Search results for: prediction of deterioration

Authors: Kalaumari Mayoral-Peña, Ana I. Montejano-Montelongo, Josué Reyes-Muñoz, Gonzalo A. Ortiz-Mancilla, Mayrin Rodríguez-Cruz, Víctor Hernández-Villalobos, Jesús A. Guzmán-López, Santiago García-Jacobo, Iván Licona-Vázquez, Grisel Fierros-Romero, Rosario Flores-Vallejo

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The group B-lactamic antibiotics include some of the most frequently used small drug molecules against bacterial infections. Nevertheless, an alarming decrease in their efficacy has been reported due to the emergence of antibiotic-resistant bacteria. Infections caused by bacteria expressing extended Spectrum B-lactamases (ESBLs) are difficult to treat and account for higher morbidity and mortality rates, delayed recovery, and high economic burden. According to the Global Report on Antimicrobial Resistance Surveillance, it is estimated that mortality due to resistant bacteria will ascend to 10 million cases per year worldwide. These facts highlight the importance of developing low-cost and readily accessible detection methods of drug-resistant ESBLs bacteria to prevent their spread and promote accurate and fast diagnosis. Bacterial detection is commonly done using molecular diagnostic techniques, where PCR stands out for its high performance. However, this technique requires specialized equipment not available everywhere, is time-consuming, and has a high cost. Loop-Mediated Isothermal Amplification (LAMP) is an alternative technique that works at a constant temperature, significantly decreasing the equipment cost. It yields double-stranded DNA of several lengths with repetitions of the target DNA sequence as a product. Although positive and negative results from LAMP can be discriminated by colorimetry, fluorescence, and turbidity, there is still a large room for improvement in the point-of-care implementation. DNA origami is a technique that allows the formation of 3D nanometric structures by folding a large single-stranded DNA (scaffold) into a determined shape with the help of short DNA sequences (staples), which hybridize with the scaffold. This research aimed to generate DNA origami structures using LAMP products as scaffolds to improve the sensitivity to detect ESBLs in point-of-care diagnosis. For this study, the coding sequence of the CTM-X-15 ESBL of E. coli was used to generate the LAMP products. The set of LAMP primers were designed using PrimerExplorerV5. As a result, a target sequence of 200 nucleotides from CTM-X-15 ESBL was obtained. Afterward, eight different DNA origami structures were designed using the target sequence in the SDCadnano and analyzed with CanDo to evaluate the stability of the 3D structures. The designs were constructed minimizing the total number of staples to reduce costs and complexity for point-of-care applications. After analyzing the DNA origami designs, two structures were selected. The first one was a zig-zag flat structure, while the second one was a wall-like shape. Given the sequence repetitions in the scaffold sequence, both were able to be assembled with only 6 different staples each one, ranging between 18 to 80 nucleotides. Simulations of both structures were performed using scaffolds of different sizes yielding stable structures in all the cases. The generation of the LAMP products were tested by colorimetry and electrophoresis. The formation of the DNA structures was analyzed using electrophoresis and colorimetry. The modeling of novel detection methods through bioinformatics tools allows reliable control and prediction of results. To our knowledge, this is the first study that uses LAMP products and DNA-origami in combination to delect ESBL-producing bacterial strains, which represent a promising methodology for diagnosis in the point-of-care.

Keywords: beta-lactamases, antibiotic resistance, DNA origami, isothermal amplification, LAMP technique, molecular diagnosis

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Authors: Philipp Sommer, Amgad Agoub

