Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm

2021 ◽  
pp. 101292
Author(s):  
Tran Thi Tuyen ◽  
Abolfazl Jaafari ◽  
Hoang Phan Hai Yen ◽  
Trung Nguyen-Thoi ◽  
Tran Van Phong ◽  
...  
Keyword(s):  
Forest Fire ◽  
Learning Algorithm ◽  
Spatially Explicit ◽  
Ensemble Models

scholarly journals Classification of forest fire areas using machine learning algorithm

2021 ◽  
Vol 3 (1) ◽  
pp. 008-015
Author(s):  
Saruni Dwiasnati ◽  
Yudo Devianto
Keyword(s):  
Machine Learning ◽  
Forest Fire ◽  
Forest Fires ◽  
Naive Bayes ◽  
Learning Algorithm ◽  
Fire Hazard ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Burned Area ◽  
K Nearest Neighbor

Forest fires that occur will cause various kinds of problems, both in terms of health, such as smoke that can interfere with the respiratory system, in terms of the economy such as the economic wheel cannot run as usual, in terms of the environment can damage the surrounding environment and the environment that is missed by smoke, and other disasters. Forest fires can also have an impact on the costs that will be incurred to resolve the problems that arise due to forest fires, so research is needed to find out and measure the area affected by forest fires that burned in the range of 1980 - 2019 using a dataset of approximately 10,000. The target in this research is to be able to generate the best percentage scenario and find out the model of using the algorithm used to explore the algorithm in the Machine Learning method for the model for estimating the area of forest fires, namely the Siak Kampar Peninsula in Riau Province. In this study, 7 parameters were used to create a forest and land fire hazard map, namely weather temperature, Burned Area Density, hotspot density, wind speed, land cover type, rainfall, and land use. The seven parameters will be searched for accuracy results using the Classification method with Machine Learning algorithms, including Naïve Bayes, SVM, and K-Nearest Neighbor (K-NN). In this study, comparisons were made to obtain the best algorithm for estimating forest fire areas. By generating each algorithm is 71.72% for the Naïve Bayes algorithm, 75.00% for the SVM algorithm, and 64.71% for the K-NN algorithm.

scholarly journals Comparative study on machine learning algorithms for early fire forest detection system using geodata.

2020 ◽  
Vol 10 (5) ◽  
pp. 5507
Author(s):  
Zouiten Mohammed ◽  
Chaaouan Hanae ◽  
Setti Larbi
Keyword(s):  
Machine Learning ◽  
Forest Fire ◽  
Forest Fires ◽  
Learning Algorithm ◽  
Detection System ◽  
Learning Algorithms ◽  
Spatial Prediction ◽  
Machine Learning Algorithms ◽  
Geographical Information ◽  
Deep Learning Algorithm

Forest fires have caused considerable losses to ecologies, societies and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, a competitive spatial prediction model for automatic early detection of wild forest fire using machine learning algorithms can be proposed. This model can help researchers to predict forest fires and identify risk zonas. System using machine learning algorithm on geodata will be able to notify in real time the interested parts and authorities by providing alerts and presenting on maps based on geographical treatments for more efficacity and analyzing of the situation. This research extends the application of machine learning algorithms for early fire forest prediction to detection and representation in geographical information system (GIS) maps.

scholarly journals Loss-Driven Adversarial Ensemble Deep Learning for On-Line Time Series Analysis

2019 ◽  
Vol 11 (12) ◽  
pp. 3489
Author(s):  
Hyungjin Ko ◽  
Jaewook Lee ◽  
Junyoung Byun ◽  
Bumho Son ◽  
Saerom Park
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Data Distribution ◽  
Series Data ◽  
Learning Models ◽  
Classification Problems ◽  
Ensemble Models ◽  
On Line

Developing a robust and sustainable system is an important problem in which deep learning models are used in real-world applications. Ensemble methods combine diverse models to improve performance and achieve robustness. The analysis of time series data requires dealing with continuously incoming instances; however, most ensemble models suffer when adapting to a change in data distribution. Therefore, we propose an on-line ensemble deep learning algorithm that aggregates deep learning models and adjusts the ensemble weight based on loss value in this study. We theoretically demonstrate that the ensemble weight converges to the limiting distribution, and, thus, minimizes the average total loss from a new regret measure based on adversarial assumption. We also present an overall framework that can be applied to analyze time series. In the experiments, we focused on the on-line phase, in which the ensemble models predict the binary class for the simulated data and the financial and non-financial real data. The proposed method outperformed other ensemble approaches. Moreover, our method was not only robust to the intentional attacks but also sustainable in data distribution changes. In the future, our algorithm can be extended to regression and multiclass classification problems.

scholarly journals Machine Learning of Spatial Data

2021 ◽  
Vol 10 (9) ◽  
pp. 600
Author(s):  
Behnam Nikparvar ◽  
Jean-Claude Thill
Keyword(s):  
Machine Learning ◽  
Spatial Data ◽  
Learning Algorithm ◽  
Spatially Explicit ◽  
Advantages And Disadvantages ◽  
Spatial Properties ◽  
Machine Learning Applications ◽  
Observation Matrix ◽  
Spatial Domains ◽  
Spatially Explicit Data

Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to handle them in machine learning applications are lagging behind. In this survey of the literature, we seek to identify and discuss spatial properties of data that influence the performance of machine learning. We review some of the best practices in handling such properties in spatial domains and discuss their advantages and disadvantages. We recognize two broad strands in this literature. In the first, the properties of spatial data are developed in the spatial observation matrix without amending the substance of the learning algorithm; in the other, spatial data properties are handled in the learning algorithm itself. While the latter have been far less explored, we argue that they offer the most promising prospects for the future of spatial machine learning.

scholarly journals Varying dataset resolution alters predictive accuracy of spatially explicit ensemble models for avian species distribution

2018 ◽  
Vol 8 (24) ◽  
pp. 12867-12878
Author(s):  
Claire M. Curry ◽  
Jeremy D. Ross ◽  
Andrea J. Contina ◽  
Eli S. Bridge
Keyword(s):  
Species Distribution ◽  
Predictive Accuracy ◽  
Avian Species ◽  
Spatially Explicit ◽  
Ensemble Models

scholarly journals A YOLO based Technique for Early Forest Fire Detection

2020 ◽  
Vol 9 (4) ◽  
pp. 1357-1362
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Forest Fire ◽  
Forest Fires ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Mobile App ◽  
Raspberry Pi ◽  
Time Data ◽  
Alert Message

Forest fires, wildfires and bushfires are a global environmental problem that causes serious damage each year. The most significant factors in the fight against forest fires involve earliest possible detection of the fire, flame or smoke event, proper classification of the fire and rapid response from the fire departments. In this paper, we developed an automatic early warning system that incorporates multiple sensors and state of the art deep learning algorithm which has a minimum number of false positives and give a good accuracy in real time data and in the lowest cost possible to our drone to monitor forest fire as early as possible and report it to the concerned authority. The drones will be equipped with sensors, Raspberry pi 3, neural stick, APM 2.5, GPS, Wifi. The neural stick will be used for real time image processing using our state-of-the-art deep learning model. And as soon as forest fire is detected the UAV will send an alert message to the concerned authority on the mobile App along with location coordinates of the fire, image and the amount of area in which forest is spread using a mesh messaging. So that immediate action will be taken to stop it from spreading and causing loss of millions of lives and money. Using both deep learning and infrared cameras to monitor the forest and surrounding area, we will take advantage of recent advances in multi-sensor surveillance technologies. This innovative technique helps the forest department to detect fire in first 12 hours of its initialization , which is the most effective time to control the fire.

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