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Mr. Omid Ghorbanzadeh
PhD candidate

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Research Keywords & Expertise

0 Geoinformatics
0 Object based image analysis
0 Spatial Decision Support Systems
0 deep learning for image processing
0 Machine Learning & Artificial Intelligence

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Machine Learning & Artificial Intelligence
Object based image analysis
deep learning for image processing

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Short Biography

Omid Ghorbanzadeh received the B.Sc. degree in Pure Mathematics, focusing on topology and differential equations from the University of Shahid Madani, Tabriz, Iran, in 2011, and the master’s degree from the University of Tabriz for remote sensing and geographic information systems (RS & GIS), Tabriz, Iran, in 2016, working on the optimization of multi-criteria analysis and artificial neural network by evaluating different techniques of interval calculations and fuzzy logic for the spatial problems. He is currently pursuing a Ph.D. degree in Geoinformatics at Z_GIS Department of Geoinformatics, University of Salzburg, Austria. His current project is semantic segmentation of Earth Observation data by integrating deep learning and, in particular, supervised deep convolutional neural network (CNN) with object-based image analysis (OBIA). His research interests include Geoinformatics, high and very high-resolution remote sensing image classification, and deep learning. Mr. Ghorbanzadeh is the recipient of the best student paper award at the GISTAM 2019 Conference (Heraklion, Crete, Greece).

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Article
Published: 16 July 2021 in Scientific Reports
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Earthquakes and heavy rainfalls are the two leading causes of landslides around the world. Since they often occur across large areas, landslide detection requires rapid and reliable automatic detection approaches. Currently, deep learning (DL) approaches, especially different convolutional neural network and fully convolutional network (FCN) algorithms, are reliably achieving cutting-edge accuracies in automatic landslide detection. However, these successful applications of various DL approaches have thus far been based on very high resolution satellite images (e.g., GeoEye and WorldView), making it easier to achieve such high detection performances. In this study, we use freely available Sentinel-2 data and ALOS digital elevation model to investigate the application of two well-known FCN algorithms, namely the U-Net and residual U-Net (or so-called ResU-Net), for landslide detection. To our knowledge, this is the first application of FCN for landslide detection only from freely available data. We adapt the algorithms to the specific aim of landslide detection, then train and test with data from three different case study areas located in Western Taitung County (Taiwan), Shuzheng Valley (China), and Eastern Iburi (Japan). We characterize three different window size sample patches to train the algorithms. Our results also contain a comprehensive transferability assessment achieved through different training and testing scenarios in the three case studies. The highest f1-score value of 73.32% was obtained by ResU-Net, trained with a dataset from Japan, and tested on China’s holdout testing area using the sample patch size of 64 × 64 pixels.

ACS Style

Omid Ghorbanzadeh; Alessandro Crivellari; Pedram Ghamisi; Hejar Shahabi; Thomas Blaschke. A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan). Scientific Reports 2021, 11, 1 -20.

AMA Style

Omid Ghorbanzadeh, Alessandro Crivellari, Pedram Ghamisi, Hejar Shahabi, Thomas Blaschke. A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan). Scientific Reports. 2021; 11 (1):1-20.

Chicago/Turabian Style

Omid Ghorbanzadeh; Alessandro Crivellari; Pedram Ghamisi; Hejar Shahabi; Thomas Blaschke. 2021. "A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan)." Scientific Reports 11, no. 1: 1-20.

Research article
Published: 19 March 2021 in Geocarto International
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The concept of leveraging the predictive capacity of predisposing factors for landslide susceptibility (LS) modeling has been continuously improved in recent work focusing on computational and machine learning algorithms. This paper explores the predictive capacity of different approaches to LS modelling using artificial intelligence. The key objective of this study is to estimate a LS map for the Taleghan-Alamut basin of Iran using Credal Decision Tree (CDT)-based (i.e. CDT-Bagging, CDT-Multiboost and CDT-SubSpace) hybrid machine learning approaches, which are state-of-the-art soft computing approaches that are hardly ever utilized in the assessment of LS. In this study, we used eighteen landslide predisposing factors (LPFs) that we considered to be the most important local morphological and geo-environmental factors influencing the occurrence of landslides. We calculated the significance of each of the LPFs in the landslide susceptibility assessment using the Random Forest Method. We also employed the Receiver Operating Characteristic curve, precision, performance, map robustness measurement and selection of the best-fitting models. The results shows that, compared to the other models, the CDT-Multiboost is the excellent model in this perspective with an average area under curve (AUC) of 0.993 based on a 4-fold cross-validation. We, therefore, consider the CDT-Multiboost models to be an effective method for improving spatial prediction of LS where landslide scarps or bodies are not clearly identified during the preparation of landslide inventory maps. Therefore, it will be helpful for preparing future landslide inventory maps and mitigate landslide damages.

ACS Style

Alireza Arabameri; Subodh Chandra Pal; Fatemeh Rezaie; Rabin Chakrabortty; Asish Saha; Thomas Blaschke; Mariano Di Napoli; Omid Ghorbanzadeh; Phuong Thao Thi Ngo. Decision tree based ensemble machine learning approaches for landslide susceptibility mapping. Geocarto International 2021, 1 -35.

AMA Style

Alireza Arabameri, Subodh Chandra Pal, Fatemeh Rezaie, Rabin Chakrabortty, Asish Saha, Thomas Blaschke, Mariano Di Napoli, Omid Ghorbanzadeh, Phuong Thao Thi Ngo. Decision tree based ensemble machine learning approaches for landslide susceptibility mapping. Geocarto International. 2021; ():1-35.

