Multidimensional Advances in Wildfire Behavior Prediction: Parameter Construction, Model Evolution and Technique Integration

The systems for vegetation (including forest) fire behavior prediction in the USA and Canada are analyzed. The conclusion is drawn about the complexity of their use in other countries due to natural differences and historically established different approaches to the classification of vegetation. Russia has all the prerequisites to develop a system for fire behavior prediction. Developed are guidelines for improving forest fire danger rating and fire hazard assessment; classification of vegetation fuels and methods of their mapping; a registered software for automatized vegetation fuel mapping; an example of a map for the nature reserve Stolby. A fire spread model is selected based on the availability of the input data. A fire behavior prediction software program is developed to predict spread of tactical fire parts over the area, fire intensity, development of the fire (from the surface fire to the crown or ground one) and immediate fire effects. In addition, the program allows you to calculate the manpower and means for fire suppression. The results of a retrospective software performance test are given on the example of the nature reserve Stolby. The software performance test is planned to be carried out on active fires with the participation of forest fire protection experts.

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) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildfire science and management. Our overall objective is to improve awareness of ML methods among wildfire researchers and managers, as well as illustrate the diverse and challenging range of problems in wildfire science available to ML data scientists. To that end, we first present an overview of popular ML approaches used in wildfire science to date and then review the use of ML in wildfire science as broadly categorized into six problem domains, including (i) fuels characterization, fire detection, and mapping; (ii) fire weather and climate change; (iii) fire occurrence, susceptibility, and risk; (iv) fire behavior prediction; (v) fire effects; and (vi) fire management. Furthermore, we discuss the advantages and limitations of various ML approaches relating to data size, computational requirements, generalizability, and interpretability, as well as identify opportunities for future advances in the science and management of wildfires within a data science context. In total, to the end of 2019, we identified 300 relevant publications in which the most frequently used ML methods across problem domains included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. As such, there exists opportunities to apply more current ML methods — including deep learning and agent-based learning — in the wildfire sciences, especially in instances involving very large multivariate datasets. We must recognize, however, that 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 such as deep learning requires a dedicated and sophisticated knowledge of their application. Finally, we stress that the wildfire research and management communities play an active role in providing relevant, high-quality, and freely available wildfire data for use by practitioners of ML methods.

We summarized research progress of forest fire occurrence prediction model in China based on the literature review, from the prospects of forest fire drivers, models of forest fire occurrence probability, models of forest fire occurrence frequency and model validation methods. The main conclusions are: 1) Meteorology, terrain, vegetation, fuel and human activities were the main driving factors of forest fire occurrence and model prediction accuracy. 2) In the models of forest fire occurrence probability, the geographically weighted logistic regression model considered the spatial correlation between model variables, the Gompit regression model could fit the asymmetric structure fire data. The random forest algorithm had a high prediction accuracy without the requirement of multicollinearity test and excessive fitting, which made it as one of the optimal methods of forest fire occurrence probability prediction. 3) Among all the forest fire occurrence frequency models, the negative binomial regression model was suitable for fitting the over discrete data, the zero-inflated model and hurdle model could deal with fire data that contained a large number of zeros. 4) ROC test, AIC test, likelihood ratio test, and Wald test were the most common methods for evaluating the accuracy of fire occurrence probability and frequency models. The study of forest fire occurrence prediction model should be the main focus of the forest fire management. Model selection should base on fire data structure of different forests. More influencing factors should be taken into account to improve the prediction accuracy of model. In addition, it was necessary to further explore the application of other mathematical methods in forest fire prediction, to improve the accuracy of the models.

By summarizing the development history, development status and technology integration of BIM technology, this paper comprehensively analyzes the research progress and application status of BIM technology in the field of construction, and predicts the research hotspot and future development trend of BIM technology in China; At the same time, it systematically introduces the specific application of BIM technology from the design stage, construction stage, operation and maintenance stage, and summarizes the application scenario and application status of BIM technology combined with specific cases. It provides reference for the subsequent research and application of BIM technology.

Fire behavior prediction with artificial intelligence in thinned black pine (Pinus nigra Arnold) stand

Based on the automatic machine learning framework, combined with the characteristics of forest fire meteorological data and the adaptive requirements of forest fire prediction, this paper optimizes the data preprocessing, parameter learning, loss function and other links of auto-sklearn, builds a forest fire risk prediction framework with regional adaptive characteristics. Based on the forest meteorological fire risk data, a forest fire risk prediction model with regional characteristics and self-learning characteristics is constructed to solve the problems of low compatibility of the existing machine learning methods with binary unbalanced forest fire data, improve the accuracy of forest fire prediction and provide decision-making basis for forestry risk management. The comparative analysis results show that the prediction accuracy of the improved framework in different test sets is improved by 13% on average. Compared with the existing machine learning model for forest fire prediction, the prediction accuracy of the framework proposed in this paper is comprehensively better than the existing methods in terms of real forest fire data.