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The construction sector significantly impacts global CO₂ emissions, particularly through the energy usage of residential buildings. To address this, various governments, including Germany's, are focusing on reducing emissions via sustainable refurbishment initiatives. This study examines the application of machine learning (ML) to estimate energy demands dynamically in residential buildings and enhance the potential for large-scale sustainable refurbishment. A major challenge in Germany is the lack of extensive publicly labeled datasets for energy performance, as energy performance certificates, which provide critical data on building-specific energy requirements and consumption, are not available for all buildings or require on-site inspections. Conversely, England and other countries in the European Union (EU) have rich public datasets, providing a viable alternative for analysis. This research adapts insights from these English datasets to the German context by developing a comprehensive data schema and calibration dataset capable of predicting building energy demand effectively. The study proposes a minimal feature set, determined through feature importance analysis, to optimize the ML model. Findings indicate that ML significantly improves the scalability and accuracy of energy demand forecasts, supporting more effective emissions reduction strategies in the construction industry. Integrating energy performance certificates into municipal heat planning in Germany highlights the transformative impact of data-driven approaches on environmental sustainability. The goal is to identify and utilize key features from open data sources that significantly influence energy demand, creating an efficient forecasting model. Using Extreme Gradient Boosting (XGB) and data from energy performance certificates, effective features such as building type, year of construction, living space, insulation level, and building materials were incorporated. These were supplemented by data derived from descriptions of roofs, walls, windows, and floors, integrated into three datasets. The emphasis was on features accessible via remote sensing, which, along with other correlated characteristics, greatly improved the model's accuracy. The model was further validated using SHapley Additive exPlanations (SHAP) values and aggregated feature importance, which quantified the effects of individual features on the predictions. The refined model using remote sensing data showed a coefficient of determination (R²) of 0.64 and a mean absolute error (MAE) of 4.12, indicating predictions based on efficiency class 1-100 (G-A) may deviate by 4.12 points. This R² increased to 0.84 with the inclusion of more samples, with wall type emerging as the most predictive feature. After optimizing and incorporating related features like estimated primary energy consumption, the R² score for the training and test set reached 0.94, demonstrating good generalization. The study concludes that ML models significantly improve prediction accuracy over traditional methods, illustrating the potential of ML in enhancing energy efficiency analysis and planning. This supports better decision-making for energy optimization and highlights the benefits of developing and refining data schemas using open data to bolster sustainability in the building sector. The study underscores the importance of supporting open data initiatives to collect similar features and support the creation of comparable models in Germany, enhancing the outlook for environmental sustainability.

Keywords: machine learning, remote sensing, residential building, energy performance certificates, data-driven, heat planning

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Authors: Denis Jordan, Daniel Golkowski, Mathias Lukas, Katharina Merz, Caroline Mlynarcik, Max Maurer, Valentin Riedl, Stefan Foerster, Eberhard F. Kochs, Andreas Bender, Ruediger Ilg

