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

Remote Sensing (2021)

Mohsen Alizadeh Hasan Zabihi Fatemeh Rezaie Asad Asadzadeh Isabelle D. Wolf

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.

SWOT Analysis

Vulnerability

Self-organizing map

Multilayer perceptron

10.3390/rs13224519

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Citations (20)

Improving the prediction of wildfire susceptibility on Hawaiʻi Island, Hawaiʻi, using explainable hybrid machine learning models

Journal of Environmental Management (2023)

Trang Thi Kieu Tran Saeid Janizadeh Sayed M. Bateni Changhyun Jun Dongkyun Kim

Elevation (ballistics)

Area under curve

Predictive modelling

Gradient boosting

10.1016/j.jenvman.2023.119724

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Citations (10)

Mapping Post-Earthquake Landslide Susceptibility Using U-Net, VGG-16, VGG-19, and Metaheuristic Algorithms

Remote Sensing (2023)

Mahyat Shafapour Tehrany Fatemeh Rezaie Changhyun Jun Essam Heggy Sayed M. Bateni

Landslides are among the most frequent secondary disasters caused by earthquakes in areas prone to seismic activity. Given the necessity of assessing the current seismic conditions for ensuring the safety of life and infrastructure, there is a rising demand worldwide to recognize the extent of landslides and map their susceptibility. This study involved two stages: First, the regions prone to earthquake-induced landslides were detected, and the data were used to train deep learning (DL) models and generate landslide susceptibility maps. The application of DL models was expected to improve the outcomes in both stages. Landslide inventory was extracted from Sentinel-2 data by using U-Net, VGG-16, and VGG-19 algorithms. Because VGG-16 produced the most accurate inventory locations, the corresponding results were used in the landslide susceptibility detection stage. In the second stage, landslide susceptibility maps were generated. From the total measured landslide locations (63,360 cells), 70% of the locations were used for training the DL models (i.e., convolutional neural network [CNN], CNN-imperialist competitive algorithm, and CNN-gray wolf optimizer [GWO]), and the remaining 30% were used for validation. The earthquake-induced landslide conditioning factors included the elevation, slope, plan curvature, valley depth, topographic wetness index, land cover, rainfall, distance to rivers, and distance to roads. The reliability of the generated susceptibility maps was evaluated using the area under the receiver operating characteristic curve (AUROC) and root mean square error (RMSE). The CNN-GWO model (AUROC = 0.84 and RMSE = 0.284) outperformed the other methods and can thus be used in similar applications. The results demonstrated the efficiency of applying DL in the natural hazard domain. The CNN-GWO predicted that approximately 38% of the total area consisted of high and very high susceptibility regions, mainly concentrated in areas with steep slopes and high levels of rainfall and soil wetness. These outcomes contribute to an enhanced understanding of DL application in the natural hazard domain. Moreover, using the knowledge of areas highly susceptible to landslides, officials can actively adopt steps to reduce the potential impact of landslides and ensure the sustainable management of natural resources.

10.3390/rs15184501

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Citations (8)

Credal decision tree based novel ensemble models for spatial assessment of gully erosion and sustainable management

Scientific Reports (2021)

Alireza Arabameri Nitheshnirmal Sãdhasivam Hamza Turabieh Majdi Mafarja Fatemeh Rezaie

Abstract We introduce novel hybrid ensemble models in gully erosion susceptibility mapping (GESM) through a case study in the Bastam sedimentary plain of Northern Iran. Four new ensemble models including credal decision tree-bagging (CDT-BA), credal decision tree-dagging (CDT-DA), credal decision tree-rotation forest (CDT-RF), and credal decision tree-alternative decision tree (CDT-ADTree) are employed for mapping the gully erosion susceptibility (GES) with the help of 14 predictor factors and 293 gully locations. The relative significance of GECFs in modelling GES is assessed by random forest algorithm. Two cut-off-independent (area under success rate curve and area under predictor rate curve) and six cut-off-dependent metrics (accuracy, sensitivity, specificity, F-score, odd ratio and Cohen Kappa) were utilized based on both calibration as well as testing dataset. Drainage density, distance to road, rainfall and NDVI were found to be the most influencing predictor variables for GESM. The CDT-RF (AUSRC = 0.942, AUPRC = 0.945, accuracy = 0.869, specificity = 0.875, sensitivity = 0.864, RMSE = 0.488, F-score = 0.869 and Cohen’s Kappa = 0.305) was found to be the most robust model which showcased outstanding predictive accuracy in mapping GES. Our study shows that the GESM can be utilized for conserving soil resources and for controlling future gully erosion.

