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Prof. Dr. H. Sebnem Düzgün
Department of Mining Engineering, Colorado School of Mines, 1500 Illinois Street Brown Hall Rm: 268, CO 80401, USA

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0 Data Analytics
0 AI
0 Health and Safety
0 Indicator-based sustainability asssessment
0 Quantitative resilience and risk assessment

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Journal article
Published: 04 August 2021 in Sustainability
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Sustainability reporting is one of the tools that contribute to incorporating sustainable development in the design of extractive operations (i.e., “Design for Sustainability”), and the demand for sustainability reports is increasing due to the increased focus on sustainable development and sustainable financing efforts. The extractive industries are believed to have unique strengths to contribute to achieving the Sustainable Development Goals. Nonetheless, companies are expected to be transparent and accountable not only to investors but to all stakeholders, including communities, suppliers, clients, employees, and governments. Therefore, extractive industries require effective sustainability accounting and reporting to transition and contribute to sustainable development. Through a data-driven approach, this paper examines the scope and consistency of sustainability indicators used in the sustainability reports of eight oil and gas and eight mining companies from 2012 to 2018. Through content analysis and relevant statistical methods, we analyze the ways in which companies reported on their contributions to sustainable development, with a focus on indicators used and trends over time both within each industry and between industries. We demonstrate that extractive industries’ sustainability reporting practices are not consistent over time and that internal issues are better represented than external issues, in particular transportation and supply chain issues. Furthermore, while there are similar trends across the industries in terms of social and environmental indicator reporting, there are significant differences in economic reporting. We conclude that although both industries have established sustainability reporting practices, there are trends that demonstrate what companies are focusing on more, as well as areas for improvement. We see this as an initial step for conceptualizing how these industries can more objectively, consistently, and effectively assess and contribute to sustainable development.

ACS Style

Cansu Perdeli Demirkan; Nicole Smith; H. Duzgun; Aurora Waclawski. A Data-Driven Approach to Evaluation of Sustainability Reporting Practices in Extractive Industries. Sustainability 2021, 13, 8716 .

AMA Style

Cansu Perdeli Demirkan, Nicole Smith, H. Duzgun, Aurora Waclawski. A Data-Driven Approach to Evaluation of Sustainability Reporting Practices in Extractive Industries. Sustainability. 2021; 13 (16):8716.

Chicago/Turabian Style

Cansu Perdeli Demirkan; Nicole Smith; H. Duzgun; Aurora Waclawski. 2021. "A Data-Driven Approach to Evaluation of Sustainability Reporting Practices in Extractive Industries." Sustainability 13, no. 16: 8716.

Journal article
Published: 08 May 2021 in International Journal of Applied Earth Observation and Geoinformation
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The exposition of minerals to oxygen as well as non-treated tailings in mining activities alter the balance of the ecosystem, specifically leading to the generation of acidic solutions in the presence of sulfidic minerals. Several secondary iron minerals are precipitated in these settings that can be detected via remote sensing applications. The purpose of this research is to investigate the capacity of hyperspectral analysis to determine the abundance of Acid Mine Drainage (AMD)-indicator secondary iron minerals in mine sites with the guidance of ground truth information. To this end, we focus on an abandoned coal mine site in Turkey to detect secondary iron minerals associated with AMD via multispectral Sentinel-2 imagery, in accordance with the laboratory analysis of field-collected samples through X-Ray Diffraction (XRD), Inductive Coupled Plasma (ICP), and ASD spectral analysis. In relation with the conducted laboratory XRD and ICP results, the proposed methodology first reveals the iron-induced absorption feature located between 700 and 900 nm on field-collected ASD spectra and reference USGS spectra through a baseline method, namely parabola fitting method. The subsequent remote sensing analysis then applies hyperspectral unmixing to Sentinel-2 imagery and identifies the spectral endmember indicating iron-absorption behavior by computing its spectral angle distance to reference spectra. The experiments reveal that while the iron absorption characteristics are not apparent in pixel spectra, the utilized unmixing methodology enables capturing of those features at sub-pixel level on the resulting endmembers. Second, the comparison between the calculated abundances with unmixing and iron levels obtained with ground based ICP analysis indicate coherent correlation values. Finally, among the utilized unmixing methods, the performance of SISAL is found better than MVSA with the resulting correlation values of 0.76 and 0.63, respectively, while also returning closer endmembers to the reference iron spectra. The performed research demonstrates the potential of hyperspectral applications on Sentinel-2 data to uncover the sub-pixel iron-induced spectral features in the visible region, proving compatible results between the spectral and laboratory analysis.