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Introduction: So far, clinical assessments that rely on behavioral responses to differentiate coma states or even predict outcome in coma patients are unreliable, e.g. because of some patients’ motor disabilities. The present study was aimed to provide prognosis in coma patients using markers from electroencephalogram (EEG), blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) and [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET). Unsuperwised principal component analysis (PCA) was used for multimodal integration of markers. Methods: Approved by the local ethics committee of the Technical University of Munich (Germany) 20 patients (aged 18-89) with severe brain damage were acquired through intensive care units at the Klinikum rechts der Isar in Munich and at the Therapiezentrum Burgau (Germany). At the day of EEG/fMRI/PET measurement (date I) patients (<3.5 month in coma) were grouped in the minimal conscious state (MCS) or vegetative state (VS) on the basis of their clinical presentation (coma recovery scale-revised, CRS-R). Follow-up assessment (date II) was also based on CRS-R in a period of 8 to 24 month after date I. At date I, 63 channel EEG (Brain Products, Gilching, Germany) was recorded outside the scanner, and subsequently simultaneous FDG-PET/fMRI was acquired on an integrated Siemens Biograph mMR 3T scanner (Siemens Healthineers, Erlangen Germany). Power spectral densities, permutation entropy (PE) and symbolic transfer entropy (STE) were calculated in/between frontal, temporal, parietal and occipital EEG channels. PE and STE are based on symbolic time series analysis and were already introduced as robust markers separating wakefulness from unconsciousness in EEG during general anesthesia. While PE quantifies the regularity structure of the neighboring order of signal values (a surrogate of cortical information processing), STE reflects information transfer between two signals (a surrogate of directed connectivity in cortical networks). fMRI was carried out using SPM12 (Wellcome Trust Center for Neuroimaging, University of London, UK). Functional images were realigned, segmented, normalized and smoothed. PET was acquired for 45 minutes in list-mode. For absolute quantification of brain’s glucose consumption rate in FDG-PET, kinetic modelling was performed with Patlak’s plot method. BOLD signal intensity in fMRI and glucose uptake in PET was calculated in 8 distinct cortical areas. PCA was performed over all markers from EEG/fMRI/PET. Prognosis (persistent VS and deceased patients vs. recovery to MCS/awake from date I to date II) was evaluated using the area under the curve (AUC) including bootstrap confidence intervals (CI, *: p<0.05). Results: Prognosis was reliably indicated by the first component of PCA (AUC=0.99*, CI=0.92-1.00) showing a higher AUC when compared to the best single markers (EEG: AUC<0.96*, fMRI: AUC<0.86*, PET: AUC<0.60). CRS-R did not show prediction (AUC=0.51, CI=0.29-0.78). Conclusion: In a multimodal analysis of EEG/fMRI/PET in coma patients, PCA lead to a reliable prognosis. The impact of this result is evident, as clinical estimates of prognosis are inapt at time and could be supported by quantitative biomarkers from EEG, fMRI and PET. Due to the small sample size, further investigations are required, in particular allowing superwised learning instead of the basic approach of unsuperwised PCA.

Keywords: coma states and prognosis, electroencephalogram, entropy, functional magnetic resonance imaging, machine learning, positron emission tomography, principal component analysis

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Authors: M. M. Hassani, S. Ammann, F. K. Wittel, P. Niemz, H. J. Herrmann

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Application of engineered wood, especially in the form of glued-laminated timbers has increased significantly. Recent progress in plywood made of high strength and high stiffness hardwoods, like European beech, gives designers in general more freedom by increased dimensional stability and load-bearing capacity. However, the strong hygric dependence of basically all mechanical properties renders many innovative ideas futile. The tendency of hardwood for higher moisture sorption and swelling coefficients lead to significant residual stresses in glued-laminated configurations, cross-laminated patterns in particular. These stress fields cause initiation and evolution of cracks in the bond-lines resulting in: interfacial de-bonding, loss of structural integrity, and reduction of load-carrying capacity. Subsequently, delamination of glued-laminated timbers made of hardwood elements can be considered as the dominant failure mechanism in such composite elements. In addition, long-term creep and mechano-sorption under changing environmental conditions lead to loss of stiffness and can amplify delamination growth over the lifetime of a structure even after decades. In this study we investigate the delamination process of adhesively bonded hardwood (European beech) elements subjected to changing climatic conditions. To gain further insight into the long-term performance of adhesively bonded elements during the design phase of new products, the development and verification of an authentic moisture-dependent constitutive model for various species is of great significance. Since up to now, a comprehensive moisture-dependent rheological model comprising all possibly emerging deformation mechanisms was missing, a 3D orthotropic elasto-plastic, visco-elastic, mechano-sorptive material model for wood, with all material constants being defined as a function of moisture content, was developed. Apart from the solid wood adherends, adhesive layer also plays a crucial role in the generation and distribution of the interfacial stresses. Adhesive substance can be treated as a continuum layer constructed from finite elements, represented as a homogeneous and isotropic material. To obtain a realistic assessment on the mechanical performance of the adhesive layer and a detailed look at the interfacial stress distributions, a generic constitutive model including all potentially activated deformation modes, namely elastic, plastic, and visco-elastic creep was developed. We focused our studies on the three most common adhesive systems for structural timber engineering: one-component polyurethane adhesive (PUR), melamine-urea-formaldehyde (MUF), and phenol-resorcinol-formaldehyde (PRF). The corresponding numerical integration approaches, with additive decomposition of the total strain are implemented within the ABAQUS FEM environment by means of user subroutine UMAT. To predict the true stress state, we perform a history dependent sequential moisture-stress analysis using the developed material models for both wood substrate and adhesive layer. Prediction of the delamination process is founded on the fracture mechanical properties of the adhesive bond-line, measured under different levels of moisture content and application of the cohesive interface elements. Finally, we compare the numerical predictions with the experimental observations of de-bonding in glued-laminated samples under changing environmental conditions.