Chicago/Turabian Style

Alireza Arabameri; Subodh Chandra Pal; Fatemeh Rezaie; Rabin Chakrabortty; Asish Saha; Thomas Blaschke; Mariano Di Napoli; Omid Ghorbanzadeh; Phuong Thao Thi Ngo. 2021. "Decision tree based ensemble machine learning approaches for landslide susceptibility mapping." Geocarto International , no. : 1-35.

Journal article
Published: 26 January 2021 in Natural Hazards and Earth System Sciences
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Comprehensive and sustainable landslide risk management, including the identification of areas susceptible to landslides, requires responsible organisations to collaborate efficiently. Landslide risk management efforts are often made after major triggering events, such as hazard mitigation after the 2015 Gorkha earthquake in Nepal. There is also a lack of knowledge sharing and collaboration among stakeholders to cope with major disaster events, in addition to a lack of efficiency and continuity. There should be a system to allow for landslide information to be easily updated after an event. For a variety of users of landslide information in Nepal, the availability and extraction of landslide data from a common database are a vital requirement. In this study, we investigate the requirements to propose a concept for a web-based Nepalese landslide information system (NELIS) that provides users with a platform to share information about landslide events to strengthen collaboration. The system will be defined as a web GIS (geographic information system) that supports responsible organisations in addressing and managing different user requirements of people working with landslides, thereby improving the current state of landslide hazard and risk management in Nepal. The overall aim of this study is to propose a conceptual framework and design of NELIS. A system like NELIS could benefit stakeholders involved in data collection and landslide risk management in their efforts to report and provide landslide information. Moreover, such a system would allow for detailed and structured landslide documentation and consequently provide valuable information regarding susceptibility and hazard and risk mapping. For the reporting of landslides directly to the system, a web portal is proposed. Based on field surveys, a literature review and stakeholder interviews, a structure of the landslide database and a conceptual framework for the NELIS platform are proposed.

ACS Style

Sansar Raj Meena; Florian Albrecht; Daniel Hölbling; Omid Ghorbanzadeh; Thomas Blaschke. Nepalese landslide information system (NELIS): a conceptual framework for a web-based geographical information system for enhanced landslide risk management in Nepal. Natural Hazards and Earth System Sciences 2021, 21, 301 -316.

AMA Style

Sansar Raj Meena, Florian Albrecht, Daniel Hölbling, Omid Ghorbanzadeh, Thomas Blaschke. Nepalese landslide information system (NELIS): a conceptual framework for a web-based geographical information system for enhanced landslide risk management in Nepal. Natural Hazards and Earth System Sciences. 2021; 21 (1):301-316.

Chicago/Turabian Style

Sansar Raj Meena; Florian Albrecht; Daniel Hölbling; Omid Ghorbanzadeh; Thomas Blaschke. 2021. "Nepalese landslide information system (NELIS): a conceptual framework for a web-based geographical information system for enhanced landslide risk management in Nepal." Natural Hazards and Earth System Sciences 21, no. 1: 301-316.

Research article
Published: 01 January 2021 in Geomatics, Natural Hazards and Risk
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The present study aims to evaluate the susceptibility to floods in the river basin of Buzau in Romania through the following 6 machine learning models: Support Vector Machine (SVM), J48 decision tree, Adaptive Neuro-Fuzzy Inference System (ANFIS), Random Forest (RF), Artificial Neural Network (ANN) and Alternating Decision Tree (ADT). In the first stage of the study, an inventory of the areas affected by floods was made in the study area, and a number of 205 flood points were identified. Further, 12 flood predictors were selected to be used for final susceptibility mapping. The six models' training was performed by using 70% of the total flood points that have been associated with the values of flood predictors. The highest accuracy (0.973) was obtained by the RF model, while J48 had the lowest performance (0.825). Besides, by classifying flood predictors' values in flood and non-flood pixels, the six flood susceptibility maps were made. High and very high flood susceptibility values cover between 17.71% (MLP) and 27.93% (ANFIS) of the study area. The validation of the results, performed using the ROC Curve, shows that the most accurate flood susceptibility values are also assigned to the RF model (AUC = 0.996).

ACS Style

Romulus Costache; Alireza Arabameri; Ismail Elkhrachy; Omid Ghorbanzadeh; Quoc Bao Pham. Detection of areas prone to flood risk using state-of-the-art machine learning models. Geomatics, Natural Hazards and Risk 2021, 12, 1488 -1507.

AMA Style

Romulus Costache, Alireza Arabameri, Ismail Elkhrachy, Omid Ghorbanzadeh, Quoc Bao Pham. Detection of areas prone to flood risk using state-of-the-art machine learning models. Geomatics, Natural Hazards and Risk. 2021; 12 (1):1488-1507.

Chicago/Turabian Style

Romulus Costache; Alireza Arabameri; Ismail Elkhrachy; Omid Ghorbanzadeh; Quoc Bao Pham. 2021. "Detection of areas prone to flood risk using state-of-the-art machine learning models." Geomatics, Natural Hazards and Risk 12, no. 1: 1488-1507.

