Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

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Stock-Price Forecasting Based on XGBoost and LSTM

Pham Hoang Vuong et al.

Summary: This article presents a method that uses machine learning and deep learning approaches for stock price forecasting. The study demonstrates that this method outperforms traditional approaches in feature extraction and price prediction.

COMPUTER SYSTEMS SCIENCE AND ENGINEERING (2022)

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Article Geosciences, Multidisciplinary

MODIS-FIRMS and ground-truthing-based wildfire likelihood mapping of Sikkim Himalaya using machine learning algorithms

Polash Banerjee

Summary: This study analyzed 15 environmental features to prepare a wildfire likelihood map of Sikkim Himalaya and compared the efficiency of machine learning methods for wildfire prediction. The results show that Random Forest outperformed other methods in predicting wildfires, with factors like average temperature, average wind speed, proximity to roadways and tree cover percentage being the most important determinants of wildfires in the region.

NATURAL HAZARDS (2022)

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Article Computer Science, Information Systems

Real-Time and Intelligent Flood Forecasting Using UAV-Assisted Wireless Sensor Network

Shidrokh Goudarzi et al.

Summary: This article proposes a fault-tolerant multi-level framework that integrates unmanned aerial vehicles (UAVs) and wireless sensor networks (WSN) for river level monitoring and flood prediction. A water-level prediction model based on a hybrid algorithm of Group Method Data Handling (GMDH) and Particle Swarm Optimization (PSO) is introduced and trained using real dataset. The performance of the proposed model is evaluated and compared with other models using different metrics, showing good results.

CMC-COMPUTERS MATERIALS & CONTINUA (2022)

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Article Computer Science, Theory & Methods

A new consistency definition of interval multiplicative preference relation

Xian-Juan Cheng et al.

Summary: This paper introduces a new consistency definition of interval multiplicative preference relation (IMPR) with robustness and invariance. Comparative analyses are conducted to reveal the relationships among different consistency definitions. A new consistency index is introduced, along with an algorithm to improve consistency level and a goal programming model. A new individual decision making method with IMPR is proposed, supported by numerical examples and simulation analyses.

FUZZY SETS AND SYSTEMS (2021)

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Article Automation & Control Systems

Robustness testing framework for RUL prediction Deep LSTM networks

Mohamed Sayah et al.

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ISA TRANSACTIONS (2021)

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Incremental semi-supervised Extreme Learning Machine for Mixed data stream classification

Qiude Li et al.

Summary: MIS-ELM is an Incremental Semi-supervised Extreme Learning Machine designed for Mixed data stream classification, which tackles the challenges of mixed data streams and limited labeled samples with innovative encoding and incremental learning methods. Experimental results demonstrate the superiority of the proposed method in real data streams.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

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Ignition and burning mechanisms of live spruce needles

Adnan Darwish Ahmad et al.

Summary: Through laboratory ignition experiments, the burning behavior of live fuel was studied, revealing that the volatile ejection from live fuel can alter flame direction and preheat nearby fuels. Scaling analysis confirmed these findings, showing that adjacent fuels were ignited by direct flame contact.

FUEL (2021)

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Article Soil Science

Effects of fire on soil respiration and its components in a Dahurian larch (Larix gmelinii) forest in northeast China: Implications for forest ecosystem carbon cycling

Tongxin Hu et al.

Summary: Fire disturbance significantly altered soil respiration components, increasing heterotrophic respiration while decreasing autotrophic respiration. Structural equation modeling indicated that soil temperature and fine root biomass were major factors influencing soil respiration, with microbial biomass carbon becoming a dominant factor in burned plots.

GEODERMA (2021)

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Article Forestry

Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method

Slobodan Milanovic et al.

Summary: Forest fire risk has increased globally in recent decades, with Mediterranean region being traditionally the most at risk in Europe. Effective fire protection systems are necessary to prevent damage to forests and infrastructure, with forest fire probability mapping being a key tool for resource allocation and firefighting efficiency. Machine learning methods such as Random Forest have shown better predictive ability in forest fire occurrence mapping compared to traditional Logistic Regression models.

FORESTS (2021)

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Article Engineering, Civil

Augmentation of limited input data using an artificial neural network method to improve the accuracy of water quality modeling in a large lake

Jaeyoung Kim et al.

Summary: The study aimed to improve the prediction accuracy of water quality in a large lake using the combined application of an artificial neural network (ANN) method and a numerical model. Results showed that the MLP-EFDC model outperformed the EFDC model based on interpolated data, but performance was not significantly improved in the downstream area where internal processes of the lake mixed with input effects.

JOURNAL OF HYDROLOGY (2021)

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Article Ecology

Prediction of regional wildfire activity in the probabilistic Bayesian framework of Firelihood

Francois Pimont et al.

Summary: Modeling wildfire activity is crucial for informing risk management and understanding ecosystem dynamics. Developing the Firelihood model allowed for accurate predictions of fire activity, but uncertainty increased with the rarity of events. The model provided insights into fire hazard and offers applications for management and projections in the context of climate change.

