. The results obtained by this index are promising, reaching values of mean absolute error below 3% when applied in the prediction of the risk of fire for the maximum period of up to 3 days. The proposed index can be used as a tool to support and assist various research agencies or institutes that need to identify the possibility of burning, corroborating the measures to reduce atmospheric emitters and meeting Goal 15 of Agenda 30 as defined by the UN in 2015, which aims to stimulate conservation actions and the recovery and sustainable use of ecosystems.O fogo sempre exerceu grande atração sobre os seres humanos. As queimadas, de maneira geral, proporcionam impactos sociais e ambientais nos locais onde ocorrem. Diversas localidades brasileiras, especialmente nos meses mais secos do ano, estão mais suscetíveis a esse fenômeno. O estudo e o monitoramento do risco do fogo são uma poderosa ferramenta adotada no mapeamento e sensoriamento de áreas afetadas ao longo do território brasileiro e em outras partes do mundo. Este trabalho apresenta um índice para classificar os níveis de risco de fogo, elaborado com base nos sistemas neuro-fuzzy. Dados da cidade de Sorocaba foram utilizados para testar os modelos propostos. Os resultados obtidos mostram-se promissores, alcançando valores referentes à média de erros absolutos abaixo de 3%, aplicados na previsão do risco de queima pelo período máximo de até três dias. O índice proposto poderá ser utilizado como ferramenta de apoio e auxílio a diversos órgãos ou institutos de pesquisa que necessitam identificar a possibilidade de ocorrência de queimadas. Pode, assim, colaborar nas medidas para a redução de emissores atmosféricos, de modo a satisfazer o objetivo 15 da Agenda 30 definido pela Organização das Nações Unidas em 2015, o qual visa estimular ações de conservação, recuperação e uso sustentável de ecossistemas, especialmente

Design and conceptual development of a novel hybrid intelligent decision support system applied towards the prevention and early detection of forest fires

Forest fires have become a major problem that every year has devastating consequences at the environmental level, negatively impacting the social and economic spheres of the affected regions. Aiming to mitigate these terrible effects, intelligent prediction models focused on early fire detection are becoming common practice. Considering mainly a preventive approach, these models often use tools that indifferently apply statistical or symbolic inference techniques. However, exploring the potential for the hybrid use of both, as is already being done in other research areas, is a significant novelty with direct application to early fire detection. In this line, this work proposes the design, development, and proof of concept of a new intelligent hybrid system that aims to provide support to the decisions of the teams responsible for defining strategies for the prevention, detection, and extinction of forest fires. The system determines three risk levels: a general one called Objective Technical Fire Risk, based on machine learning algorithms, which determines the global danger of a fire in some area of the region under study, and two more specific others which indicate the risk over a limited area of the region. These last two risk levels, expressed in matrix form and called Technical Risk Matrix and Expert Risk Matrix, are calculated through a convolutional neural network and an expert system, respectively. After that, they are combined by means of another expert system to determine the Global Risk Matrix that quantifies the risk of fire in each of the study regions and generates a visual representation of these results through a color map of the region itself. The proof of concept of the system has been carried out on a set of historical data from fires that occurred in the Montesinho Natural Park (Portugal), demonstrating its potential utility as a tool for the prevention and early detection of forest fires. The intelligent hybrid system designed has demonstrated excellent predictive capabilities in such a complex environment as forest fires, which are conditioned by multiple factors. Future improvements associated with data integration and the formalization of knowledge bases will make it possible to obtain a standard tool that could be used and validated in real time in different forest areas

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Air pollution forecasts: An overview

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies

Who are the actors and what are the factors that are used in models to map forest fire susceptibility? A systematic review

In the last decades, natural fire regimes have experienced significant alterations in terms of intensity, frequency and severity in fire prone regions of the world. Modelling forest fire susceptibility has been essential in identifying areas of high risk to minimize threats to natural resources, biodiversity and life. There have been significant improvements in forest fire susceptibility modelling over the past two decades 2001–2021. In this study, we conducted a systematic literature review of literature covering forest fire susceptibility modelling published during this period. The review provides insights on the main themes of forest fire susceptibility modelling research, the main base input factors used in models to map forest fire susceptibility, the main researchers, the areas where this type of research were implemented, technology and models used. It also highlights collaboration opportunities, and regions, such as Central America and Africa, where mapping of forest fire susceptibility is needed. We argue that such knowledge is crucial in order to identify critical factors and opportunities which can aid in improving factor selection and forest fire management.Peer Reviewe