Intelligent System - AI as a service” , is a project to predict the occurrence of forest fire.Forest fires pose a significant threat to both the environment and human safety. Timely prediction of forest fires is crucial for effective mitigation and prevention efforts. To enhance real-time monitoring and early warning capabilities, the system incorporates remote sensing data using machine learning techniques. Fires are now causing an additional 3 million hectares (7.5 million acres) of tree cover loss per year than they did in 2001, according to a newly released Global Forest Watch analysis that examined fires that burn all or most of a forest’s living overstory trees. The majority of all fire-caused tree cover loss in the past 20 years (nearly 70%) occurred in boreal regions. Although fires are naturally occurring there, they are now increasing at an annual rate of 3% and burning with greater frequency and severity and over larger areas than historically recorded. Forest fires pose significant threats to ecosystems, human lives, and property. Early prediction and mitigation of forest fires are crucial to reducing their destructive impact. This abstract presents an overview of a forest fire prediction system that leverages modern technology and data-driven approaches. The proposed forest fire prediction system utilizes a combination of remote sensing data, weather information, historical fire data, and machine learning algorithms to forecast the likelihood of forest fires in specific regions. Advanced techniques such as deep learning and ensemble models are employed to analyze the complex interactions between various environmental factors that contribute to fire ignition and spread. Fires are not naturally occurring in tropical rainforests, but in recent years, as deforestation and climate change have degraded and dried out intact forests, fires have been escaping into standing tropical rainforests. GFW findings suggest fires in the tropics have increased by roughly 5% per year since 2001.

Forest fires, severe natural disasters causing substantial damage, necessitate accurate predictive modeling to guide preventative measures effectively. This study introduces an enhanced window-based Transformer time series forecasting model aimed at improving the precision of forest fire predictions. Leveraging time series data from 2020 to 2021 in Chongli, a myriad of forest fire influencing factors were ascertained using remote sensing satellite and GIS technologies, with their interrelationships estimated through a multicollinearity test. Given the intricate nature of real-world forest fire prediction tasks, we propose a novel window-based Transformer architecture complemented by a dual time series input strategy premised on 13 influential factors. Subsequently, time series data were incorporated into the model to generate a forest fire risk prediction map in Chongli District. The model’s effectiveness was then evaluated using various metrics, including accuracy (ACC), root mean square error (RMSE), and mean absolute error (MAE), and compared with traditional deep learning methods. Our model demonstrated superior predictive performance (ACC = 91.56%, RMSE = 0.37, MAE = 0.05), harnessing spatial background information efficiently and effectively utilizing the periodicity of forest fire factors. Consequently, the study proves this method to be a novel and potent approach for time series fire prediction.

Forests, one of the most valuable and necessary resources and protect earth’s ecological balance, are a natural habitat to animals and forest products are vital in our lives in many direct and indirect ways. But wildfires can cause critical damage to grounds and many other resources like properties, human life, wildlife in superabundant amounts. Wildfires burn acres of land and destroy everything in their paths in mere minutes. Wildfire destroys homes, animals, trees and plants, wildlife as well as vegetation. The effects of wildfires are numerous and wide-ranging. it causes a hugely significant impact on the economy, environment, heritage and social fabric of rural areas. Naturally caused wildfires can be predicted using factors[3] like temperature, humidity, soil moisture, pressure and many more. In this paper, the prediction of forest fires by machine learning using some operational monitoring over a region and encountering changes in climate using different sensors are advocated. The Wildfire Prediction System (WiPreSy) monitors and records changes in climatic parameters and predicts the intensity of forest fire based on real-time data, thus avoiding the massive loss due to forest fires.

Summary Nomographs (computational graphics) that calculate the fire spread rate, fireline intensity and flamelength of fires burning in Mediterranean wildlands were created, by using the BEHAVE fire behaviour prediction system. BEHAVE simulated fires spreading, under a wide range of environmental parameters, in fuel models representative of dominant Mediterranean vegetation types: two for broadleaved-evergreen shrublands (maquis), one for Kermes oak shrublands, two for phrygana (garigue), one for grasslands and two for the litter of pine and oak forests. Eight nomographs were produced covering a wide and diversified range of potential fire behaviour. These nomographs present the novelty that their fire predictions are dependent on fluctuations of the windspeed rather than live fuel moisture, taking into consideration that wind is the dominant factor affecting fire behaviour in Mediterranean climates. Additionally, they calculate fireline intensity, a parameter directly related to fire severity. Nomographs can be useful in fire management by providing fast and reliable fire behaviour predictions in prevention planning or in decision-making during actual fire suppression.