Research article
Published: 01 January 2021 in Geomatics, Natural Hazards and Risk
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Landslides are a form of soil erosion threatening the sustainability of some areas of the world. There is, therefore, a need to investigate landslide rates and behaviour. In this research, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a meta classifier based on reduced error pruning tree (REPTree) as a base classifier called RF-REPTree, for landslide susceptibility mapping (LSM) in the Kalaleh watershed, Golestan Province, Iran. Some benchmark models, including the open-source Java decision tree (J48), naive Bayes tree (NBTree), and REPTree were used to compare the designed model. A total of 249 landslide locations were identified and mapped. The group was split into training (70%) and testing (30%) data for modelling and reliability analysis. Based on a literature review and multi-collinearity tests, 16 landslide conditioning factors (LCFs) were selected. Of the LCFs, the topographical position index (TPI) had the highest correlation with landslide occurrence. The LSM produced by RF-REPTree revealed that nearly 29% of the study areas have high to very high landslide susceptibility (LS). Statistical analysis of the model results included the receiver operating characteristic curve (ROC), the efficiency test, the true skill statistic (TSS), and the kappa index. ROC demonstrated that the AUC values of RF-REPTree, REPTree, J48, and NBTree models were 0.832, 0.700, 0.695, and 0.759 for succession rate curves and 0.794, 0.740, 0.788, and 0.728 for prediction rate curves, respectively. Therefore, all models were judged to be acceptably accurate for LSM. Among the LS models, the RF-REPTree model achieved the highest accuracy, followed by REPTree, J48, and NBTree. The results of LSM can be used to target the mitigation of landslide hazards and provide a foundation for sustainable environmental planning.

ACS Style

Alireza Arabameri; M. Santosh; Sunil Saha; Omid Ghorbanzadeh; Jagabandhu Roy; John P. Tiefenbacher; Hossein Moayedi; Romulus Costache. Spatial prediction of shallow landslide: application of novel rotational forest-based reduced error pruning tree. Geomatics, Natural Hazards and Risk 2021, 12, 1343 -1370.

AMA Style

Alireza Arabameri, M. Santosh, Sunil Saha, Omid Ghorbanzadeh, Jagabandhu Roy, John P. Tiefenbacher, Hossein Moayedi, Romulus Costache. Spatial prediction of shallow landslide: application of novel rotational forest-based reduced error pruning tree. Geomatics, Natural Hazards and Risk. 2021; 12 (1):1343-1370.

Chicago/Turabian Style

Alireza Arabameri; M. Santosh; Sunil Saha; Omid Ghorbanzadeh; Jagabandhu Roy; John P. Tiefenbacher; Hossein Moayedi; Romulus Costache. 2021. "Spatial prediction of shallow landslide: application of novel rotational forest-based reduced error pruning tree." Geomatics, Natural Hazards and Risk 12, no. 1: 1343-1370.

Journal article
Published: 10 December 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Beyond the direct hazards of earthquakes, the deposited mass of earthquake-induced landslide (EQIL) in the riverbeds causing the river to thrust upward. The EQIL inventories are generated mostly by traditional or semi-supervised mapping approaches which required parameter's tuning or binary threshold decision in practical application. In this study, we investigated the impact of optical data from the PlanetScope sensor and topographic factors from the ALOS sensor on EQIL mapping using a deep-learning convolution neural network (CNN). Thus, six training datasets were prepared and used to evaluate the performance of the CNN model using only optical data and using this data along with each and all topographic factors across the west coast of the Trishuli River in Nepal. For the first time, the Dempster—Shafer (DS) model was applied for combining the resulting maps from each CNN stream that trained with different datasets. Finally, seven different resulting maps were compared against a detailed and accurate inventory of landslide polygons by a mean intersection-over-union (mIOU). Our results confirm that using the training dataset of the spectral information along with the topographic factor of the slope is helpful to distinguish the landslide bodies from other similar features such as barren lands and consequently increase the mapping accuracy. The improvement of the mIOU was a range from approximately zero to more than 17%. Moreover, the DS model can be considered as an optimizer method to combine the results from different scenarios.

ACS Style

Omid Ghorbanzadeh; Sansar Raj Meena; Hejar Shahabi Sorman Abadi; Sepideh Tavakkoli Piralilou; Lv Zhiyong; Thomas Blaschke. Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network Streams Combined by the Dempster–Shafer Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 14, 452 -463.

AMA Style

Omid Ghorbanzadeh, Sansar Raj Meena, Hejar Shahabi Sorman Abadi, Sepideh Tavakkoli Piralilou, Lv Zhiyong, Thomas Blaschke. Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network Streams Combined by the Dempster–Shafer Model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 14 (99):452-463.

Chicago/Turabian Style

Omid Ghorbanzadeh; Sansar Raj Meena; Hejar Shahabi Sorman Abadi; Sepideh Tavakkoli Piralilou; Lv Zhiyong; Thomas Blaschke. 2020. "Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network Streams Combined by the Dempster–Shafer Model." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 452-463.

Journal article
Published: 27 September 2020 in ISPRS International Journal of Geo-Information
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In this study, we used Sentinel-1 and Sentinel-2 data to delineate post-earthquake landslides within an object-based image analysis (OBIA). We used our resulting landslide inventory map for training the data-driven model of the frequency ratio (FR) for landslide susceptibility modelling and mapping considering eleven conditioning factors of soil type, slope angle, distance to roads, distance to rivers, rainfall, normalised difference vegetation index (NDVI), aspect, altitude, distance to faults, land cover, and lithology. A fuzzy analytic hierarchy process (FAHP) also was used for the susceptibility mapping using expert knowledge. Then, we integrated the data-driven model of the FR with the knowledge-based model of the FAHP to reduce the associated uncertainty in each approach. We validated our resulting landslide inventory map based on 30% of the global positioning system (GPS) points of an extensive field survey in the study area. The remaining 70% of the GPS points were used to validate the performance of the applied models and the resulting landslide susceptibility maps using the receiver operating characteristic (ROC) curves. Our resulting landslide inventory map got a precision of 94% and the AUCs (area under the curve) of the susceptibility maps showed 83%, 89%, and 96% for the F-AHP, FR, and the integrated model, respectively. The introduced methodology in this study can be used in the application of remote sensing data for landslide inventory and susceptibility mapping in other areas where earthquakes are considered as the main landslide-triggered factor.

