Development of an Intelligent System for Predicting the Forest Fire Development Based on Convolutional Neural Networks

Stankevich, Tatiana S.

doi:10.1007/978-3-030-39162-1_1

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…In this review, we report only the methods based on deep learning, as presented in Table 5. Stankevich [158] describes the process of an intelligent system to predict wildfire spread, avoiding state-of-the-art challenges such as low forecast performance, computational cost and time, and limited functionality in uncertain and unsteady conditions. Various data were used as inputs: satellite images collected from several sources: fire propagation data obtained from the NASA FIRMS resource management system [159]; environment data including air temperature, window speed, and humidity; forest vegetation data obtained from the European Space Agency Climate Change Initiative's global annual Land Cover Map [160]; and weather data from Ventusky InMeteo [161].…”

Section: Deep Learning-based Approaches For Fire Spread Prediction Us...mentioning

confidence: 99%

Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction

Ghali

Akhloufi

2023

Fire

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Wildland fires are one of the most dangerous natural risks, causing significant economic damage and loss of lives worldwide. Every year, millions of hectares are lost, and experts warn that the frequency and severity of wildfires will increase in the coming years due to climate change. To mitigate these hazards, numerous deep learning models were developed to detect and map wildland fires, estimate their severity, and predict their spread. In this paper, we provide a comprehensive review of recent deep learning techniques for detecting, mapping, and predicting wildland fires using satellite remote sensing data. We begin by introducing remote sensing satellite systems and their use in wildfire monitoring. Next, we review the deep learning methods employed for these tasks, including fire detection and mapping, severity estimation, and spread prediction. We further present the popular datasets used in these studies. Finally, we address the challenges faced by these models to accurately predict wildfire behaviors, and suggest future directions for developing reliable and robust wildland fire models.

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Section: Deep Learning-based Approaches For Fire Spread Prediction Us...mentioning

confidence: 99%

Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction

Ghali

Akhloufi

2023

Fire

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“…Stankevich [44] proposed to develop an intelligent system for predicting wildland fire based on artificial intelligence and deep computer-aided learning. Machine learning technology is used for smoke and fire recognition in the images using optical cameras [45].…”

Section: Literature Reviewmentioning

confidence: 99%

Rating Wildfire Danger by Big Weather Data and Machine Learning: A Case Study in Eastern China

Gao

et al. 2022

Preprint

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Wildfire, as a natural phenomenon, shapes global ecosystems and threatens human communities. The meteorological conditions of the forest environment is one of the critical factors, which exists obvious effect on the risk of wildfire for a given landscape and fuel type. It is high challenge to predict wildland fire risks owing to the huge amount of meteorological paramenters with volume,variety,value and velocity. Driven by the emerging Artificial Intelligence and Big Data analytics, this work proposes a machine learning model (Fuzzy C-Means algorithm) to assess the probability of wildland fire. By training the historical weather and fire data in Eastern China (Jiangsu Province), the complex relationships between the weather parameter and the rating of wildland fire danger are quantified. The Fire Weather Index system are applied to evaluate the accuracy of predictions and compare them with the standards in the literature. This system further reveals that the spring season is the peak period for wildland fires in Eastern China. Such a prediction is also in accordance with the practice of wildfire occurrence that, in practice, can provide early warning to local residences and forest services.

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“…BA[77,86,90,149,172,[180][181][182][183][184][185][186][187][188][189] Big Data[29,45,145,181,[190][191][192][193][194][195] Figure6. Number of studies found versus the problem to be solved.…”

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confidence: 99%

Decision Support System Development of Wildland Fire: A Systematic Mapping

Vásquez

Cravero

Castro

et al. 2021

Forests

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Wildland fires have been a rising problem on the worldwide level, generating ecological and economic losses. Specifically, between wildland fire types, uncontrolled fires are critical due to the potential damage to the ecosystem and their effects on the soil, and, in the last decade, different technologies have been applied to fight them. Selecting a specific technology and Decision Support Systems (DSS) is fundamental, since the results and validity of this could drastically oscillate according to the different environmental and geographic factors of the terrain to be studied. Given the above, a systematic mapping was realized, with the purpose of recognizing the most-used DSS and context where they have been applied. One hundred and eighty-three studies were found that used different types of DSS to solve problems of detection, prediction, prevention, monitoring, simulation, administration, and access to routes. The concepts key to the type of solution are related to the use or development of systems or Information and Communication Technologies (ICT) in the computer science area. Although the use of BA and Big Data has increased in recent years, there are still many challenges to face, such as staff training, the friendly environment of DSS, and real-time decision-making.

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Development of an Intelligent System for Predicting the Forest Fire Development Based on Convolutional Neural Networks

Cited by 5 publications

References 16 publications

Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction

Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction

Rating Wildfire Danger by Big Weather Data and Machine Learning: A Case Study in Eastern China

Decision Support System Development of Wildland Fire: A Systematic Mapping

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