scholarly journals Seismic Vulnerability Mapping to Support Spatial Plans in Lhokseumawe City Area

2020 ◽  
Vol 10 (1) ◽  
pp. 269
Author(s):  
Deassy Siska ◽  
Herman Fithra ◽  
Nova Purnama Lisa ◽  
Nandi Haerudin ◽  
Muhammad Farid
Keyword(s):  
Seismic Vulnerability ◽  
Vulnerability Mapping ◽  
City Area

scholarly journals Earthquake Vulnerability Mapping Using Different Hybrid Models

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 405 ◽  
Author(s):  
Peyman Yariyan ◽  
Mohammadtaghi Avand ◽  
Fariba Soltani ◽  
Omid Ghorbanzadeh ◽  
Thomas Blaschke
Keyword(s):  
Logistic Regression ◽  
Hybrid Model ◽  
Seismic Vulnerability ◽  
Accuracy Assessment ◽  
Multiple Criteria ◽  
Vulnerability Mapping ◽  
Training Dataset ◽  
Factors Affecting ◽  
Relative Index ◽  
Multi Criteria Evaluation

The main purpose of the present study was to mathematically integrate different decision support systems to enhance the accuracy of seismic vulnerability mapping in Sanandaj City, Iran. An earthquake is considered to be a catastrophe that poses a serious threat to human infrastructures at different scales. Factors affecting seismic vulnerability were identified in three different dimensions; social, environmental, and physical. Our computer-based modeling approach was used to create hybrid training datasets via fuzzy-multiple criteria analysis (fuzzy-MCDA) and multiple criteria decision analysis-multi-criteria evaluation (MCDA-MCE) for training the multi-criteria evaluation–logistic regression (MCE–LR) and fuzzy-logistic regression (fuzzy-LR) hybrid model. The resulting dataset was validated using the seismic relative index (SRI) method and ten damaged spots from the study area, in which the MCDA-MCE model showed higher accuracy. The hybrid learning models of MCE-LR and fuzzy-LR were implemented using both resulting datasets for seismic vulnerability mapping. Finally, the resulting seismic vulnerability maps based on each model were validation using area under curve (AUC) and frequency ratio (FR). Based on the accuracy assessment results, the MCDA-MCE hybrid model (AUC = 0.85) showed higher accuracy than the fuzzy-MCDA model (AUC = 0.80), and the MCE-LR hybrid model (AUC = 0.90) resulted in more accurate vulnerability map than the fuzzy-LR hybrid model (AUC = 0.85). The results of the present study show that the accuracy of modeling and mapping seismic vulnerability in our case study area is directly related to the accuracy of the training dataset.

GIS-based Seismic Vulnerability Mapping: A Comparison of Artificial neural networks Hybrid Models

2021 ◽  
pp. 1-21
Author(s):  
Peyman Yariyan ◽  
Rahim Ali Abbaspour ◽  
Alireza Chehreghan ◽  
MohammadReza Karami ◽  
Artemi Cerdà
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Seismic Vulnerability ◽  
Hybrid Models ◽  
Vulnerability Mapping ◽  
Artificial Neural

scholarly journals GIS-BASED RAPID EARTHQUAKE EXPOSURE AND VULNERABILITY MAPPING USING LIDAR DEM AND MACHINE LEARNING ALGORITHMS: CASE OF PORAC, PAMPANGA

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 125-132
Author(s):  
M. J. D. De Los Santos ◽  
J. A. Principe
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Seismic Vulnerability ◽  
Model Building ◽  
Data Access ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Vulnerability Mapping ◽  
Support Vector ◽  
Surface Model