ACS Style

Omid Ghorbanzadeh; Khalil Didehban; Hamid Rasouli; Khalil Valizadeh Kamran; Bakhtiar Feizizadeh; Thomas Blaschke. An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping. ISPRS International Journal of Geo-Information 2020, 9, 561 .

AMA Style

Omid Ghorbanzadeh, Khalil Didehban, Hamid Rasouli, Khalil Valizadeh Kamran, Bakhtiar Feizizadeh, Thomas Blaschke. An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping. ISPRS International Journal of Geo-Information. 2020; 9 (10):561.

Chicago/Turabian Style

Omid Ghorbanzadeh; Khalil Didehban; Hamid Rasouli; Khalil Valizadeh Kamran; Bakhtiar Feizizadeh; Thomas Blaschke. 2020. "An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping." ISPRS International Journal of Geo-Information 9, no. 10: 561.

Journal article
Published: 25 August 2020 in Remote Sensing
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We live in a sphere that has unpredictable and multifaceted landscapes that make the risk arising from several incidences that are omnipresent. Floods and landslides are widespread and recurring hazards occurring at an alarming rate in recent years. The importance of this study is to produce multi-hazard exposure maps for flooding and landslides for the federal State of Salzburg, Austria, using the selected machine learning (ML) approach of support vector machine (SVM) and random forest (RF). Multi-hazard exposure maps were established on thirteen influencing factors for flood and landslides such as elevation, slope, aspect, topographic wetness index (TWI), stream power index (SPI), normalized difference vegetation index (NDVI), geology, lithology, rainfall, land cover, distance to roads, distance to faults, and distance to drainage. We classified the inventory data for flood and landslide into training and validation with the widely used splitting ratio, where 70% of the locations are used for training, and 30% are used for validation. The accuracy assessment of the exposure maps was derived through ROC (receiver operating curve) and R-Index (relative density). RF yielded better results for both flood and landslide exposure with 0.87 for flood and 0.90 for landslides compared to 0.87 for flood and 0.89 for landslides using SVM. However, the multi-hazard exposure map for the State of Salzburg derived through RF and SVM provides the planners and managers to plan better for risk regions affected by both floods and landslides.

ACS Style

Thimmaiah Nachappa; Omid Ghorbanzadeh; Khalil Gholamnia; Thomas Blaschke. Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria. Remote Sensing 2020, 12, 2757 .

AMA Style

Thimmaiah Nachappa, Omid Ghorbanzadeh, Khalil Gholamnia, Thomas Blaschke. Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria. Remote Sensing. 2020; 12 (17):2757.

Chicago/Turabian Style

Thimmaiah Nachappa; Omid Ghorbanzadeh; Khalil Gholamnia; Thomas Blaschke. 2020. "Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria." Remote Sensing 12, no. 17: 2757.

Review article
Published: 08 July 2020 in Journal of Hydrology
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Floods are one of the most widespread natural hazards occurring across the globe. The main objective of this study was to produce flood susceptibility maps for the province of Salzburg, Austria, using two multi-criteria decision analysis (MCDA) models including analytical hierarchical process (AHP) and analytical network process (ANP) and two machine learning (ML) models including random forest (RF) and support vector machine (SVM). Additionally, we compare which of the MCDA and ML models are better suited for flood susceptibility and evaluate the use of Dempster Shafer Theory (DST) for optimising the resulting flood susceptibility maps based on eleven flood conditioning factors: elevation, slope, aspect, topographic wetness index (TWI), stream power index (SPI), normalised difference vegetation index (NDVI), geology, rainfall, land cover, distance to roads and distance to drainage. The accuracy evaluation of the flood susceptibility maps through the AUC (area under the receiver operating characteristic curve) method along with the relative flood density (R-Index) shows that RF (AUC = 87.8%) and SVM (AUC = 87%) outperform the ANP (AUC = 86.6%) and AHP (AUC = 85.9%) models. Therefore, the predictive performance of ML models was slightly better than the MCDA models. The DST could further increase the accuracy of both ML models (AUC = 88.3%) and MCDA models (AUC = 87.3%). However, the best accuracy (AUC = 89.3%) is reached through an ensemble of all four models.

ACS Style

Thimmaiah Gudiyangada Nachappa; Sepideh Tavakkoli Piralilou; Khalil Gholamnia; Omid Ghorbanzadeh; Omid Rahmati; Thomas Blaschke. Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory. Journal of Hydrology 2020, 590, 125275 .

AMA Style

Thimmaiah Gudiyangada Nachappa, Sepideh Tavakkoli Piralilou, Khalil Gholamnia, Omid Ghorbanzadeh, Omid Rahmati, Thomas Blaschke. Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory. Journal of Hydrology. 2020; 590 ():125275.

Chicago/Turabian Style

Thimmaiah Gudiyangada Nachappa; Sepideh Tavakkoli Piralilou; Khalil Gholamnia; Omid Ghorbanzadeh; Omid Rahmati; Thomas Blaschke. 2020. "Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory." Journal of Hydrology 590, no. : 125275.