ACS Style

Hilal Soydan; Alper Koz; H. Şebnem Düzgün. Secondary Iron Mineral Detection via Hyperspectral Unmixing Analysis with Sentinel-2 Imagery. International Journal of Applied Earth Observation and Geoinformation 2021, 101, 102343 .

AMA Style

Hilal Soydan, Alper Koz, H. Şebnem Düzgün. Secondary Iron Mineral Detection via Hyperspectral Unmixing Analysis with Sentinel-2 Imagery. International Journal of Applied Earth Observation and Geoinformation. 2021; 101 ():102343.

Chicago/Turabian Style

Hilal Soydan; Alper Koz; H. Şebnem Düzgün. 2021. "Secondary Iron Mineral Detection via Hyperspectral Unmixing Analysis with Sentinel-2 Imagery." International Journal of Applied Earth Observation and Geoinformation 101, no. : 102343.

Journal article
Published: 20 January 2021 in Remote Sensing
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The availability of free and high temporal resolution satellite data and advanced SAR techniques allows us to analyze ground displacement cost-effectively. Our aim was to properly define subsidence and uplift areas to delineate a geothermal field and perform time-series analysis to identify temporal trends. A Persistent Scatterer Interferometry (PSI) algorithm was used to estimate vertical displacement in the Brady geothermal field located in Nevada by analyzing 70 Sentinel-1A Synthetic-Aperture Radar (SAR) images, between January 2017 and December 2019. To classify zones affected by displacement, an unsupervised Self-Organizing Map (SOM) algorithm was applied to classify points based on their behavior in time, and those clusters were used to determine subsidence, uplift, and stable regions automatically. Finally, time-series analysis was applied to the clustered data to understand the inflection dates. The maximum subsidence is –19 mm/yr with an average value of –6 mm/yr within the geothermal field. The maximum uplift is 14 mm/yr with an average value of 4 mm/yr within the geothermal field. The uplift occurred on the NE of the field, where the injection wells are located. On the other hand, subsidence is concentrated on the SW of the field where the production wells are located. The coupling of the PSInSAR and the SOM algorithms was shown to be effective in analyzing the direction and pattern of the displacements observed in the field.

ACS Style

Mahmut Cavur; Jaime Moraga; H. Duzgun; Hilal Soydan; Ge Jin. Displacement Analysis of Geothermal Field Based on PSInSAR and SOM Clustering Algorithms: A Case Study of Brady Field, Nevada—USA. Remote Sensing 2021, 13, 349 .

AMA Style

Mahmut Cavur, Jaime Moraga, H. Duzgun, Hilal Soydan, Ge Jin. Displacement Analysis of Geothermal Field Based on PSInSAR and SOM Clustering Algorithms: A Case Study of Brady Field, Nevada—USA. Remote Sensing. 2021; 13 (3):349.

Chicago/Turabian Style

Mahmut Cavur; Jaime Moraga; H. Duzgun; Hilal Soydan; Ge Jin. 2021. "Displacement Analysis of Geothermal Field Based on PSInSAR and SOM Clustering Algorithms: A Case Study of Brady Field, Nevada—USA." Remote Sensing 13, no. 3: 349.

