Earthquake risk assessment using an integrated Fuzzy Analytic Hierarchy Process with Artificial Neural Networks based on GIS: A case study of Sanandaj in Iran

Yariyan, Peyman; Zabihi, Hasan; Wolf, Isabelle D.; Karami, Mohammadreza; Amiriyan, Sohrab

doi:10.1016/j.ijdrr.2020.101705

Cited by 108 publications

(55 citation statements)

References 166 publications

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“…Furthermore, several methodologies have been utilized to improve the maximum displacement estimation that produces the seismic fragility curve for the responses of natural disasters. Machine learning techniques, including an artificial neural network, an integrated fuzzy analytic hierarchy process, and machine learning-based regression models, were applied for the evaluation of earthquake damages [12][13][14]. Lagaros and Fragiadakis [15] used neural networks to conduct a fragility assessment based on steel frames with seismic activities.…”

Section: Introductionmentioning

confidence: 99%

Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment

Park

Jung

2021

IJGI

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Seismic activities are serious disasters that induce natural hazards resulting in an incalculable amount of damage to properties and millions of deaths. Typically, seismic risk assessment can be performed by means of structural damage information computed based on the maximum displacement of the structure. In this study, machine learning models based on GPR are developed in order to estimate the maximum displacement of the structures from seismic activities and then used to construct fragility curves as an application. During construction of the models, 13 features of seismic waves are considered, and six wave features are selected to establish the seismic models with the correlation analysis normalizing the variables with the peak ground acceleration. Two models for six-floor and 13-floor buildings are developed, and a sensitivity analysis is performed to identify the relationship between prediction accuracy and sampling size. A 10-fold cross-validation method is used to evaluate the model performance, using the R-squared, root mean squared error, Nash criterion, and mean bias. Results of the six-parameter-based model apparently indicate a similar performance to that of the 13-parameter-based model for the two types of buildings. The model for the six-floor building affords a steadily enhanced performance by increasing the sampling size, while the model for the 13-floor building shows a significantly improved performance with a sampling size of over 200. The results indicate that the heighted structure requires a larger sampling size because it has more degrees of freedom that can influence the model performance. Finally, the proposed models are successfully constructed to estimate the maximum displacement, and applied to obtain fragility curves with various performance levels. Then, the regional seismic damage is assessed in Gyeonjgu city of South Korea as an application of the developed models. The damage assessment with the fragility curve provides the structural response from the seismic activities, which can assist in minimizing damage.

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Section: Introductionmentioning

confidence: 99%

Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment

Park

Jung

2021

IJGI

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“…It allows the input data to read, analyse, and train up to maximum accuracy compared to any traditional approaches. Several studies [19,32,51,57] of earthquake risk assessment have utilized ML techniques paired with traditional approaches. This study utilizes three ML models: SVM, DT, and RF to classify most risk-prone zones within Fujairah and its surrounding towns.…”

Section: Pairwise Analysis Of Parametersmentioning

confidence: 99%

“…Geotechnical, structural, and social-economic are some of the key points to investigate the earthquake [3,[12][13][14]. Many studies [15][16][17][18][19][20][21][22][23][24][25][26][27] have combined spatial statistical techniques and the analytical hierarchy process (AHP) integrated with a GIS to investigate seismic hazards, vulnerability, and their associated risks to humans and the environment.…”

Section: Introductionmentioning

confidence: 99%

Geospatial Multicriteria Analysis for Earthquake Risk Assessment: Case Study of Fujairah City in the UAE

et al. 2021

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A clear understanding of the spatial distribution of earthquake events facilitates the prediction of seismicity and vulnerability among researchers in the social, physical, environmental, and demographic aspects. Generally, there are few studies on seismic risk assessment in United Arab Emirates (UAE) within the geographic information system (GIS) platform. Former researches and recent news events have demonstrated that the eastern part of the country experiences jolts of 3-5 magnitude, specifically near Fujairah city and surrounding towns. This study builds on previous research on the seismic hazard that extracted the eastern part of the UAE as the most hazard-prone zone. Therefore, this study develops an integrated analytical hierarchical process (AHP) and machine learning (ML) for risk mapping considering eight geospatial parameters—distance from shoreline, schools, hospitals, roads, residences, streams, confined area, and confined area slope. Experts’ opinions and literature reviews were the basis of the AHP ranking and weighting system. To validate the AHP system, support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers were applied to the datasets. The datasets were split into 60 : 40 ratio for training and testing. Results show that SVM has the highest accuracy of 79.6% compared to DT and RF with a “predicted high” precision of 87.5% attained from the model. Risk maps from both AHP and ML approaches were developed and compared. Risk analysis was categorised into 5 classes “very high,” “high,” “moderate,” “low,” and “very low.” Both approaches modelled relatable spatial patterns as risk-prone zones. AHP approach concluded 3.6% as “very high” risk zone, whereas only 0.3% of total area was identified from ML. The total area for the “very high” (20 km2) and “high” (114 km2) risk was estimated from ML approach.

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“…Natural disasters have resulted in significant human and economic losses worldwide [1][2][3][4][5][6]. Calamities such as earthquakes are no longer considered mere natural phenomena but the outcome of multifaceted interactions between human society and hazardous events [7].…”

Section: Introductionmentioning

confidence: 99%

Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model

et al. 2021

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Tabriz city in NW Iran is a seismic-prone province with recurring devastating earthquakes that have resulted in heavy casualties and damages. This research developed a new computational framework to investigate four main dimensions of vulnerability (environmental, social, economic and physical). An Artificial Neural Network (ANN) Model and a SWOT-Quantitative Strategic Planning Matrix (QSPM) were applied. Firstly, a literature review was performed to explore indicators with significant impact on aforementioned dimensions of vulnerability to earthquakes. Next, the twenty identified indicators were analyzed in ArcGIS, a geographic information system (GIS) software, to map earthquake vulnerability. After classification and reclassification of the layers, standardized maps were presented as input to a Multilayer Perceptron (MLP) and Self-Organizing Map (SOM) neural network. The resulting Earthquake Vulnerability Maps (EVMs) showed five categories of vulnerability ranging from very high, to high, moderate, low and very low. Accordingly, out of the nine municipality zones in Tabriz city, Zone one was rated as the most vulnerable to earthquakes while Zone seven was rated as the least vulnerable. Vulnerability to earthquakes of residential buildings was also identified. To validate the results data were compared between a Multilayer Perceptron (MLP) and a Self-Organizing Map (SOM). The scatter plots showed strong correlations between the vulnerability ratings of the different zones achieved by the SOM and MLP. Finally, the hybrid SWOT-QSPM paradigm was proposed to identify and evaluate strategies for hazard mitigation of the most vulnerable zone. For hazard mitigation in this zone we recommend to diligently account for environmental phenomena in designing and locating of sites. The findings are useful for decision makers and government authorities to reconsider current natural disaster management strategies.

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Earthquake risk assessment using an integrated Fuzzy Analytic Hierarchy Process with Artificial Neural Networks based on GIS: A case study of Sanandaj in Iran

Cited by 108 publications

References 166 publications

Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment

Gaussian Process Regression-Based Structural Response Model and Its Application to Regional Damage Assessment

Geospatial Multicriteria Analysis for Earthquake Risk Assessment: Case Study of Fujairah City in the UAE

Earthquake Vulnerability Assessment for Urban Areas Using an ANN and Hybrid SWOT-QSPM Model

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