Journal article
Published: 16 June 2020 in ISPRS International Journal of Geo-Information
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Landslides are one of the most detrimental geological disasters that intimidate human lives along with severe damages to infrastructures and they mostly occur in the mountainous regions across the globe. Landslide susceptibility mapping (LSM) serves as a key step in assessing potential areas that are prone to landslides and could have an impact on decreasing the possible damages. The application of the fuzzy best-worst multi-criteria decision-making (FBWM) method was applied for LSM in Austria. Further, the role of employing a few numbers of pairwise comparisons on LSM was investigated by comparing the FBWM and Fuzzy Analytical Hierarchical Process (FAHP). For this study, a wide range of data was sourced from the Geological Survey of Austria, the Austrian Land Information System, Humanitarian OpenStreetMap Team, and remotely sensed data were collected. We used nine conditioning factors that were based on the previous studies and geomorphological characteristics of Austria, such as elevation, slope, slope aspect, lithology, rainfall, land cover, distance to drainage, distance to roads, and distance to faults. Based on the evaluation of experts, the slope conditioning factor was chosen as the best criterion (highest impact on LSM) and the distance to roads was considered as the worst criterion (lowest impact on LSM). LSM was generated for the region based on the best and worst criterion. The findings show the robustness of FBWM in landslide susceptibility mapping. Additionally, using fewer pairwise comparisons revealed that the FBWM can obtain higher accuracy as compared to FAHP. The finding of this research can help authorities and decision-makers to provide effective strategies and plans for landslide prevention and mitigation at the national level.

ACS Style

Meisam Moharrami; Amin Naboureh; Thimmaiah Gudiyangada Nachappa; Omid Ghorbanzadeh; Xudong Guan; Thomas Blaschke. National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making. ISPRS International Journal of Geo-Information 2020, 9, 393 .

AMA Style

Meisam Moharrami, Amin Naboureh, Thimmaiah Gudiyangada Nachappa, Omid Ghorbanzadeh, Xudong Guan, Thomas Blaschke. National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making. ISPRS International Journal of Geo-Information. 2020; 9 (6):393.

Chicago/Turabian Style

Meisam Moharrami; Amin Naboureh; Thimmaiah Gudiyangada Nachappa; Omid Ghorbanzadeh; Xudong Guan; Thomas Blaschke. 2020. "National-Scale Landslide Susceptibility Mapping in Austria Using Fuzzy Best-Worst Multi-Criteria Decision-Making." ISPRS International Journal of Geo-Information 9, no. 6: 393.

Journal article
Published: 12 June 2020 in Remote Sensing
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The Zagros forests in Western Iran are valuable ecosystems that have been seriously damaged by human interference (harvesting the wood and forest sub-products, converting the forests to the agricultural lands, and grazing) and natural events (drought events and fire). In this study, we generated accurate land cover (LC), and tree canopy cover percentage (TCC%) maps for the forests of Shirvan County, a part of Zagros forests in Western Iran using Sentinel-2, Google Earth, and field data for protective management. First, we assessed the accuracy of Google Earth data using 300 random field plots in 10 different land cover types. For land cover mapping, we evaluated the performance of four supervised classification algorithms (minimum distance (MD), Mahalanobis distance (MaD), neural network (NN), and support vector machine (SVM)). The accuracy of the land cover maps was assessed using a set of 150 stratified random plots in Google Earth. We mapped the forest canopy cover by using the normalized difference vegetation index (NDVI) map, and field plots. We calculated the Pearson correlation between the NDVI values and the TCC% (obtained from field plots). The linear regression between the NDVI values and the TCC% was used to obtain the predictive model of TCC% based on the NDVI. The results showed that Google Earth data yielded an overall accuracy of 94.4%. The SVM algorithm had the highest accuracy for the classification of Sentinel-2 data with an overall accuracy of 81.33% and a kappa index of 0.76. The results of the forest canopy cover analysis showed a Pearson correlation coefficient of 0.93 between the NDVI and TCC%, which is highly significant. The results also showed that the linear regression model is a good predictive model for TCC% estimation based on the NDVI (r2 = 0.864). The results can be used as a baseline for decision-makers to monitor land cover change in the region, whether produced by human activities or natural events and to establish measures for protective management of forests.

ACS Style

Saeedeh Eskandari; Mohammad Reza Jaafari; Patricia Oliva; Omid Ghorbanzadeh; Thomas Blaschke. Mapping Land Cover and Tree Canopy Cover in Zagros Forests of Iran: Application of Sentinel-2, Google Earth, and Field Data. Remote Sensing 2020, 12, 1912 .

AMA Style

Saeedeh Eskandari, Mohammad Reza Jaafari, Patricia Oliva, Omid Ghorbanzadeh, Thomas Blaschke. Mapping Land Cover and Tree Canopy Cover in Zagros Forests of Iran: Application of Sentinel-2, Google Earth, and Field Data. Remote Sensing. 2020; 12 (12):1912.

Chicago/Turabian Style

Saeedeh Eskandari; Mohammad Reza Jaafari; Patricia Oliva; Omid Ghorbanzadeh; Thomas Blaschke. 2020. "Mapping Land Cover and Tree Canopy Cover in Zagros Forests of Iran: Application of Sentinel-2, Google Earth, and Field Data." Remote Sensing 12, no. 12: 1912.