Tree (set theory)

Ensemble forecasting

Kappa

Ensemble Learning

10.1038/s41598-021-82527-3

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Citations (23)

Novel hybrid intelligence models for flood-susceptibility prediction: Meta optimization of the GMDH and SVR models with the genetic algorithm and harmony search

Journal of Hydrology (2020)

Esmaeel Dodangeh Mahdi Panahi Fatemeh Rezaie Saro Lee Dieu Tien Bui

Harmony search

Equifinality

10.1016/j.jhydrol.2020.125423

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Citations (128)

Novel hybrid models by coupling support vector regression (SVR) with meta-heuristic algorithms (WOA and GWO) for flood susceptibility mapping

Natural Hazards (2022)

Fatemeh Rezaie Mahdi Panahi Sayed M. Bateni Changhyun Jun Christopher M. U. Neale

Meta heuristic

10.1007/s11069-022-05424-6

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Citations (18)

Radon potential mapping in Jangsu-gun, South Korea using probabilistic and deep learning algorithms

Environmental Pollution (2021)

Fatemeh Rezaie Mahdi Panahi Jongchun Lee Jungsub Lee Seonhong Kim

Lithology

Topographic Wetness Index

10.1016/j.envpol.2021.118385

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Citations (16)

Application of novel ensemble models and k-fold CV approaches for Land subsidence susceptibility modelling

Stochastic Environmental Research and Risk Assessment (2021)

Alireza Arabameri M. Santosh Fatemeh Rezaie Sunil Saha Romulus Coastache

RSS

10.1007/s00477-021-02036-7

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Citations (18)

Land subsidence susceptibility mapping based on InSAR and a hybrid machine learning approach

The Egyptian Journal of Remote Sensing and Space Science (2024)

Ali Asghar Alesheikh Zahra Chatrsimab Fatemeh Rezaie Saro Lee Ali Jafari

Land subsidence (LS) due to natural processes or human activity can irreparably damage the environment. This study employed the quasi-permanent scatterer method to detect areas with and without subsidence in the period from 2018 to 2020. In addition, 12 factors affecting subsidence were selected to detect LS-prone areas. Information gain ratio (IGR) and frequency ratio methods were used to determine the importance and weighting of various factors and sub-factors affecting subsidence. Novel approaches, including the standard adaptive-network-based fuzzy inference system (ANFIS) algorithm and its integration with the particle swarm optimization (PSO) algorithm, yielded LS maps. The models' predictive performance was assessed using statistical indexes such as the root mean square error (RMSE), area under the receiver operating characteristic curve (AUROC) and confusion matrix criteria (e.g., sensitivity, specificity, precision, accuracy, and recall). Finally, Shapley additive explanations approach was used to explore the importance of features in modeling. The findings showed that the subsidence pattern was V-shaped, averaging 321 mm/year. Ground-leveling and interferometric synthetic aperture radar measurements revealed a 0.93 correlation coefficient with a σ = 1.43 mm/year deformation rate. Based on IGR analysis, aquifer media, the decline of the water table, and aquifer thickness played pivotal roles in LS occurrences. In addition, the ANFIS-PSO model classified approximately 50.26 % of the study area as high and very high susceptibility. The AUROC values of ANFIS-PSO and ANFIS models for the training dataset were 0.912 and 0.807, respectively. For the testing dataset, the ANFIS-PSO model produced a higher AUROC value of 0.863, while the ANFIS model had a value of 0.771. In addition, the RMSE values for the ANFIS-PSO model were lower. Given its remarkable accuracy, the ANFIS-PSO model was deemed suitable for evaluating subsidence susceptibility in the study area.

10.1016/j.ejrs.2024.03.004

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Citations (7)

Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms

Geoscience Frontiers (2020)

Abdul‐Lateef Balogun Fatemeh Rezaie Quoc Bao Pham Ljubomir Gigović Siniša Drobnjak

In this study, we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models. We created a geographic information system database, and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth, aerial photographs, and other validated sources. A support vector regression (SVR) machine-learning model was used to divide the landslide inventory into training (70%) and testing (30%) datasets. The landslide susceptibility map was produced using 14 causative factors. We applied the established gray wolf optimization (GWO) algorithm, bat algorithm (BA), and cuckoo optimization algorithm (COA) to fine-tune the parameters of the SVR model to improve its predictive accuracy. The resultant hybrid models, SVR-GWO, SVR-BA, and SVR-COA, were validated in terms of the area under curve (AUC) and root mean square error (RMSE). The AUC values for the SVR-GWO (0.733), SVR-BA (0.724), and SVR-COA (0.738) models indicate their good prediction rates for landslide susceptibility modeling. SVR-COA had the greatest accuracy, with an RMSE of 0.21687, and SVR-BA had the least accuracy, with an RMSE of 0.23046. The three optimized hybrid models outperformed the SVR model (AUC = 0.704, RMSE = 0.26689), confirming the ability of metaheuristic algorithms to improve model performance.

10.1016/j.gsf.2020.10.009

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Citations (139)