Abstract. Disaster risk reduction and management (DRRM) not only requires a thorough understanding of hazards but also knowledge of how much built-up structures are exposed and vulnerable to a specific hazard. This study proposed a rapid earthquake exposure and vulnerability mapping methodology using the municipality of Porac, Pampanaga as a case study. To address the challenges and limitations of data access and availability in DRRM operations, this study utilized Light Detection and Ranging (LiDAR) data and machine learning (ML) algorithms to produce an exposure database and conduct vulnerability estimation in the study area. Buildings were delineated through image thresholding and classification of the normalized Digital Surface Model (nDSM) and an exposure database containing building attributes was created using Geographic Information System (GIS). ML algorithms such as Support Vector Machine (SVM), logistic regression, and Random Forest (RF) were then used to predict the model building type (MBT) of delineated buildings to estimate seismic vulnerability. Results showed that the SVM model yielded the lowest accuracy (53%) while logistic regression and RF models performed fairly (72% and 78% respectively) as indicated by their F-1 scores. To improve the accuracy of the exposure database and vulnerability estimation, this study recommends that the proposed building delineation process be further refined by experimenting with more appropriate thresholds or by conducting point cloud classification instead of pixel-based image classification. Moreover, ground truth MBT samples should be used as training data for MBT prediction. For future work, the methodology proposed in this study can be implemented when conducting earthquake damage assessments.

Public space in the old derelict city area – a case study of Mong Kok, Hong Kong

2001 ◽  
Vol 6 (1) ◽  
pp. 15-31 ◽  
Author(s):  
Charlie Q L Xue ◽  
Kevin K Manuel ◽  
Rex H Y Chung
Keyword(s):  
Hong Kong ◽  
Public Space ◽  
City Area

Lifelog using Mobility Context Information in Urban City Area

2019 ◽  
Author(s):  
Yanlei Gu ◽  
Dailin Li ◽  
Yoshihiko Kamiya ◽  
Shunsuke Kamijo
Keyword(s):  
Context Information ◽  
City Area ◽  
Urban City

Quantitative Analysis of the Socio-economic and Environmental Aspects of the Edo City Area

2009 ◽  
Vol 39 (2) ◽  
pp. 323-338 ◽  
Author(s):  
Katsuhiro SAKURAI ◽  
Tetsuya TAKAHASHI ◽  
Yoshiro HIGANO
Keyword(s):  
Quantitative Analysis ◽  
Environmental Aspects ◽  
City Area

Stratigraphic position and localities of the Temlyan flora from the Anadyr river lower course basin

Palaeobotany ◽  
2015 ◽  
Vol 6 ◽  
pp. 48-67 ◽  
Author(s):  
L. B. Golovneva ◽  
A. A. Grabovskiy
Keyword(s):  
Volcanic Activity ◽  
City Area ◽  
Plant Fossils ◽  
Stratigraphic Position ◽  
Early Paleocene ◽  
First Time ◽  
Systematic Composition ◽  
Stratigraphic Range

Plant fossils from the volcano-clastic deposits of the lower part of the Tanyurer Formation and lower part of the Tavaivaam Unit in the Anadyr city area (Northeastrn Russia) are described for the first time. This assemblage was named as the Temlyan flora. It consists of 25 taxa and includes ferns, horsetails, lycophytes, ginkgoaleans, czekanowskialeans, cycadophytes, conifers and angiosperms. The Temlyan flora is similar in systematic composition to the Rarytkin flora from the upper part of the Rarytkin Formation which was dated as the late Maastrichtian-Danian. But it is distinguished from the latter by presence of the numerous relicts (Lokyma, Nilssonia, Encephalartopsis, Phoenicopsis and Ginkgo ex gr. sibirica). Probably the presence of relicts in the Temlyan flora is connected with influence of volcanic activity. Age of the Temlyan flora is determined as the late Maastrichtian-Danian on the basis of systematic similarity with the Rarytkin Flora. However this age may be slightly younger, possibly only early Paleocene, because the Tanyurer Formation superposes the Rarytkin Formation. Stratigraphic range of Lokyma, Nilssonia, Encephalartopsis, Phoenicopsis and Ginkgo ex gr. sibirica is extended from its previously known latest records in the early Campanian or middle Maastrichtian up to as late as the latest Maastrichtian or early Paleocene. It is very possible, that these typical Mesozoic taxa may have persisted into the Paleogene.

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