Journal article
Published: 19 May 2020 in ISPRS International Journal of Geo-Information
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Land use types and anthropogenic activities represent considerable threats to groundwater pollution. To effectively monitor the groundwater quality, it is vital to measure pollution levels before they become severe. In our research area, located in Gilgit Baltistan in northern Pakistan, groundwater supplies are diminishing due to urban sprawl. In this study, we used a GIS-based DRASTIC model (Depth to water, Recharge, Aquifer media, Soil media, Topography, Impact of the vadose zone, Hydraulic conductivity) to analyze the area’s hydrological attributes to assess the groundwater susceptibility to pollution. Considering the importance of anthropogenic activities, this research primarily utilizes an adjusted DRASTIC model called DRASTICA, which incorporates anthropogenic impact as a parameter in the model. The resulting map, which depicts vulnerability to groundwater contamination, reveals that 19% of the study area is classed as having high vulnerability, 42% has moderate vulnerability, 37% has low vulnerability, and 2% has very low vulnerability to groundwater contamination. The adopted validation process (nitrate parameter of water quality) revealed that the suggested DRASTICA model achieved better results than the established DRASTIC model in a built-up environment. We used the nitrate concentration in groundwater to verify the formulated results, and the single parameter sensitivity analysis and map removal sensitivity analysis to analyze the model sensitivity. The sensitivity analysis indicated that the groundwater vulnerability to pollution is largely influenced by anthropogenic impact and depth to the water table, thereby suggesting that anthropogenic impact must be explicitly tackled in such studies. The groundwater zones exposed to anthropogenic pollution can be better classified with the help of the proposed DRASTICA model, particularly in and around built-up environments. The responsible authorities can use this groundwater contamination data as an early warning sign, so they can take practical actions to avoid extra pressure on this vital resource.

ACS Style

Ahsen Maqsoom; Bilal Aslam; Umer Khalil; Omid Ghorbanzadeh; Hassan Ashraf; Rana Faisal Faisal Tufail; Danish Farooq; Thomas Blaschke. A GIS-based DRASTIC Model and an Adjusted DRASTIC Model (DRASTICA) for Groundwater Susceptibility Assessment along the China–Pakistan Economic Corridor (CPEC) Route. ISPRS International Journal of Geo-Information 2020, 9, 332 .

AMA Style

Ahsen Maqsoom, Bilal Aslam, Umer Khalil, Omid Ghorbanzadeh, Hassan Ashraf, Rana Faisal Faisal Tufail, Danish Farooq, Thomas Blaschke. A GIS-based DRASTIC Model and an Adjusted DRASTIC Model (DRASTICA) for Groundwater Susceptibility Assessment along the China–Pakistan Economic Corridor (CPEC) Route. ISPRS International Journal of Geo-Information. 2020; 9 (5):332.

Chicago/Turabian Style

Ahsen Maqsoom; Bilal Aslam; Umer Khalil; Omid Ghorbanzadeh; Hassan Ashraf; Rana Faisal Faisal Tufail; Danish Farooq; Thomas Blaschke. 2020. "A GIS-based DRASTIC Model and an Adjusted DRASTIC Model (DRASTICA) for Groundwater Susceptibility Assessment along the China–Pakistan Economic Corridor (CPEC) Route." ISPRS International Journal of Geo-Information 9, no. 5: 332.

Digital earth observation
Published: 06 May 2020 in European Journal of Remote Sensing
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The availability and usage of optical very high spatial resolution (VHR) satellite images for efficient support of refugee/IDP (internally displaced people) camp planning and humanitarian aid are growing. In this research, an integrated approach was used for dwelling classification from VHR satellite images, which applied the preliminary results of a convolutional neural network (CNN) model as input data for an object-based image analysis (OBIA) knowledge-based semantic classification method. Unlike standard pixel-based classification methods that usually are applied for the CNN model, our integrated approach aggregates CNN results on separately delineated objects as the basic units of a rule-based classification, to include additional prior-knowledge and spatial concepts in the final instance segmentation. An object-based accuracy assessment methodology was used to assess the accuracy of the classified dwelling categories on a single object-level. Our findings reveal accuracies of more than 90% for each applied parameter of precision, recall and F1-score. We conclude that integrating the CNN models with the OBIA capabilities can be considered an efficient approach for dwelling extraction and classification, integrating not only sample derived knowledge but also prior-knowledge about refugee/IDP camp situations, like dwellings size constraints and additional context.

ACS Style

Omid Ghorbanzadeh; Dirk Tiede; Lorenz Wendt; Martin Sudmanns; Stefan Lang. Transferable instance segmentation of dwellings in a refugee camp - integrating CNN and OBIA. European Journal of Remote Sensing 2020, 54, 127 -140.

AMA Style

Omid Ghorbanzadeh, Dirk Tiede, Lorenz Wendt, Martin Sudmanns, Stefan Lang. Transferable instance segmentation of dwellings in a refugee camp - integrating CNN and OBIA. European Journal of Remote Sensing. 2020; 54 (sup1):127-140.

Chicago/Turabian Style

Omid Ghorbanzadeh; Dirk Tiede; Lorenz Wendt; Martin Sudmanns; Stefan Lang. 2020. "Transferable instance segmentation of dwellings in a refugee camp - integrating CNN and OBIA." European Journal of Remote Sensing 54, no. sup1: 127-140.

Journal article
Published: 10 April 2020 in Symmetry
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Climate change has increased the probability of the occurrence of catastrophes like wildfires, floods, and storms across the globe in recent years. Weather conditions continue to grow more extreme, and wildfires are occurring quite frequently and are spreading with greater intensity. Wildfires ravage forest areas, as recently seen in the Amazon, the United States, and more recently in Australia. The availability of remotely sensed data has vastly improved, and enables us to precisely locate wildfires for monitoring purposes. Wildfire inventory data was created by integrating the polygons collected through field surveys using global positioning systems (GPS) and the data collected from the moderate resolution imaging spectrometer (MODIS) thermal anomalies product between 2012 and 2017 for the study area. The inventory data, along with sixteen conditioning factors selected for the study area, was used to appraise the potential of various machine learning (ML) methods for wildfire susceptibility mapping in Amol County. The ML methods chosen for this study are artificial neural network (ANN), dmine regression (DR), DM neural, least angle regression (LARS), multi-layer perceptron (MLP), random forest (RF), radial basis function (RBF), self-organizing maps (SOM), support vector machine (SVM), and decision tree (DT), along with the statistical approach of logistic regression (LR), which is very apt for wildfire susceptibility studies. The wildfire inventory data was categorized as three-fold, with 66% being used for training the models and 33% being used for accuracy assessment within three-fold cross-validation (CV). Receiver operating characteristics (ROC) was used to assess the accuracy of the ML approaches. RF had the highest accuracy of 88%, followed by SVM with an accuracy of almost 79%, and LR had the lowest accuracy of 65%. This shows that RF is better suited for wildfire susceptibility assessments in our case study area.

