ASPires: Case-Study of Regional Cooperation and Technology

Forest fires are a persistent global problem, causing devastating consequences such as loss of human lives, harm to the environment, and substantial economic losses. To mitigate these impacts, the accurate prediction and early detection of forest fires is critical. In response to this challenge and living in the digital era of Artificial Intelligence (AI) and smart economies, there has been a growing interest in utilising AI mechanisms for forest fire management. This study provides an in-depth examination of the use of AI algorithms in the fight against forest fires. In particular, our paper starts with an overview of the forest fire problem, followed by a comprehensive review of various systems and approaches. This review includes a thorough analysis of the various works that have evaluated the factors that influence fire occurrence and severity, as well as those that focus on fire prediction and detection systems. The paper also explores the use of AI in adapting and restoring after the occurrence of forest fires. The paper concludes with an evaluation of the potential impact of AI on forest fire management and suggestions for future research directions, taking full advantage of novel technologies, such as 5G communications, Software Defined Networking (SDN), digital twins, federated learning and blockchain. Finally, the paper draws lessons and insights on the potential and limitations of AI in forest fire management, highlighting the need for further research and development in this field to maximise its impact and benefits.

Early Forest Fire Detection and Verification using Optical Smoke, Gas and Microwave Sensors

This paper presents a drone-based early forest fire detection system. Using multiple on-board aerial sensors, thermal images, RGB images, and distance between forest fire points and drones can be captured and determined from the air. To take advantage of data from different sources for forest fire detection and confirmation, both deep learning-based and traditional computer vision algorithms are developed and employed. The on-board computer and ground station computer are designed to work collaboratively according to the different complexity and computational demands of sub-modules in this system. By integrating different sensor data with a two-phase strategy for potential early forest fire detection and confirmation, the proposed system achieves a relatively low false alarm rate and has good robustness in the outdoor real-time early forest fire detection experiments with an on-board computer installed on a DJI M300 drone.

A multi-sign method of laser control of the fire situation in the forest is proposed. In the process of burning in the forest, a significant amount of greenhouse gases and heat is released into the atmosphere. The main gases released are carbon dioxide, methane, nitrogen oxide, and carbon monoxide. An overview of publications on methods of early detection of forest fires is presented. An approach to improvement the sensitivity of fire detection in a forest by means of simultaneously registering all emitted gases is considered. The approach makes use of the differential absorption method for each gas with an integrated laser node emitting at several specially selected wavelengths. The optimization of the proposed method is carried out, as a result of which recommendations are developed for the construction of a highly sensitive system for early detection of forest fires at specified energy consumption.

Automatic environmental monitoring is a field that encompasses several scientific practices for the assessment of risks that may negatively impact a given environment, such as the forest. A forest is a natural environment that hosts various forms of plant and animal life, so preserving the forest is a top priority. To this end, the authors of this paper will focus on the development of an intelligent system for the early detection of forest fires, based on an IoT solution. This latter will thus facilitate the exploitation of the functionalities offered by the Cloud and mobile applications. Detecting and predicting forest fires with accuracy is a difficult task that requires machine learning and an in-depth analysis of environmental conditions. This leads the authors to adopt the forward neural network algorithm by highlighting its contribution through real experiments, performed on the prototype developed in this paper.

The progress in the development of high-performance advanced materials and cost-effective manufacturing technologies is paving the way for large-scale, low-cost fabrication of miniaturized sensors for meteorological and environmental monitoring applications. This paper reviews sensors based on microelectromechanical systems (MEMS) and nanotechnology with great potential for use in wireless sensor networks (WSN) aimed at the prevention and early-stage detection of wildfires.

It is certain that neither regional interdependence nor external push give a fully satisfactory answer to the question of what sustains the Balkans as a regional unit. As noted, different external initiatives have mapped the area in variable ways, though the centre of gravity has been former Yugoslavia. However, South East Europe is used in political discourse to denote a grouping spanning well beyond the confines of the Western Balkans. We have also seen that interdepend­ence typically binds territorially contiguous countries, yet by that token Romania would form a region with neighbouring Hungary or Moldova, rather than with Albania or Bosnia. Turkey and Greece’s security concerns might be interdependent but what, if any, is the common thread that links them to, say, Montenegro’s relations with Serbia and Croatia?