In recent years, forest fires have increased drastically due to global warming. Forest fire prediction is the best way to control the spread of fire. Therefore, several studies have focused on developing models that predict the behavior of forest fires. Predicting fire spread and its behavior is crucial to mitigate the adverse effects on weather conditions, environment, and human activities. Improving forest fire prediction using higher quality data can be expensive. In some cases, obtaining or even precise estimation of these data is difficult. On the other hand, using prediction models are more reasonable and feasible to increase prediction accuracy. In this paper, we introduced a novel Belief-Desire-Intention (BDI) agent-based model to predict the behavior of forest fires in the Mazandaran region in the north of Iran. This paper attempted to map the concepts of BDI agent architecture into generic GIS. A novel BDI-GIS model was then proposed in which an agent’s belief, desire, and intention were defined based on spatial or non-spatial data and GIS functions. Therefore, an agent-based model was developed to determine the prediction of forest fires and implemented it on a real dataset. The experimental results showed that the proposed model could be successfully applied to the real-world scenarios with a Kappa Coefficient of more than 68.2%.

At present, data security has become a key area of attention from all aspects. The proliferation of data leakage incidents, the acceleration of the introduction of data security laws and regulations, and the rising cost of enterprise compliance punishment jointly urge all enterprises to step up to improve their own data security system. In the construction of data security, data desensitization is always a core technical ability. In view of the current situation and future development trend of data desensitization technology and products, this paper introduces the development of data desensitization technology in detail from desensitization algorithm, anonymization, static data desensitization and dynamic data desensitization. At the same time, from the core function, product form, supply side status and other aspects of the data desensitization technology are described. On this basis, the standardization direction of data desensitization products in terms of core functions, concept definition, performance and stability is proposed. Finally, the development trend of data desensitization technology and products in four aspects of regularization, anonymization, intellectualization and contextualization are put forward.

The escalating issue of forest fires poses severe risks to ecosystems and human habitats, primarily due to the greenhouse effect and sudden climate changes. These fires, mostly occurring naturally, necessitate prompt detection and control. Addressing this, the authors introduce the Forest Fire Detection, Prediction, and Behaviour Analysis (FDPA) system, an innovative Internet of Things (IoT) solution. The FDPA system leverages a wireless sensor network to efficiently detect and analyse fire behaviour, providing real‐time data on fire spread, speed, and direction. Uniquely, it can anticipate natural fires hours before they occur by monitoring ecological parameters such as humidity, temperature, and using the Chandler Burning Index (CBI) for quantifying fire danger. Designed for the challenging forest environment, the FDPA system prioritises minimal power usage and simple components, crucial in areas with limited power resources. Its resilient design ensures the wireless sensor network and sensor nodes withstand harsh weather and fire conditions, maintaining functionality and reliability. Field tests of the FDPA system in various Jordanian forest locations, including Burgish–Ajloun, have demonstrated its effectiveness. The trials revealed the system's capability in early fire detection, low latency response, predicting fire behaviour, and determining fire spread direction. Continuous monitoring of the forest ecosystem and rapid detection allow authorities to act swiftly, preventing potential fires from escalating. Furthermore, the system tracks ecological changes within the forest, offering insights into imminent natural fires. This feature enables proactive measures to mitigate fire spread, safeguarding the environment and nearby communities. The strategic placement of sensor nodes and the use of durable yet straightforward components reduce the risk of system damage due to environmental extremities. Overall, the FDPA system emerges as a promising tool for forest fire management. Its ability to detect, predict, and analyse forest fires in real‐time positions it as a vital asset in minimising the detrimental impacts of forest fires on the environment and human settlements.

Using Deep Learning to Predict Fracture Patterns in Crystalline Solids

With the continuous development of research in the field of machine learning, especially the progress in deep learning and the continuous improvement of arithmetic power such as image processors, the recognition technology using biometric big data has gained wide attention and has been well applied in many fields such as human-witness matching, intelligent monitoring and epidemic prevention and control. The development trend of big data biometric identification technology is analyzed, the types of biometric features and the development and application of big data-driven biometric identification technology are summarized, and the future development trend of big data biometric identification technology is discussed.