scholarly journals Wildfire Risk Map Based on DBSCAN Clustering and Cluster Density Evaluation

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Muchamad Taufiq Anwar ◽  
Wiwien Hadikurniawati ◽  
Edy Winarno ◽  
Aji Supriyanto
Keyword(s):  
Risk Analysis ◽  
Historical Data ◽  
Risk Map ◽  
Clustering Method ◽  
Wildfire Risk ◽  
Dbscan Clustering ◽  
Cluster Density ◽  
Density Evaluation

Wildfire risk analysis can be based on historical data of fire hotspot occurrence. Traditional wildfire risk analyses often rely on the use of administrative or grid polygons which has their own limitations. This research aims to develop a wildfire risk map by implementing DBSCAN clustering method to identify areas with wildfire risk based on historical data of wildfire hotspot occurrence points. The risk ranks for each area/cluster were then ranked/calculated based on the cluster density. The result showed that this method is capable of detecting major clusters/areas with their respective wildfire risk and that the majority of consequent fire occurrences were repeated inside the identified clusters/areas.Keywords: wildfire risk map; clustering; DBSCAN; cluster density;

RISK ANALYSIS OF OFFSHORE TRANSPORTATION ACCIDENT IN ARCTIC WATERS

2021 ◽  
Vol 159 (A3) ◽  
Author(s):  
R Abbassi ◽  
F Khan ◽  
N Khakzad ◽  
B Veitch ◽  
S Ehlers
Keyword(s):  
Monte Carlo ◽  
Risk Analysis ◽  
Historical Data ◽  
Fault Trees ◽  
Northern Sea Route ◽  
Accident Scenarios ◽  
Oil Tanker ◽  
Harsh Conditions ◽  
Monte Carlo Framework ◽  
Transportation Accident

A methodology for risk analysis applicable to shipping in arctic waters is introduced. This methodology uses the Bowtie relationship to represent an accident causes and consequences. It is further used to quantify the probability of a ship accident and also the related accident consequences during navigation in arctic waters. Detailed fault trees for three possible ship accident scenarios in arctic transits are developed and represented as bowties. Factors related to cold and harsh conditions and their effects on grounding, foundering, and collision are considered as part of this study. To illustrate the application of the methodology, it is applied to a case of an oil-tanker navigating on the Northern Sea Route (NSR). The methodology is implemented in a Markov Chain Monte Carlo framework to assess the uncertainties arisen from historical data and expert judgments involved in the risk analysis.

scholarly journals AN EFFICIENT CLUSTERING METHOD FOR DBSCAN GEOGRAPHIC SPATIO-TEMPORAL LARGE DATA WITH IMPROVED PARAMETER OPTIMIZATION

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 581-584
Author(s):  
J. W. Li ◽  
X. Q. Han ◽  
J. W. Jiang ◽  
Y. Hu ◽  
L. Liu
Keyword(s):  
Parameter Optimization ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Large Data ◽  
Parameter Selection ◽  
Physical Analysis ◽  
Clustering Method ◽  
K Value ◽  
Dbscan Clustering ◽  
Spatio Temporal

Abstract. How to establish an effective method of large data analysis of geographic space-time and quickly and accurately find the hidden value behind geographic information has become a current research focus. Researchers have found that clustering analysis methods in data mining field can well mine knowledge and information hidden in complex and massive spatio-temporal data, and density-based clustering is one of the most important clustering methods.However, the traditional DBSCAN clustering algorithm has some drawbacks which are difficult to overcome in parameter selection. For example, the two important parameters of Eps neighborhood and MinPts density need to be set artificially. If the clustering results are reasonable, the more suitable parameters can not be selected according to the guiding principles of parameter setting of traditional DBSCAN clustering algorithm. It can not produce accurate clustering results.To solve the problem of misclassification and density sparsity caused by unreasonable parameter selection in DBSCAN clustering algorithm. In this paper, a DBSCAN-based data efficient density clustering method with improved parameter optimization is proposed. Its evaluation index function (Optimal Distance) is obtained by cycling k-clustering in turn, and the optimal solution is selected. The optimal k-value in k-clustering is used to cluster samples. Through mathematical and physical analysis, we can determine the appropriate parameters of Eps and MinPts. Finally, we can get clustering results by DBSCAN clustering. Experiments show that this method can select parameters reasonably for DBSCAN clustering, which proves the superiority of the method described in this paper.

A Modified DBSCAN Clustering Method to Estimate Retail Center Extent

2017 ◽  
Vol 50 (2) ◽  
pp. 141-161 ◽  
Author(s):  
Michalis Pavlis ◽  
Les Dolega ◽  
Alex Singleton
Keyword(s):  
Clustering Method ◽  
Dbscan Clustering

scholarly journals Complex solution on solving problems of estimating plant resources by GIS and climate model methods

2021 ◽  
Vol 20 (1) ◽  
pp. 87-91
Author(s):  
A. V. Vaganov ◽  
Z. V. Pokalyakin ◽  
L. A. Khvorova
Keyword(s):  
Initial Data ◽  
Information Technologies ◽  
Climate Model ◽  
Complex Solution ◽  
Clustering Method ◽  
Plant Resources ◽  
Factors Affecting ◽  
Dbscan Clustering ◽  
Modern Information

The paper considers the applied aspects of the use of modern information technologies for an accurateassessment of plant resources using GIS and climate model methods. For the most effective achievement of the goals ofintegrated monitoring and assessment of plant resources, the authors discussed and proposed a number of requirementsfor the initial data, factors affecting the change in the area and the results of the assessment of plant resources. As anavailable free analogue of the method for correcting the spatial unevenness of the points of registration of species inSDMtoolbox (ArcGIS), we proposed the DBSCAN clustering method, which is implemented in the Python library sklearn.

scholarly journals MRG-DBSCAN: An Improved DBSCAN Clustering Method Based on Map Reduce and Grid

2015 ◽  
Vol 8 (2) ◽  
pp. 119-128 ◽  
Author(s):  
Li Ma ◽  
Lei Gu ◽  
Bo Li ◽  
Shouyi Qiao ◽  
Jin Wang
Keyword(s):  
Map Reduce ◽  
Clustering Method ◽  
Dbscan Clustering

Provision of a Wildfire Risk Map: Informing Residents in the Wildland Urban Interface

Risk Analysis ◽  
2009 ◽  
Vol 29 (11) ◽  
pp. 1588-1600 ◽  
Author(s):  
Pallab Mozumder ◽  
Ryan Helton ◽  
Robert P. Berrens
Keyword(s):  
Risk Map ◽  
Wildland Urban Interface ◽  
Wildfire Risk

scholarly journals Risk Analysis of Flood Disaster Based on Fuzzy Clustering Method

2011 ◽  
Vol 5 ◽  
pp. 1915-1919 ◽  
Author(s):  
Junfei Chen ◽  
Shufang Zhao ◽  
Huimin Wang
Keyword(s):  
Risk Analysis ◽  
Fuzzy Clustering ◽  
Flood Disaster ◽  
Clustering Method ◽  
Fuzzy Clustering Method
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