In the past ten years, rates of forest fires around the world have increased significantly. Forest fires greatly affect the ecosystem by damaging vegetation. Forest fires are caused by several causes, including both human and natural causes. Human causes lie in intentional and irregular burning operations. Global warming is a major natural cause of forest fires. The early detection of forest fires reduces the rate of their spread to larger areas by speeding up their extinguishing with the help of equipment and materials for early detection. In this research, an early detection system for forest fires is proposed called Forest Defender Fusion. This system achieved high accuracy and long-term monitoring of the site by using the Intermediate Fusion VGG16 model and Enhanced Consumed Energy-Leach protocol (ECP-LEACH). The Intermediate Fusion VGG16 model receives RGB (red, green, blue) and IR (infrared) images from drones to detect forest fires. The Forest Defender Fusion System provides regulation of energy consumption in drones and achieves high detection accuracy so that forest fires are detected early. The detection model was trained on the FLAME 2 dataset and obtained an accuracy of 99.86%, superior to the rest of the models that track the input of RGB and IR images together. A simulation using the Python language to demonstrate the system in real time was performed.

Early detection of a forest fire can save our flora and fauna. Ad Hoc Wireless Sensor Networks (WSN) plays an important role in detecting forest fire. This article proposes a model for early detection of forest fire through predictive analytics. In this approach, the forest area is divided into different zones. Status of a zone, i.e., High Active (HA), Medium Active (MA), and Low Active (LA), is predicted by applying the semi-supervised classification technique. Each zone has static sensors, mobile sensors, and an Initiator node. Initiator nodes of LA and MA zone transfer their mobile nodes (MN) to the nearer HA zone for the quick prediction of forest fire by using the Random trajectory generation (RTG) technique. This technique generates the intermediate points between LA/MA to HA zone to create the movement path of MN. Compressed sensing based Gradient descent (GD) localization technique is used to track the movement of MN by the anchor nodes. This technique reduces the energy consumption of MN that causes an increase in network lifetime. The analysis of the localization error of MN during its traveling towards the HA zone increases the accuracy of its path detection. Thus the increase of sensor nodes in the HA zone results in transferring a huge amount of data from HA zone to base station for quick prediction of a forest fire.

In recent years, wildfires pose a critical threat to ecosystems, human life, and infrastructure, with early detection being vital for effective mitigation. Traditional fire monitoring systems often suffer from limited coverage, and deep sleep issues, especially in remote forested regions. To overcome these limitations, this research presents a novel low-power wildfire detection framework that combines Ultra-Low-Power coprocessors with Convolutional Neural Networks (ULP-CNN) based aerial video analysis from autonomous drones. The integration of ULP processors continuously monitoring and real-time Artificial Intelligence (AI) video analysis offers a full coverage quick response solution for wildfire prevention. The proposed system begins with the deployment of Long-Range Wide Area Network (LoRaWAN)-enabled sensing nodes equipped with ULP processors for efficient environmental data collection and minimal power draw during sleep cycles. Further, based on detecting abnormal air quality changes, an autonomous drone is dispatched to the suspected fire location. Then, the onboard CNN model analyzes real-time images and video frames to classify fire occurrences and initiate particle filter-based tracking. Experimental evaluation demonstrates that the system achieves high detection accuracy (99.60%), precision (99.89R%), recall (99.74%), and F1 score (99.83%) making it a highly effective solution for real-time detection of wildfires.