Journal article
Published: 30 November 2020 in Remote Sensing
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Hyperspectral image processing techniques, with their ability to provide information about the chemical compositions of materials, have great potential for pavement condition assessment. This study introduces a novel age-based pavement assessment method, employing an integrated algorithm with artificial neural network (ANN) and spectral angle mapping (SAM) on hyperspectral images. In the proposed method, the resulting ANN prediction outputs are used to make a new prediction along with the results from SAM scores. Tests are performed on hyperspectral images that have 360 spectral bands between 400 and 900 nm, collected by a specifically designed vehicular system for proximal image acquisition. The acquired images have eight classes, including three different pavement classes (good (5-year), medium (10-year), and poor (25-year)), yellow dye, white dye, soil, paving stone, and shadow. Several experiments are performed to evaluate the robustness of the followed methodology with limited learning data that include 5, 10, 25, and 50 samples per class, selected randomly from our independent spectral database. For a fair comparison, the individual ANN, SAM, support vector machine (SVM), and stacked auto-encoders (SAE) algorithms are also evaluated. The classification performances of individual ANN and SAM are significantly increased with their joint use, demonstrating a 1.2% to 21% classification accuracy improvement in relation to the training sample size. The study proves that the proposed approach is quite robust in cases wherein few training data are available, while SAE and standard ANN algorithms are more successful in cases wherein more learning data are present.

ACS Style

Okan Özdemir; Hilal Soydan; Yasemin Yardımcı Çetin; Hafize Düzgün. Neural Network Based Pavement Condition Assessment with Hyperspectral Images. Remote Sensing 2020, 12, 3931 .

AMA Style

Okan Özdemir, Hilal Soydan, Yasemin Yardımcı Çetin, Hafize Düzgün. Neural Network Based Pavement Condition Assessment with Hyperspectral Images. Remote Sensing. 2020; 12 (23):3931.

Chicago/Turabian Style

Okan Özdemir; Hilal Soydan; Yasemin Yardımcı Çetin; Hafize Düzgün. 2020. "Neural Network Based Pavement Condition Assessment with Hyperspectral Images." Remote Sensing 12, no. 23: 3931.

Original paper
Published: 31 October 2020 in Journal on Multimodal User Interfaces
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Serious games—games that have additional purposes rather than only entertainment—aim to educate people, solve, and plan several real-life tasks and circumstances in an interactive, efficient, and user-friendly way. Emergency training and planning provide structured curricula, rule-based action items, and interdisciplinary collaborative entities to imitate and teach real-life tasks. This rule-based structure enables the curricula to be transferred into other systematic learning platforms. Although emergency training includes these highly structured and repetitive action responses, a general framework to map the training scenarios’ actions, roles, and collaborative structures to serious games’ game mechanics and game dialogues, is still not available. To address this issue, in this study, a scenario-based game generator, which maps domain-oriented tasks to game rules and game mechanics, was developed. Also, two serious games (i.e., Hospital game and BioGarden game) addressing the training mechanisms of Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNe) domain, were developed by both the game developers and the scenario-based game generator for comparative analysis. Finally, the outcomes of these games were mapped to the virtual reality environment to provide a thorough training program. To test the usability, immersion, presence, and technology acceptance aspects of the proposed game generator’s outcomes, 15 game developer participants tested a complete set of games and answered the questionnaires of the corresponding phenomenon. The results show that although the game generator has higher CPU time and memory usage, it highly outperforms the game development pipeline performance of the game developers and provides usable and immersive games. Thus, this study provides a promising game generator which bridges the CBRNe practitioners and game developers to transform real-life training scenarios into video games efficiently and quickly.

ACS Style

Elif Surer; Mustafa Erkayaoğlu; Zeynep Nur Öztürk; Furkan Yücel; Emin Alp Bıyık; Burak Altan; Büşra Şenderin; Zeliha Oğuz; Servet Gürer; H. Şebnem Düzgün. Developing a scenario-based video game generation framework for computer and virtual reality environments: a comparative usability study. Journal on Multimodal User Interfaces 2020, 1 -19.