ACS Style

Khalil Gholamnia; Thimmaiah Gudiyangada Nachappa; Omid Ghorbanzadeh; Thomas Blaschke. Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping. Symmetry 2020, 12, 604 .

AMA Style

Khalil Gholamnia, Thimmaiah Gudiyangada Nachappa, Omid Ghorbanzadeh, Thomas Blaschke. Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping. Symmetry. 2020; 12 (4):604.

Chicago/Turabian Style

Khalil Gholamnia; Thimmaiah Gudiyangada Nachappa; Omid Ghorbanzadeh; Thomas Blaschke. 2020. "Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping." Symmetry 12, no. 4: 604.

Journal article
Published: 14 March 2020 in International Journal of Environmental Research and Public Health
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Driver behavior has been considered as the most critical and uncertain criteria in the study of traffic safety issues. Driver behavior identification and categorization by using the Fuzzy Analytic Hierarchy Process (FAHP) can overcome the uncertainty of driver behavior by capturing the ambiguity of driver thinking style. The main goal of this paper is to examine the significant driver behavior criteria that influence traffic safety for different traffic cultures such as Hungary, Turkey, Pakistan and China. The study utilized the FAHP framework to compare and quantify the driver behavior criteria designed on a three-level hierarchical structure. The FAHP procedure computed the weight factors and ranked the significant driver behavior criteria based on pairwise comparisons (PCs) of driver’s responses on the Driver Behavior Questionnaire (DBQ). The study results observed “violations” as the most significant driver behavior criteria for level 1 by all nominated regions except Hungary. While for level 2, “aggressive violations” is observed as the most significant driver behavior criteria by all regions except Turkey. Moreover, for level 3, Hungary and Turkey drivers evaluated the “drive with alcohol use” as the most significant driver behavior criteria. While Pakistan and China drivers evaluated the “fail to yield pedestrian” as the most significant driver behavior criteria. Finally, Kendall’s agreement test was performed to measure the agreement degree between observed groups for each level in a hierarchical structure. The methodology applied can be easily transferable to other study areas and our results in this study can be helpful for the drivers of each region to focus on highlighted significant driver behavior criteria to reduce fatal and seriously injured traffic accidents.

ACS Style

Danish Farooq; Sarbast Moslem; Rana Faisal Tufail; Omid Ghorbanzadeh; Szabolcs Duleba; Ahsen Maqsoom; Thomas Blaschke. Analyzing the Importance of Driver Behavior Criteria Related to Road Safety for Different Driving Cultures. International Journal of Environmental Research and Public Health 2020, 17, 1893 .

AMA Style

Danish Farooq, Sarbast Moslem, Rana Faisal Tufail, Omid Ghorbanzadeh, Szabolcs Duleba, Ahsen Maqsoom, Thomas Blaschke. Analyzing the Importance of Driver Behavior Criteria Related to Road Safety for Different Driving Cultures. International Journal of Environmental Research and Public Health. 2020; 17 (6):1893.

Chicago/Turabian Style

Danish Farooq; Sarbast Moslem; Rana Faisal Tufail; Omid Ghorbanzadeh; Szabolcs Duleba; Ahsen Maqsoom; Thomas Blaschke. 2020. "Analyzing the Importance of Driver Behavior Criteria Related to Road Safety for Different Driving Cultures." International Journal of Environmental Research and Public Health 17, no. 6: 1893.

Journal article
Published: 13 March 2020 in Mathematics
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Driver behavior plays a major role in road safety because it is considered as a significant argument in traffic accident avoidance. Drivers mostly face various risky driving factors which lead to fatal accidents or serious injury. This study aims to evaluate and prioritize the significant driver behavior factors related to road safety. In this regard, we integrated a decision-making model of the Best-Worst Method (BWM) with the triangular fuzzy sets as a solution for optimizing our complex decision-making problem, which is associated with uncertainty and ambiguity. Driving characteristics are different in different driving situations which indicate the ambiguous and complex attitude of individuals, and decision-makers (DMs) need to improve the reliability of the decision. Since the crisp values of factors may be inadequate to model the real-world problem considering the vagueness and the ambiguity, and providing the pairwise comparisons with the requirement of less compared data, the BWM integrated with triangular fuzzy sets is used in the study to evaluate risky driver behavior factors for a designed three-level hierarchical structure. The model results provide the most significant driver behavior factors that influence road safety for each level based on evaluator responses on the Driver Behavior Questionnaire (DBQ). Moreover, the model generates a more consistent decision process by the new consistency ratio of F-BWM. An adaptable application process from the model is also generated for future attempts.

ACS Style

Sarbast Moslem; Muhammet Gul; Danish Farooq; Erkan Celik; Omid Ghorbanzadeh; Thomas Blaschke. An Integrated Approach of Best-Worst Method (BWM) and Triangular Fuzzy Sets for Evaluating Driver Behavior Factors Related to Road Safety. Mathematics 2020, 8, 414 .

AMA Style

Sarbast Moslem, Muhammet Gul, Danish Farooq, Erkan Celik, Omid Ghorbanzadeh, Thomas Blaschke. An Integrated Approach of Best-Worst Method (BWM) and Triangular Fuzzy Sets for Evaluating Driver Behavior Factors Related to Road Safety. Mathematics. 2020; 8 (3):414.