Forest fires pose significant threats to both natural ecosystems and human communities due to their unpredictable nature and capacity for widespread destruction. Identifying and mitigating fires in the trunk, ground, and canopy of forests is crucial for reducing their adverse effects on the ecosystem and climate. The detrimental impacts of forest fires, such as the exacerbation of the greenhouse effect, the hastening of global warming, and the modification of climatic patterns, underscore the urgent necessity for the creation of efficient detection systems. This study presents a real-time Universal Trust Model (UTM) that is specifically designed for the early forest fires detection (FFD) using an intelligent wireless sensor network and machine learning approaches. Our method seeks to reduce fire detection time and improve the reliability of the detection process. This is achieved by employing environmental indicators and moisture levels to swiftly identify fires. The intelligent WSN functions by partitioning the forest into suitable clusters and intelligently positioning sensor nodes to guarantee extensive coverage and effective data transmission to the sink. The proposed UTM system’s core component is the computation of trust ratings for every sensor node. These ratings consider communication, energy, and data trust factors to evaluate the reliability of the data being delivered. This integrated trust model enhances the robustness and accuracy of fire detection, especially under difficult environmental conditions. Furthermore, a machine learning regression model is deployed at the base station to augment the precision of fire detection. This is accomplished by examining essential attributes such as temperature, humidity, and CO2 concentrations. We have conducted thorough experiments using actual datasets consisting of 7200 samples to confirm the efficacy of our proposed UTM in detecting forest fires at an early stage. The results suggest that our system obtains a high rate of data processing and a reduced time delay in comparison to existing systems. This renders it a promising solution for the imperative need to promptly detect and prevent forest fires. Our technique combines trust mechanisms with machine learning algorithms to create a very advanced forest fire detection system.

In the age of increasing globalization, transnational education (TNE) allows for higher education opportunities beyond ones' national borders. There are varying models of TNE; Branch campuses, franchise arrangements, joint degree programmes, twinning programmes, and distance delivery – all varying in motivation, impact, resource requirements, and collaboration. This study will examine closely the collaborative provision and international branch campus model by analysing the contextual impact of City College in Thessaloniki and its branches throughout the South East Europe (SEE) region, a collaborative partnership with University of Sheffield established in 1993. Examining the Organisation for Economic Co-Operation and Development (OECD) reports, the study aims to explore the state and needs of the region, and analyse how City College programmes, research, and initiatives align with the regional goals. Following this policy analysis, evidence is presented which determines that the SEE region is in need of quality TNE that offers stability, structure, and quality assurance. Case studies are woven throughout to demonstrate ways in which City College and the University of Sheffield are offering needed opportunities for research and employment, specific to the regional trajectory.

Forest wealth is the biggest proportionality in the environmental sustainability. The forest wealth is mainly endangered by various natural calamities such as flood, hurricane, fire,volcano, land striding and so on. The forest is also threatened with man made damages such as afforestation, wild-trafficking, animal trade, illegal expansions. The human oriented calamities can be controlled with the existence of severe laws and regulations. The natural calamities are uncontrollable in nature. The only phenomena which can be controlled by human is forest fire. This proposed work provides insights on the detection of the forest fire in early stages through satellite imaging and the same is supplied to the nearest monitoring stations. In these stations we apply deep image neural network models such as InceptionV3, VGG-16, Resnet 50 and DenseNet 201 to determine the presence of forest fire or not through binary classification. The results show that the Densenet 201 has the deepest convolutional layer among all these methods, provides the accuracy of 98.46 % by outperforming all other models.

Das Werk versammelt Originalbeiträge von politischen EntscheidungsträgerInnen, führenden WissenschaftlerInnen und PraktikerInnen im Post-Konflikt-Managment aus Europa und den USA. Manche der Autoren wie Madeleine Albright, Carl Bildt, Erhard Busek, Carla Del Ponte, Joschka Fischer, Lord Robertson, Javier Solana aber auch Milan Kucan, Stjepan Mesic, Zeljko Komsic, Dimitrij Rupelj, Goran Svilanovic waren oder sind als Akteure an den historischen Prozessen in Südosteuropa aktiv beteiligt.

Over the last few decades, forest fires are increased due to deforestation and global warming. Many trees and animals in the forest are affected by forest fires. Technology can be efficiently utilized to solve this problem. Forest fire detection is inevitable for forest fire management. The purpose of this work is to propose deep learning techniques to predict forest fires, which would be cost-effective. The mixed learning technique is composed of YOLOv4 tiny and LiDAR techniques. Unmanned aerial vehicles (UAVs) are promising options to patrol the forest by making them fly over the region. The proposed model deployed on an onboard UAV has achieved 1.24 seconds of classification time with an accuracy of 91% and an F1 score of 0.91. The onboard CPU is able to make a 3D model of the forest fire region and can transmit the data in real time to the ground station. The proposed model is trained on both dense and rainforests in detecting and predicting the chances of fire. The proposed model outperforms the traditional methods such as Bayesian classifiers, random forest, and support vector machines.