AMA Style

Elif Surer, Mustafa Erkayaoğlu, Zeynep Nur Öztürk, Furkan Yücel, Emin Alp Bıyık, Burak Altan, Büşra Şenderin, Zeliha Oğuz, Servet Gürer, H. Şebnem Düzgün. Developing a scenario-based video game generation framework for computer and virtual reality environments: a comparative usability study. Journal on Multimodal User Interfaces. 2020; ():1-19.

Chicago/Turabian Style

Elif Surer; Mustafa Erkayaoğlu; Zeynep Nur Öztürk; Furkan Yücel; Emin Alp Bıyık; Burak Altan; Büşra Şenderin; Zeliha Oğuz; Servet Gürer; H. Şebnem Düzgün. 2020. "Developing a scenario-based video game generation framework for computer and virtual reality environments: a comparative usability study." Journal on Multimodal User Interfaces , no. : 1-19.

Regular article
Published: 17 August 2020 in OR Spectrum
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Route restoration is considered to be a task of foremost priority in disaster relief. In this paper, we propose a robust optimization approach for post-disaster route restoration under uncertain restoration times. We present a novel decision rule based on restoration time ordering that yields optimal restoration sequencing and propose conditions for complexity reduction in the model and prove probability bounds on the satisfaction of these conditions. We implement our models in a realistic study of the 2015 Gorkha earthquake in Nepal.

ACS Style

Aakil M. Caunhye; Nazli Yonca Aydin; H. Sebnem Duzgun. Robust post-disaster route restoration. OR Spectrum 2020, 42, 1055 -1087.

AMA Style

Aakil M. Caunhye, Nazli Yonca Aydin, H. Sebnem Duzgun. Robust post-disaster route restoration. OR Spectrum. 2020; 42 (4):1055-1087.

Chicago/Turabian Style

Aakil M. Caunhye; Nazli Yonca Aydin; H. Sebnem Duzgun. 2020. "Robust post-disaster route restoration." OR Spectrum 42, no. 4: 1055-1087.

Journal article
Published: 12 September 2019 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Land use and land cover (LULC) maps in many areas have been used by companies, government offices, municipalities, and ministries. Accurate classification for LULC using remotely sensed data requires State of Art classification methods. The SNAP free software and ArcGIS Desktop were used for analysis and report. In this study, the optical Sentinel-2 images were used. In order to analyze the data, an object-oriented method was applied: Supported Vector Machines (SVM). An accuracy assessment is also applied to the classified results based on the ground truth points or known reference pixels. The overall classification accuracy of 83,64% with the kappa value of 0.802 was achieved using SVM. The study indicated that of SVM algorithms, the proposed framework on Sentinel-2 imagery results is satisfactory for LULC maps.

ACS Style

Mahmut Cavur; H. S. Duzgun; S. Kemec; D. C. Demirkan. LAND USE AND LAND COVER CLASSIFICATION OF SENTINEL 2-A: ST PETERSBURG CASE STUDY. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2019, XLII-1/W2, 13 -16.

AMA Style

Mahmut Cavur, H. S. Duzgun, S. Kemec, D. C. Demirkan. LAND USE AND LAND COVER CLASSIFICATION OF SENTINEL 2-A: ST PETERSBURG CASE STUDY. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019; XLII-1/W2 ():13-16.

Chicago/Turabian Style

Mahmut Cavur; H. S. Duzgun; S. Kemec; D. C. Demirkan. 2019. "LAND USE AND LAND COVER CLASSIFICATION OF SENTINEL 2-A: ST PETERSBURG CASE STUDY." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W2, no. : 13-16.