Keywords: engineered wood, adhesive, material model, FEM analysis, fracture mechanics, delamination

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Authors: Faruk Ozdemir, Roy Kalawsky, Peter Hubbard

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Accurate estimation of RUL provides a basis for effective predictive maintenance, reducing unexpected downtime for industrial equipment. However, while models such as the Random Forest have effective predictive capabilities, they are the so-called ‘black box’ models, where interpretability is at a threshold to make critical diagnostic decisions involved in industries related to aviation. The purpose of this work is to present a prognostic framework that embeds Explainable Artificial Intelligence (XAI) techniques in order to provide essential transparency in Machine Learning methods' decision-making mechanisms based on sensor data, with the objective of procuring actionable insights for the aviation industry. Sensor readings have been gathered from critical equipment such as turbofan jet engine and landing gear, and the prediction of the RUL is done by a Random Forest model. It involves steps such as data gathering, feature engineering, model training, and evaluation. These critical components’ datasets are independently trained and evaluated by the models. While suitable predictions are served, their performance metrics are reasonably good; such complex models, however obscure reasoning for the predictions made by them and may even undermine the confidence of the decision-maker or the maintenance teams. This is followed by global explanations using SHAP and local explanations using LIME in the second phase to bridge the gap in reliability within industrial contexts. These tools analyze model decisions, highlighting feature importance and explaining how each input variable affects the output. This dual approach offers a general comprehension of the overall model behavior and detailed insight into specific predictions. The proposed framework, in its third component, incorporates the techniques of causal analysis in the form of Granger causality tests in order to move beyond correlation toward causation. This will not only allow the model to predict failures but also present reasons, from the key sensor features linked to possible failure mechanisms to relevant personnel. The causality between sensor behaviors and equipment failures creates much value for maintenance teams due to better root cause identification and effective preventive measures. This step contributes to the system being more explainable. Surrogate Several simple models, including Decision Trees and Linear Models, can be used in yet another stage to approximately represent the complex Random Forest model. These simpler models act as backups, replicating important jobs of the original model's behavior. If the feature explanations obtained from the surrogate model are cross-validated with the primary model, the insights derived would be more reliable and provide an intuitive sense of how the input variables affect the predictions. We then create an iterative explainable feedback loop, where the knowledge learned from the explainability methods feeds back into the training of the models. This feeds into a cycle of continuous improvement both in model accuracy and interpretability over time. By systematically integrating new findings, the model is expected to adapt to changed conditions and further develop its prognosis capability. These components are then presented to the decision-makers through the development of a fully transparent condition monitoring and alert system. The system provides a holistic tool for maintenance operations by leveraging RUL predictions, feature importance scores, persistent sensor threshold values, and autonomous alert mechanisms. Since the system will provide explanations for the predictions given, along with active alerts, the maintenance personnel can make informed decisions on their end regarding correct interventions to extend the life of the critical machinery.

Keywords: predictive maintenance, explainable artificial intelligence, prognostic, RUL, machine learning, turbofan engines, C-MAPSS dataset

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Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Elhadi Nasr, Ibrahim Ramadan