ECOLOGICAL APPLICATIONS (2021)

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Article Computer Science, Interdisciplinary Applications

Energy consumption prediction model with deep inception residual network inspiration and LSTM

Abdulwahed Salam et al.

Summary: The paper introduces a deep learning model based on deep feedforward neural networks and Long Short-Term Memory for predicting electricity consumption. Through comparisons with other models on two datasets, it is proven that the proposed model achieves the smallest error in predicting electricity consumption.

MATHEMATICS AND COMPUTERS IN SIMULATION (2021)

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Article Optics

High-accuracy projector calibration method for fringe projection profilometry considering perspective transformation

Jin Yu et al.

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OPTICS EXPRESS (2021)

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Article Multidisciplinary Sciences

Fire Behavior Prediction in Larch Forests of the Kazakhstan Altai

Aleksandra Volokitina et al.

Summary: This paper discusses automated fire behavior prediction in larch forests of the Kazakhstan Altai region based on large-scale vegetation fuel maps. By utilizing fire simulation software, it is possible to effectively assess fire spread rate and intensity, providing valuable insights for targeted fire prevention and response measures. The study highlights the importance of creating VF maps and utilizing GIS technology for fire prevention and management in forested areas.

SYMMETRY-BASEL (2021)

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Article Chemistry, Analytical

Machine Learning Methods and Synthetic Data Generation to Predict Large Wildfires

Fernando-Juan Perez-Porras et al.

Summary: Wildfires are increasing in frequency globally, making prediction complex. Machine learning methods for predicting large wildfires face challenges of overfitting or underfitting due to imbalanced datasets. Using synthetic data can improve prediction accuracy, offering a new approach for Decision Support Systems in wildfire management.

SENSORS (2021)

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Article Engineering, Environmental

Spatio-temporal analysis of fire occurrence in Australia

Fernanda Valente et al.

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2021)

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Article Environmental Sciences

Quantitative Assessment of Vertical and Horizontal Deformations Derived by 3D and 2D Decompositions of InSAR Line-of-Sight Measurements to Supplement Industry Surveillance Programs in the Tengiz Oilfield (Kazakhstan)

Emil Bayramov et al.

Summary: This research focused on quantitatively assessing surface deformation velocities and rates at Tengiz Oilfield in Kazakhstan using remote sensing techniques. The study revealed continuous subsidence at the oilfield due to natural and man-made factors, with maximum annual vertical deformation velocity reaching 70 mm. The results will support operators of oil and gas fields and other infrastructure in evaluating ground deformation measurements.

REMOTE SENSING (2021)

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Review Ecology

Existing Improvements in Simulation of Fire-Wind Interaction and Its Effects on Structures

Maryam Ghodrat et al.

Summary: This work provides a comprehensive overview of current research on fire-wind interaction phenomena, highlighting the neglect of disturbance interactions that influence structures in previous studies. It also investigates the impact of a fire transitioning from a wildfire to a wildland-urban interface on high- and low-rise buildings.

FIRE-SWITZERLAND (2021)

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Article Thermodynamics

Using cellular automata to simulate field-scale flaming and smouldering wildfires in tropical peatlands

Dwi M. J. Purnomo et al.

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PROCEEDINGS OF THE COMBUSTION INSTITUTE (2021)

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Article Computer Science, Information Systems

Using Residual Networks and Cosine Distance-Based K-NN Algorithm to Recognize On-Line Signatures

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IEEE ACCESS (2021)

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Article Ecology

Examining Landscape-Scale Fuel and Terrain Controls of Wildfire Spread Rates Using Repetitive Airborne Thermal Infrared (ATIR) Imagery

Gavin M. Schag et al.

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FIRE-SWITZERLAND (2021)

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On the confusion matrix in credit scoring and its analytical properties

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Simulating forest fire spread and fire-fighting using cellular automata

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CHINESE JOURNAL OF PHYSICS (2020)

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Review Environmental Sciences

A review of machine learning applications in wildfire science and management

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A novel model to predict significant wave height based on long short-term memory network

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Global Wildfire Outlook Forecast with Neural Networks

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Wetland Type Information Extraction Using Deep Convolutional Neural Network

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New fractional derivative with sigmoid function as the kernel and its models

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Research on the identification method for the forest fire based on deep learning

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Predicting Fire Propagation across Heterogeneous Landscapes Using WyoFire: A Monte Carlo-Driven Wildfire Model

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Identification before-after Forest Fire and Prediction of Mangrove Forest Based on Markov-Cellular Automata in Part of Sembilang National Park, Banyuasin, South Sumatra, Indonesia

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PROPAGATOR: An Operational Cellular-Automata Based Wildfire Simulator

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Wildland Fire Spread Modeling Using Convolutional Neural Networks

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A novel criterion applicable to transformer differential protection based on waveform sinusoidal similarity identification

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Rapid spatio-temporal flood prediction and uncertainty quantification using a deep learning method

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Simulation of forest fire spread using a two-way coupling algorithm and its application to a real wildfire

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Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM

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Live Fuel Moisture Content: The 'Pea Under the Mattress' of Fire Spread Rate Modeling?

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Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

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