Journal article
Published: 22 June 2019 in The Extractive Industries and Society
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Gemstone value is often associated with origin as particular color, clarity, and other attributes of interest to consumers are often determined by the geological location of the stone. In this paper, we consider how the provenance of gemstones is harnessed through the 4 P framework of product, price, promotion, and place. Both tanzanite and zultanite/csarite are currently each found in only one location in the world. Tanzanite is mined in Mererani, near Arusha, Tanzania, and zultanite/csarite is mined in Milas, near Mugla, Turkey. While this rarity and other attributes such as the color and durability of tanzanite were successfully leveraged so that tanzanite attained global recognition, zultanite/csarite has remained largely unknown. Our study examines the potential reasons why tanzanite and zultanite/csarite have experienced such different degrees of success on the global gemstone market. Our main findings are that rarity itself is not an inadequate determinant of value and that consumer preferences for color need to be carefully marketed with a powerful storyline and linked to other sectors such as tourism.

ACS Style

Mehmet Altingoz; Nicole M. Smith; H. Sebnem Duzgun; Patricia F. Syvrud; Saleem H. Ali. Color and local heritage in gemstone branding: A comparative study of blue zoisite (Tanzanite) and color-change diaspore (Zultanite/Csarite). The Extractive Industries and Society 2019, 6, 1030 -1039.

AMA Style

Mehmet Altingoz, Nicole M. Smith, H. Sebnem Duzgun, Patricia F. Syvrud, Saleem H. Ali. Color and local heritage in gemstone branding: A comparative study of blue zoisite (Tanzanite) and color-change diaspore (Zultanite/Csarite). The Extractive Industries and Society. 2019; 6 (4):1030-1039.

Chicago/Turabian Style

Mehmet Altingoz; Nicole M. Smith; H. Sebnem Duzgun; Patricia F. Syvrud; Saleem H. Ali. 2019. "Color and local heritage in gemstone branding: A comparative study of blue zoisite (Tanzanite) and color-change diaspore (Zultanite/Csarite)." The Extractive Industries and Society 6, no. 4: 1030-1039.

Journal article
Published: 28 September 2018 in International Journal of Applied Earth Observation and Geoinformation
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Hydrocarbon micro and macro seeps alter chemical and mineral composition of the Earth’s surface, providing prospects for detection with remote sensing tools. There have been several studies focusing on mapping these anomalies by utilizing ever evolving multispectral and hyperspectral imaging instruments, which has proven their capacity for mapping both hydrocarbons and hydrocarbon-induced alterations so far. These studies broadly comprise of methods like calculating band ratios, spectral angle mapping, spectral feature fitting, and principal component analysis as detection techniques. However, there is a lack of concentration on advanced signature based detection algorithms and unmixing methods for mapping surface manifestations of hydrocarbon microseeps. Signature based detection algorithms utilize target spectra to correlate with each pixel’s spectrum in order to allocate possible target locations. Unmixing methods, on the other hand, require no input spectra beforehand, aiming to resolve each pixel’s spectral constituents and their corresponding abundance fractions. In this paper, the potential of all these methods in mapping microseepage related anomalies are evaluated by implementing and comparing them for Gemrik Anticline, one of the prospective hydrocarbon exploration fields in Turkey. Hence, it provides a complete knowledge on determination surface manifestations of hydrocarbon microseeps with the help of well known supervised target detection algorithms and hyperspectral unmixing algorithms. The study area is located in the Southeastern Anatolia, between the cities of Adıyaman and Şanlıurfa. The spectral signatures were collected with Analytical Spectral Devices Inc. (ASD) spectrometer during the field studies conducted by Avcıoğlu (2010), to be utilized as an input to the signature based detection algorithms as well as a reference to select the related abundance map among the outputs of unmixing methods. Advanced Space Borne Thermal Emission and Radiometer (ASTER) image of the study region, with an atmospheric correction before running the algorithms, is selected for the applications. Among the applied algorithms, Simplex Identification via Split Augmented Lagrangian (SISAL) is selected as a base of comparison, as it possess minimum calculated error metrics in the experiments. Another unmixing method, the Minimum Volume Simplex Algorithm (MVSA), and signature-based techniques, Desired Target Detection and Classification Algorithm (DTDCA) & Spectral Matched Filter (SMF) follow the success of the SISAL, respectively. The Crosta technique, which is performed as a conventional approach for experimental comparisons, has also shown its capability, succeeding these algorithms. The study provides an overall assessment for methodologies to be used for hydrocarbon microseepage mapping, which also serves guidance for further exploration studies in the region. The potential of ASTER data for hydrocarbon-induced alterations is also emphasized as a cost effective tool for the future applications.