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Biostratigraphical analyses of well sections from the Maragh Low in the Eastern Sirt Basin has allowed high resolution correlations to be undertaken. Full integration of this data with available palaeoenvironmental, lithological, gravity, seismic, aeromagnetic, igneous, radiometric and wireline log information and a geochemical analysis of source rock quality and distribution has led to a more detailed understanding of the geological and the structural history of this area. Pre Sirt Unconformity two superimposed rifting cycles have been identified. The oldest is represented by the Amal Group of sediments and is of Late Carboniferous, Kasimovian / Gzelian to Middle Triassic, Anisian age. Unconformably overlying is a younger rift cycle which is represented the Sarir Group of sediments and is of Early Cretaceous, late Neocomian to Aptian in age. Overlying the Sirt Unconformity is the marine Late Cretaceous section. An assessment of pyrolysis results and a palynofacies analysis has allowed hydrocarbon source facies and quality to be determined. There are a number of hydrocarbon source rock horizons in the Maragh Low, these are sometimes vertically stacked and they are of fair to excellent quality. The oldest identified source rock is the Triassic Shale, this unit is unconformably overlain by sandstones belonging to the Sarir Group and conformably overlies a Triassic Siltstone unit. Palynological dating of the Triassic Shale unit indicates a Middle Triassic, Anisian age. The Triassic Shale is interpreted to have been deposited in a lacustrine palaeoenvironment. This particularly is evidenced by the dark, fine grained, organic rich nature of the sediment and is supported by palynofacies analysis and by the recovery of fish fossils. Geochemical analysis of the Triassic Shale indicates total organic carbon varying between 1.37 and 3.53. S2 pyrolysate yields vary between 2.15 mg/g and 6.61 mg/g and hydrogen indices vary between 156.91 and 278.91. The source quality of the Triassic Shale varies from being of fair to very good / rich. Linked to thermal maturity it is now a very good source for light oil and gas. It was once a very good to rich oil source. The Early Barremian Shale was also deposited in a lacustrine palaeoenvironment. Recovered palynomorphs indicate an Early Cretaceous, late Neocomian to early Barremian age. The Early Barremian Shale is conformably underlain and overlain by sandstone units belonging to the Sarir Group of sediments which are also of Early Cretaceous age. Geochemical analysis of the Early Barremian Shale indicates that it is a good oil source and was originally very good. Total organic carbon varies between 3.59% and 7%. S2 varies between 6.30 mg/g and 10.39 mg/g and the hydrogen indices vary between 148.4 and 175.5. A Late Barremian Shale unit of this age has also been identified in the central Maragh Low. Geochemical analyses indicate that total organic carbon varies between 1.05 and 2.38%, S2 pyrolysate between 1.6 and 5.34 mg/g and the hydrogen index between 152.4 and 224.4. It is a good oil source rock which is now mature. In addition to the non marine hydrocarbon source rocks pre Sirt Unconformity, three formations in the overlying Late Cretaceous section also provide hydrocarbon quality source rocks. Interbedded shales within the Rachmat Formation of Late Cretaceous, early Campanian age have total organic carbon ranging between, 0.7 and 1.47%, S2 pyrolysate varying between 1.37 and 4.00 mg/g and hydrogen indices varying between 195.7 and 272.1. The indication is that this unit would provide a fair gas source to a good oil source. Geochemical analyses of the overlying Tagrifet Limestone indicate that total organic carbon varies between 0.26% and 1.01%. S2 pyrolysate varies between 1.21 and 2.16 mg/g and hydrogen indices vary between 195.7 and 465.4. For the overlying Sirt Shale Formation of Late Cretaceous, late Campanian age, total organic carbon varies between 1.04% and 1.51%, S2 pyrolysate varies between 4.65 mg/g and 6.99 mg/g and the hydrogen indices vary between 151 and 462.9. The study has proven that both the Sirt Shale Formation and the Tagrifet Limestone are good to very good and rich sources for oil in the Maragh Low. High resolution biostratigraphical interpretations have been integrated and calibrated with thermal maturity determinations (Vitrinite Reflectance (%Ro), Spore Colour Index (SCI) and Tmax (ºC) and the determined present day geothermal gradient of 25ºC / Km for the Maragh Low. Interpretation of generated basin modelling profiles allows a detailed prediction of timing of maturation development of these source horizons and leads to a determination of amounts of missing section at major unconformities. From the results the top of the oil window (0.72% Ro) is picked as high as 10,700’ and the base of the oil window (1.35% Ro) assuming a linear trend and by projection is picked as low as 18,000’ in the Maragh Low. For the Triassic Shale the early phase of oil generation was in the Late Palaeocene / Early to Middle Eocene and the main phase of oil generation was in the Middle to Late Eocene. The Early Barremian Shale reached the main phase of oil generation in the Early Oligocene with late generation being reached in the Middle Miocene. For the Rakb Group section (Rachmat Formation, Tagrifet Limestone and Sirt Shale Formation) the early phase of oil generation started in the Late Eocene with the main phase of generation being between the Early Oligocene and the Early Miocene. From studying maturity profiles and from regional considerations it can be predicted that up to 500’ of sediment may have been deposited and eroded by the Sirt Unconformity in the central Maragh Low while up to 2000’ of sediment may have been deposited and then eroded to the south of the trough.