A Brief Review of Machine Learning Algorithms in Forest Fires Science

Due to the harm forest fires cause to the environment and the economy as they occur more frequently around the world, early fire prediction and detection are necessary. To anticipate and discover forest fires, several technologies and techniques were put forth. To forecast the likelihood of forest fires and evaluate the risk of forest fire-induced damage, artificial intelligence techniques are a crucial enabling technology. In current times, there has been a lot of interest in machine learning techniques. The machine learning methods that are used to identify and forecast forest fires are reviewed in this article. Selecting the best forecasting model is a constant gamble because each ML algorithm has advantages and disadvantages. Our main goal is to discover the research gaps and recent studies that use machine learning techniques to study forest fires. By choosing the best ML techniques based on particular forest characteristics, the current research results boost prediction power

IoT and Sensor Networks in Industry and Society

The exponential progress of Information and Communication Technology (ICT) is one of the main elements that fueled the acceleration of the globalization pace. Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics are some of the key players of the digital transformation that is affecting every aspect of human's daily life, from environmental monitoring to healthcare systems, from production processes to social interactions. In less than 20 years, people's everyday life has been revolutionized, and concepts such as Smart Home, Smart Grid and Smart City have become familiar also to non-technical users. The integration of embedded systems, ubiquitous Internet access, and Machine-to-Machine (M2M) communications have paved the way for paradigms such as IoT and Cyber Physical Systems (CPS) to be also introduced in high-requirement environments such as those related to industrial processes, under the forms of Industrial Internet of Things (IIoT or I2oT) and Cyber-Physical Production Systems (CPPS). As a consequence, in 2011 the German High-Tech Strategy 2020 Action Plan for Germany first envisioned the concept of Industry 4.0, which is rapidly reshaping traditional industrial processes. The term refers to the promise to be the fourth industrial revolution. Indeed, the first industrial revolution was triggered by water and steam power. Electricity and assembly lines enabled mass production in the second industrial revolution. In the third industrial revolution, the introduction of control automation and Programmable Logic Controllers (PLCs) gave a boost to factory production. As opposed to the previous revolutions, Industry 4.0 takes advantage of Internet access, M2M communications, and deep learning not only to improve production efficiency but also to enable the so-called mass customization, i.e. the mass production of personalized products by means of modularized product design and flexible processes. Less than five years later, in January 2016, the Japanese 5th Science and Technology Basic Plan took a further step by introducing the concept of Super Smart Society or Society 5.0. According to this vision, in the upcoming future, scientific and technological innovation will guide our society into the next social revolution after the hunter-gatherer, agrarian, industrial, and information eras, which respectively represented the previous social revolutions. Society 5.0 is a human-centered society that fosters the simultaneous achievement of economic, environmental and social objectives, to ensure a high quality of life to all citizens. This information-enabled revolution aims to tackle today’s major challenges such as an ageing population, social inequalities, depopulation and constraints related to energy and the environment. Accordingly, the citizens will be experiencing impressive transformations into every aspect of their daily lives. This book offers an insight into the key technologies that are going to shape the future of industry and society. It is subdivided into five parts: the I Part presents a horizontal view of the main enabling technologies, whereas the II-V Parts offer a vertical perspective on four different environments. The I Part, dedicated to IoT and Sensor Network architectures, encompasses three Chapters. In Chapter 1, Peruzzi and Pozzebon analyse the literature on the subject of energy harvesting solutions for IoT monitoring systems and architectures based on Low-Power Wireless Area Networks (LPWAN). The Chapter does not limit the discussion to Long Range Wise Area Network (LoRaWAN), SigFox and Narrowband-IoT (NB-IoT) communication protocols, but it also includes other relevant solutions such as DASH7 and Long Term Evolution MAchine Type Communication (LTE-M). In Chapter 2, Hussein et al. discuss the development of an Internet of Things message protocol that supports multi-topic messaging. The Chapter further presents the implementation of a platform, which integrates the proposed communication protocol, based on Real Time Operating System. In Chapter 3, Li et