ACS Style

Hilal Soydan; Alper Koz; H. Şebnem Düzgün. Identification of hydrocarbon microseepage induced alterations with spectral target detection and unmixing algorithms. International Journal of Applied Earth Observation and Geoinformation 2018, 74, 209 -221.

AMA Style

Hilal Soydan, Alper Koz, H. Şebnem Düzgün. Identification of hydrocarbon microseepage induced alterations with spectral target detection and unmixing algorithms. International Journal of Applied Earth Observation and Geoinformation. 2018; 74 ():209-221.

Chicago/Turabian Style

Hilal Soydan; Alper Koz; H. Şebnem Düzgün. 2018. "Identification of hydrocarbon microseepage induced alterations with spectral target detection and unmixing algorithms." International Journal of Applied Earth Observation and Geoinformation 74, no. : 209-221.

Journal article
Published: 02 August 2018 in Safety Science
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Analyzing accidents of sociotechnical systems requires an understanding of the system safety structure. Among various methods proposed for accident analysis in complex sociotechnical systems the Systems-Theoretic Accident Model and Processes (STAMP) model is one of the most widely used model for predictive applications in the literature. The STAMP accident causality model with the accident analysis tool, called CAST (Causal Analysis based on Systems Theory) is an effective method for accident analysis. The Soma Mine Disaster (SMD), which occurred due to a fire in the underground coal mine and caused 301 fatalities in 2014, is one of the largest mine disasters in the last few decades. Although mine fires usually do not cause large number of casualties as compared to explosions in underground coal mines, the SMD has one of the highest number of deaths in the 21st century. In this paper, the CAST, which is based on STAMP is used for analyzing the SMD as it provides a system engineering perspective in accident analysis. Considering the complex nature of the SMD, a variety of factors were involved in the high number of casualties. Among them, socio-technical factors like unstructured organizational and human performance as well as inadequate safety culture, improper decision making and risk perception, which played a critical role in the SMD, are defined in an integrated system thinking framework. Finally, inadequate system control constraints are identified in each hierarchical level of the system and improvements are suggested, accordingly. It is also demonstrated that a CAST analysis is robust for the cases like the SMD, which involves high degree of uncertainty related to the occurrence of the accident. The analyses presented in this paper also show the design of prevention and mitigation measures against such disasters in different levels of the accident control hierarchy.

ACS Style

H. Sebnem Düzgün; Nancy Leveson. Analysis of soma mine disaster using causal analysis based on systems theory (CAST). Safety Science 2018, 110, 37 -57.

AMA Style

H. Sebnem Düzgün, Nancy Leveson. Analysis of soma mine disaster using causal analysis based on systems theory (CAST). Safety Science. 2018; 110 ():37-57.

Chicago/Turabian Style

H. Sebnem Düzgün; Nancy Leveson. 2018. "Analysis of soma mine disaster using causal analysis based on systems theory (CAST)." Safety Science 110, no. : 37-57.