Keywords: Geochemical analysis of the source rocks from wells in Eastern Sirt Basin.

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Authors: Manavjit Singh Dhindsa, Ursula Das, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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These days, wildland fires, also known as forest fires, are more prevalent than ever. Wildfires have major repercussions that affect ecosystems, communities, and the environment in several ways. Wildfires lead to habitat destruction and biodiversity loss, affecting ecosystems and causing soil erosion. They also contribute to poor air quality by releasing smoke and pollutants that pose health risks, especially for individuals with respiratory conditions. Wildfires can damage infrastructure, disrupt communities, and cause economic losses. The economic impact of firefighting efforts, combined with their direct effects on forestry and agriculture, causes significant financial difficulties for the areas impacted. This research explores different forest fire spread models and presents a comprehensive review of various techniques and methodologies used in the field. A forest fire spread model is a computational or mathematical representation that is used to simulate and predict the behavior of a forest fire. By applying scientific concepts and data from empirical studies, these models attempt to capture the intricate dynamics of how a fire spreads, taking into consideration a variety of factors like weather patterns, topography, fuel types, and environmental conditions. These models assist authorities in understanding and forecasting the potential trajectory and intensity of a wildfire. Emphasizing the need for a comprehensive understanding of wildfire dynamics, this research explores the approaches, assumptions, and findings derived from various models. By using a comparison approach, a critical analysis is provided by identifying patterns, strengths, and weaknesses among these models. The purpose of the survey is to further wildfire research and management techniques. Decision-makers, researchers, and practitioners can benefit from the useful insights that are provided by synthesizing established information. Fire spread models provide insights into potential fire behavior, facilitating authorities to make informed decisions about evacuation activities, allocating resources for fire-fighting efforts, and planning for preventive actions. Wildfire spread models are also useful in post-wildfire mitigation strategies as they help in assessing the fire's severity, determining high-risk regions for post-fire dangers, and forecasting soil erosion trends. The analysis highlights the importance of customized modeling approaches for various circumstances and promotes our understanding of the way forest fires spread. Some of the known models in this field are Rothermel’s wildland fuel model, FARSITE, WRF-SFIRE, FIRETEC, FlamMap, FSPro, cellular automata model, and others. The key characteristics that these models consider include weather (includes factors such as wind speed and direction), topography (includes factors like landscape elevation), and fuel availability (includes factors like types of vegetation) among other factors. The models discussed are physics-based, data-driven, or hybrid models, also utilizing ML techniques like attention-based neural networks to enhance the performance of the model. In order to lessen the destructive effects of forest fires, this initiative aims to promote the development of more precise prediction tools and effective management techniques. The survey expands its scope to address the practical needs of numerous stakeholders. Access to enhanced early warning systems enables decision-makers to take prompt action. Emergency responders benefit from improved resource allocation strategies, strengthening the efficacy of firefighting efforts.

Keywords: artificial intelligence, deep learning, forest fire management, fire risk assessment, fire simulation, machine learning, remote sensing, wildfire modeling

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