al. investigate the heterogeneous task scheduling problem for data-intensive scenarios, to reduce the global task execution time, and consequently reducing data centers' energy consumption. The proposed approach aims to maximize the efficiency by comparing the cost between remote task execution and data migration. The II Part is dedicated to Industry 4.0, and includes two Chapters. In Chapter 4, Grecuccio et al. propose a solution to integrate IoT devices by leveraging a blockchain-enabled gateway based on Ethereum, so that they do not need to rely on centralized intermediaries and third-party services. As it is better explained in the paper, where the performance is evaluated in a food-chain traceability application, this solution is particularly beneficial in Industry 4.0 domains. Chapter 5, by De Fazio et al., addresses the issue of safety in workplaces by presenting a smart garment that integrates several low-power sensors to monitor environmental and biophysical parameters. This enables the detection of dangerous situations, so as to prevent or at least reduce the consequences of workers accidents. The III Part is made of two Chapters based on the topic of Smart Buildings. In Chapter 6, Petroșanu et al. review the literature about recent developments in the smart building sector, related to the use of supervised and unsupervised machine learning models of sensory data. The Chapter poses particular attention on enhanced sensing, energy efficiency, and optimal building management. In Chapter 7, Oh examines how much the education of prosumers about their energy consumption habits affects power consumption reduction and encourages energy conservation, sustainable living, and behavioral change, in residential environments. In this Chapter, energy consumption monitoring is made possible thanks to the use of smart plugs. Smart Transport is the subject of the IV Part, including three Chapters. In Chapter 8, Roveri et al. propose an approach that leverages the small world theory to control swarms of vehicles connected through Vehicle-to-Vehicle (V2V) communication protocols. Indeed, considering a queue dominated by short-range car-following dynamics, the Chapter demonstrates that safety and security are increased by the introduction of a few selected random long-range communications. In Chapter 9, Nitti et al. present a real time system to observe and analyze public transport passengers' mobility by tracking them throughout their journey on public transport vehicles. The system is based on the detection of the active Wi-Fi interfaces, through the analysis of Wi-Fi probe requests. In Chapter 10, Miler et al. discuss the development of a tool for the analysis and comparison of efficiency indicated by the integrated IT systems in the operational activities undertaken by Road Transport Enterprises (RTEs). The authors of this Chapter further provide a holistic evaluation of efficiency of telematics systems in RTE operational management. The book ends with the two Chapters of the V Part on Smart Environmental Monitoring. In Chapter 11, He et al. propose a Sea Surface Temperature Prediction (SSTP) model based on time-series similarity measure, multiple pattern learning and parameter optimization. In this strategy, the optimal parameters are determined by means of an improved Particle Swarm Optimization method. In Chapter 12, Tsipis et al. present a low-cost, WSN-based IoT system that seamlessly embeds a three-layered cloud/fog computing architecture, suitable for facilitating smart agricultural applications, especially those related to wildfire monitoring. We wish to thank all the authors that contributed to this book for their efforts. We express our gratitude to all reviewers for the volunteering support and precious feedback during the review process. We hope that this book provides valuable information and spurs meaningful discussion among researchers, engineers, businesspeople, and other experts about the role of new technologies into industry and society

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

In this modern age, Internet of Things (IoT) and Wireless Sensor Network (WSN) as its derivatives have become one of the most popular and important technological advancements. In IoT, all things and services in the real world are digitalized and it continues to grow exponentially every year. This growth in number of IoT device in the end has created a tremendous amount of data and new data services such as big data systems. These new technologies can be managed to produce additional value to the existing business model. It also can provide a forecasting service and is capable to produce decision-making support using computational intelligence methods. In this survey paper, we provide detailed research activities concerning Computational Intelligence methods application in IoT WSN. To build a good understanding, in this paper we also present various challenges and issues for Computational Intelligence in IoT WSN. In the last presentation, we discuss the future direction of Computational Intelligence applications in IoT WSN such as Self-Organizing Network (dynamic network) concept