Social Vulnerability Assessment Using Artificial Neural Network (ANN) Model for Earthquake Hazard in Tabriz City, Iran

Alizadeh, Mohsen; Alizadeh, Esmaeil; Kotenaee, Sara Asadollahpour; Shahabi, Himan; Pour, Amin Beiranvand; Panahi, Mahdi; Ahmad, Baharin Bin; Lee, Saro

doi:10.3390/su10103376

Cited by 84 publications

(48 citation statements)

References 59 publications

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“…Alizadeh et al [8], in their paper entitled "Social Vulnerability Assessment Using Artificial Neural Network (ANN) Model for Earthquake Hazard in Tabriz City, Iran", apply the ANN approach to estimate social vulnerability to earthquakes in the city of Tabriz in Iran. From their results, the very highly vulnerable area was only 0.77%, while the area of about 76.31% had a very low vulnerability.…”

Section: Sustainable Applications Of Rs and Gis Technologiesmentioning

confidence: 99%

Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations

2020

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The Special Issue on "Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations" is published. A total of 20 qualified papers are published in this Special Issue. The topics of the papers are the application of remote sensing and geospatial information systems to Earth observations in various fields such as (1) object change detection, (2) air pollution, (3) earthquakes, (4) landslides, (5) mining, (6) biomass, (7) groundwater, and (8) urban development using the techniques of remote sensing and geospatial information systems. More than 100 researchers have participated in this Special Issue. We hope that this Special Issue is helpful for sustainable applications.

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Section: Sustainable Applications Of Rs and Gis Technologiesmentioning

confidence: 99%

Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations

2020

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“…Every year, the number of disasters around the world increases, causing more damage and deaths. Earthquakes, floods, landslides, and other natural disasters cause irreparable damage, often endangering the lives of people, cultures, material resources, and the environment [1,2]. Recently, the number of natural disasters has been on the rise, with earthquakes, in particular.…”

Section: Introductionmentioning

confidence: 99%

A Contribution to a UHS-Based Seismic Risk Assessment in Croatia—A Case Study for the City of Osijek

2020

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Due to increases in the number of inhabitants and their concentrations in densely populated areas, there is a growing need in modern society to be cautious towards the impact of catastrophic natural events. An earthquake is a particularly major example of this. Knowledge of the seismic vulnerability of buildings in Europe and around the world has deepened and expanded over the last 20 years, as a result of the many devastating earthquakes. In this study, a review of seismic risk assessment methods in Croatia was presented with respect to the hazard, exposure, and vulnerability of buildings in the fourth largest city (Osijek) in Croatia. The proposed algorithm for a detailed risk assessment was applied to a database and is currently in its initial stage.Sustainability 2020, 12, 1796 2 of 24 to estimate the losses. These losses can be assessed in a material form, through the damage of the building stock or non-structural elements of buildings, or in the form of casualties or injuries during the earthquake [10,11]. The last step is to analyze uncertainties, costs, decision-making criteria, etc. The ultimate goal of studying earthquakes and their impact on people and buildings is to create a safer environment, in case an earthquake occurs.The basic elements of the earthquake risk assessment process are shown in Figure 1.Sustainability 2020, 12, 1796 2 of 25 probable intensity in a given geographical area). Then, based on the available data from the exposure model, evaluate the damage using one of the existing vulnerability assessment methods in order to estimate the losses. These losses can be assessed in a material form, through the damage of the building stock or non-structural elements of buildings, or in the form of casualties or injuries during the earthquake [10,11]. The last step is to analyze uncertainties, costs, decision-making criteria, etc. The ultimate goal of studying earthquakes and their impact on people and buildings is to create a safer environment, in case an earthquake occurs. The basic elements of the earthquake risk assessment process are shown in Figure 1.

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“…Recently, machine-learning (ML) techniques have become popular for the spatial prediction of natural hazards like wildfires [22], sinkholes [23], groundwater depletion and flooding [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38], droughts [39], earthquakes [40], land subsidence [41], and landslides [42][43][44][45][46][47][48]. ML is a type of artificial intelligence (AI) that uses computer algorithms to analyze and forecast information by learning from training data.…”

Section: Introductionmentioning

confidence: 99%

Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran

Arabameri

Chen

Blaschke

et al. 2019

Water

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To more effectively prevent and manage the scourge of gully erosion in arid and semi-arid regions, we present a novel-ensemble intelligence approach-bagging-based alternating decision-tree classifier (bagging-ADTree)-and use it to model a landscape's susceptibility to gully erosion based on 18 gully-erosion conditioning factors. The model's goodness-of-fit and prediction performance are compared to three other machine learning algorithms (single alternating decision tree, rotational-forest-based alternating decision tree (RF-ADTree), and benchmark logistic regression). To achieve this, a gully-erosion inventory was created for the study area, the Chah Mousi watershed, Iran by combining archival records containing reports of gully erosion, remotely sensed data from Google Earth, and geolocated sites of gully head-cuts gathered in a field survey. A total of 119 gully head-cuts were identified and mapped. To train the models' analysis and prediction capabilities, 83 head-cuts (70% of the total) and the corresponding measures of the conditioning factors were input into each model. The results from the models were validated using the data pertaining to the remaining 36 gully locations (30%). Next, the frequency ratio is used to identify which conditioning-factor classes have the strongest correlation with gully erosion. Using random-forest modeling, the relative importance of each of the conditioning factors was determined. Based on the random-forest results, the top eight factors in this study area are distance-to-road, drainage density, distance-to-stream, LU/LC, annual precipitation, topographic wetness index, NDVI, and elevation. Finally, based on goodness-of-fit and AUROC of the success rate curve (SRC) and prediction rate curve (PRC), the results indicate that the bagging-ADTree ensemble model had the best performance, with SRC (0.964) and PRC (0.978). RF-ADTree (SRC = 0.952 and PRC = 0.971), ADTree (SRC = 0.926 and PRC = 0.965), and LR (SRC = 0.867 and PRC = 0.870) were the subsequent best performers. The results also indicate that bagging and RF, as meta-classifiers, improved the performance of the ADTree model as a base classifier. The bagging-ADTree model's results indicate that 24.28% of the study area is classified as having high and very high susceptibility to gully erosion. The new ensemble model accurately Water 2020, 12, 16 2 of 26 identified the areas that are susceptible to gully erosion based on the past patterns of formation, but it also provides highly accurate predictions of future gully development. The novel ensemble method introduced in this research is recommended for use to evaluate the patterns of gullying in arid and semi-arid environments and can effectively identify the most salient conditioning factors that promote the development and expansion of gullies in erosion-susceptible environments.

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Social Vulnerability Assessment Using Artificial Neural Network (ANN) Model for Earthquake Hazard in Tabriz City, Iran

Cited by 84 publications

References 59 publications

Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations

Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations

A Contribution to a UHS-Based Seismic Risk Assessment in Croatia—A Case Study for the City of Osijek

Gully Head-Cut Distribution Modeling Using Machine Learning Methods—A Case Study of N.W. Iran

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