Journal article
Published: 30 July 2018 in International Journal of Disaster Risk Reduction
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Rural transportation networks are highly susceptible to geohazards such as earthquakes and landslides. Indirect losses can be severe because the breakdown of a transportation network aggravates rescue, supply, and other recovery activities. The operations and logistics of rural networks that are under seismic risks must be managed using the limited resources specifically in developing countries. We propose a methodology to evaluate road recovery strategies for restoring connectivity after blockages due to earthquakes and earthquake-triggered landslides. This paper gives insight into the recovery process, which can be used by decision-makers for enhancing resilience and supplying immediate relief to rural areas. The proposed framework has four steps: 1) identification of strategies for increasing recovery performance, 2) determination of graph-based metrics to represent network connectivity, 3) applying topology-based and Monte Carlo simulations to each strategy, and 4) analysis of recovery times to compare these resilience-enhancement strategies. The methodology was tested using a case study from Sindhupalchok District, Nepal, a region that was severely affected by the Gorkha earthquake in 2015. The closed road segments and recovery times were determined through field surveys with locals and governmental authorities, and by investigating the intensity of earthquake-triggered landslides. Our results showed that the proposed approach provides information about the recovery behavior of road networks and simplifies the evaluation process. It is robust enough to extend and assess decision-makers’ preferences for improving resilience.

ACS Style

Nazli Yonca Aydin; H. Sebnem Duzgun; Hans Rudolf Heinimann; Friedemann Wenzel; Kaushal Raj Gnyawali. Framework for improving the resilience and recovery of transportation networks under geohazard risks. International Journal of Disaster Risk Reduction 2018, 31, 832 -843.

AMA Style

Nazli Yonca Aydin, H. Sebnem Duzgun, Hans Rudolf Heinimann, Friedemann Wenzel, Kaushal Raj Gnyawali. Framework for improving the resilience and recovery of transportation networks under geohazard risks. International Journal of Disaster Risk Reduction. 2018; 31 ():832-843.

Chicago/Turabian Style

Nazli Yonca Aydin; H. Sebnem Duzgun; Hans Rudolf Heinimann; Friedemann Wenzel; Kaushal Raj Gnyawali. 2018. "Framework for improving the resilience and recovery of transportation networks under geohazard risks." International Journal of Disaster Risk Reduction 31, no. : 832-843.

Journal article
Published: 20 September 2017 in IEEE Transactions on Knowledge and Data Engineering
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Temporal or spatio-temporal sequential pattern discovery is a well-recognized important problem in many domains like seismology, criminology, and finance. The majority of the current approaches are based on candidate generation which necessitates parameter tuning, namely, definition of a neighborhood, an interest measure, and a threshold value to evaluate candidates. However, their performance is limited as the success of these methods relies heavily on parameter settings. In this paper, we propose an algorithm which uses a nonparametric stochastic de-clustering procedure and a multivariate Hawkes model to define triggering relations within and among the event types and employs the estimated model to extract significant triggering patterns of event types. We tested the proposed method with real and synthetic data sets exhibiting different characteristics. The method gives good results that are comparable with the methods based on candidate generation in the literature.

ACS Style

Berna Bakir Batu; Tugba Taskaya Temizel; H. Sebnem Duzgun. A Non-Parametric Algorithm for Discovering Triggering Patterns of Spatio-Temporal Event Types. IEEE Transactions on Knowledge and Data Engineering 2017, 29, 2629 -2642.

AMA Style

Berna Bakir Batu, Tugba Taskaya Temizel, H. Sebnem Duzgun. A Non-Parametric Algorithm for Discovering Triggering Patterns of Spatio-Temporal Event Types. IEEE Transactions on Knowledge and Data Engineering. 2017; 29 (12):2629-2642.

Chicago/Turabian Style

Berna Bakir Batu; Tugba Taskaya Temizel; H. Sebnem Duzgun. 2017. "A Non-Parametric Algorithm for Discovering Triggering Patterns of Spatio-Temporal Event Types." IEEE Transactions on Knowledge and Data Engineering 29, no. 12: 2629-2642.