Fatemeh Rezaie

Korea Institute of Geoscience and Mineral Resources

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Mahdi Panahi

Stockholm University

Saro Lee

Korea Institute of Geoscience and Mineral Resources

Sayed M. Bateni

University of Hawaiʻi at Mānoa

Alireza Arabameri

Tarbiat Modares University

Subodh Chandra Pal

University of Burdwan

Changhyun Jun

Korea University

Chang-Wook Lee

Kangwon National University

Khabat Khosravi

Florida International University

Omid Rahmati

Soil Conservation and Watershed Management Research

Rabin Chakrabortty

Asian Institute of Technology

Cooperative Institutions

University of Tehran

Islamic Azad University, Science and Research Branch

University of Hawaiʻi at Mānoa

Kangwon National University

University of Tabriz

University of Hawaii System

Tarbiat Modares University

University of Burdwan

Soil Conservation and Watershed Management Research

Duy Tan University

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GIS modeling of seismic vulnerability of residential fabrics considering geotechnical, structural, social and physical distance indicators in Tehran using multi-criteria decision-making techniques

Natural hazards and earth system sciences (2015)

Fatemeh Rezaie Mahdi Panahi

Abstract. The main issue in determining seismic vulnerability is having a comprehensive view of all probable damages related to earthquake occurrence. Therefore, taking into account factors such as peak ground acceleration at the time of earthquake occurrence, the type of structures, population distribution among different age groups, level of education and the physical distance to hospitals (or medical care centers) and categorizing them into four indicators of geotechnical, structural, social and physical distance to needed facilities and from dangerous ones will provide us with a better and more exact outcome. To this end, this paper uses the analytic hierarchy process to study the importance of criteria or alternatives and uses the geographical information system to study the vulnerability of Tehran to an earthquake. This study focuses on the fact that Tehran is surrounded by three active and major faults: Mosha, North Tehran and Rey. In order to comprehensively determine the vulnerability, three scenarios are developed. In each scenario, seismic vulnerability of different areas in Tehran is analyzed and classified into four levels: high, medium, low and safe. The results show that, regarding seismic vulnerability, the faults of Mosha, North Tehran and Rey make, respectively, 6, 16 and 10% of Tehran highly vulnerable, while 34, 14 and 27% is safe.

Vulnerability

Social vulnerability

Seismic zone

10.5194/nhess-15-461-2015

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Citations (75)

Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility

CATENA (2019)

Abolfazl Jaafari Mahdi Panahi Binh Thai Pham Himan Shahabi Dieu Tien Bui

Robustness

10.1016/j.catena.2018.12.033

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Citations (228)

Optimization modelling to establish false measures implemented with ex-situ plant species to control gully erosion in a monsoon-dominated region with novel in-situ measurements

Journal of Environmental Management (2021)

Asish Saha Subodh Chandra Pal Alireza Arabameri Indrajit Chowdhuri Fatemeh Rezaie

10.1016/j.jenvman.2021.112284

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Citations (59)

Mapping of landslide potential in Pyeongchang-gun, South Korea, using machine learning meta-based optimization algorithms

The Egyptian Journal of Remote Sensing and Space Science (2022)

Muhammad Fulki Fadhillah Wahyu Luqmanul Hakim Mahdi Panahi Fatemeh Rezaie Chang-Wook Lee

Landslides are geological hazards that can have severe impacts, threatening both the people and the local environment of highlands or mountain slopes. Landslide susceptibility mapping is an essential tool for predicting landslides and mitigating landslide-associated damage in areas prone to these events. This study aims to investigate the combination of using an adaptive network-based fuzzy inference system (ANFIS) with metaheuristic optimization algorithms: gray wolf optimizer (GWO), particle swarm optimization algorithm (PSO), and the imperialist competitive algorithm (ICA) in mapping landslide potential. The study area was Pyeongchang-gun, South Korea, for which an accurate landslide inventory dataset is available. A landslide inventory map was organized, and the data were separated randomly into training data (70%) and validation data (30%). In addition, 16 landslide-related factors consisting of geo-environmental and topo-hydrological factors were considered as predictive variables. This landslide susceptibility model was be evaluated based on the value of the area under the receiver operating characteristic (ROC) curve (AUC) to measure its accuracy. Based on the maps, the validation results showed that the optimized models of ANFIS-ICA, ANFIS-PSO, and ANFIS-GWO had AUC accuracies of 0.927, 0.947, and 0.968, respectively. The result from the hybrid algorithms model of ANFIS with metaheuristic algorithms outperformed the standalone ANFIS model in terms of accuracy in predicting landslide potential. Therefore, the ML algorithm and optimization algorithm models proposed in this study are more suitable for landslide susceptibility mapping in the study area.

Imperialist competitive algorithm

10.1016/j.ejrs.2022.03.008

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Citations (16)

Flood susceptibility mapping of Cheongju, South Korea based on the integration of environmental factors using various machine learning approaches

Journal of Environmental Management (2024)

Liadira Kusuma Widya Fatemeh Rezaie Woojin Lee Chang-Wook Lee Nurwatik Nurwatik

10.1016/j.jenvman.2024.121291

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Citations (4)

Flash-flood hazard susceptibility mapping in Kangsabati River Basin, India

Geocarto International (2021)

Rabin Chakrabortty Subodh Chandra Pal Fatemeh Rezaie Alireza Arabameri Saro Lee

Flood-susceptibility mapping is an important component of flood risk management to control the effects of natural hazards and prevention of injury. We used a remote-sensing and geographic information system (GIS) platform and a machine-learning model to develop a flood susceptibility map of Kangsabati River Basin, India where flash flood is common due to monsoon precipitation with short duration and high intensity. And in this subtropical region, climate change's impact helps to influence the distribution of rainfall and temperature variation. We tested three models-particle swarm optimization (PSO), an artificial neural network (ANN), and a deep-leaning neural network (DLNN)-and prepared a final flood susceptibility map to classify flood-prone regions in the study area. Environmental, topographical, hydrological, and geological conditions were included in the models, and the final model was selected based on the relations between potentiality of causative factors and flood risk based on multi-collinearity analysis. The model results were validated and evaluated using the area under receiver operating characteristic (ROC) curve (AUC), which is an indicator of the current state of the environment and a value >0.95 implies a greater risk of flash floods. The AUC values for ANN, DLNN, and PSO for training datasets were 0.914, 0.920, and 0.942, respectively. Among these three models, PSO showed the best performance with an AUC value of 0.942. The PSO approach is applicable for flood susceptibility mapping of the eastern part of India, a subtropical region, to allow flood mitigation and help to improve risk management in this region.

Flash flood

Elevation (ballistics)

10.1080/10106049.2021.1953618

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Citations (70)

Large-scale dynamic flood monitoring in an arid-zone floodplain using SAR data and hybrid machine-learning models

Journal of Hydrology (2022)

Mahdi Panahi Omid Rahmati Zahra Kalantari Hamid Darabi Fatemeh Rezaie

Differential Evolution

Land Cover

10.1016/j.jhydrol.2022.128001

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