Chicago/Turabian Style

Sarbast Moslem; Muhammet Gul; Danish Farooq; Erkan Celik; Omid Ghorbanzadeh; Thomas Blaschke. 2020. "An Integrated Approach of Best-Worst Method (BWM) and Triangular Fuzzy Sets for Evaluating Driver Behavior Factors Related to Road Safety." Mathematics 8, no. 3: 414.

Journal article
Published: 04 March 2020 in Symmetry
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The main purpose of the present study was to mathematically integrate different decision support systems to enhance the accuracy of seismic vulnerability mapping in Sanandaj City, Iran. An earthquake is considered to be a catastrophe that poses a serious threat to human infrastructures at different scales. Factors affecting seismic vulnerability were identified in three different dimensions; social, environmental, and physical. Our computer-based modeling approach was used to create hybrid training datasets via fuzzy-multiple criteria analysis (fuzzy-MCDA) and multiple criteria decision analysis-multi-criteria evaluation (MCDA-MCE) for training the multi-criteria evaluation–logistic regression (MCE–LR) and fuzzy-logistic regression (fuzzy-LR) hybrid model. The resulting dataset was validated using the seismic relative index (SRI) method and ten damaged spots from the study area, in which the MCDA-MCE model showed higher accuracy. The hybrid learning models of MCE-LR and fuzzy-LR were implemented using both resulting datasets for seismic vulnerability mapping. Finally, the resulting seismic vulnerability maps based on each model were validation using area under curve (AUC) and frequency ratio (FR). Based on the accuracy assessment results, the MCDA-MCE hybrid model (AUC = 0.85) showed higher accuracy than the fuzzy-MCDA model (AUC = 0.80), and the MCE-LR hybrid model (AUC = 0.90) resulted in more accurate vulnerability map than the fuzzy-LR hybrid model (AUC = 0.85). The results of the present study show that the accuracy of modeling and mapping seismic vulnerability in our case study area is directly related to the accuracy of the training dataset.

ACS Style

Peyman Yariyan; Mohammadtaghi Avand; Fariba Soltani; Omid Ghorbanzadeh; Thomas Blaschke. Earthquake Vulnerability Mapping Using Different Hybrid Models. Symmetry 2020, 12, 405 .

AMA Style

Peyman Yariyan, Mohammadtaghi Avand, Fariba Soltani, Omid Ghorbanzadeh, Thomas Blaschke. Earthquake Vulnerability Mapping Using Different Hybrid Models. Symmetry. 2020; 12 (3):405.

Chicago/Turabian Style

Peyman Yariyan; Mohammadtaghi Avand; Fariba Soltani; Omid Ghorbanzadeh; Thomas Blaschke. 2020. "Earthquake Vulnerability Mapping Using Different Hybrid Models." Symmetry 12, no. 3: 405.

Journal article
Published: 05 February 2020 in Symmetry
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The use of driver behavior has been considered a complex way to solve road safety complications. Car drivers are usually involved in various risky driving factors which lead to accidents where people are fatally or seriously injured. The present study aims to dissect and rank the significant driver behavior factors related to road safety by applying an integrated multi-criteria decision-making (MCDM) model, which is structured as a hierarchy with at least one 5 × 5 (or bigger) pairwise comparison matrix (PCM). A real-world, complex decision-making problem was selected to evaluate the possible application of the proposed model (driver behavior preferences related to road safety problems). The application of the analytic hierarchy process (AHP) alone, by precluding layman participants, might cause a loss of reliable information in the case of the decision-making systems with big PCMs. Evading this tricky issue, we used the Best Worst Method (BWM) to make the layman’s evaluator task easier and timesaving. Therefore, the AHP-BWM model was found to be a suitable integration to evaluate risky driver behavior factors within a designed three-level hierarchical structure. The model results found the most significant driver behavior factors that influence road safety for each level, based on evaluator responses on the driver behavior questionnaire (DBQ). Moreover, the output vector of weights in the integrated model is more consistent, with results for 5 × 5 PCMs or bigger. The proposed AHP-BWM model can be used for PCMs with scientific data organized by traditional means.

ACS Style

Sarbast Moslem; Danish Farooq; Omid Ghorbanzadeh; Thomas Blaschke. Application of the AHP-BWM Model for Evaluating Driver Behavior Factors Related to Road Safety: A Case Study for Budapest. Symmetry 2020, 12, 243 .

AMA Style

Sarbast Moslem, Danish Farooq, Omid Ghorbanzadeh, Thomas Blaschke. Application of the AHP-BWM Model for Evaluating Driver Behavior Factors Related to Road Safety: A Case Study for Budapest. Symmetry. 2020; 12 (2):243.

Chicago/Turabian Style

Sarbast Moslem; Danish Farooq; Omid Ghorbanzadeh; Thomas Blaschke. 2020. "Application of the AHP-BWM Model for Evaluating Driver Behavior Factors Related to Road Safety: A Case Study for Budapest." Symmetry 12, no. 2: 243.

Journal article
Published: 01 January 2020 in Geomatics, Natural Hazards and Risk
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ACS Style

Peyman Yariyan; Mohammadtaghi Avand; Rahim Ali Abbaspour; Ali Torabi Haghighi; Romulus Costache; Omid Ghorbanzadeh; Saeid Janizadeh; Thomas Blaschke. Flood susceptibility mapping using an improved analytic network process with statistical models. Geomatics, Natural Hazards and Risk 2020, 11, 2282 -2314.

AMA Style

Peyman Yariyan, Mohammadtaghi Avand, Rahim Ali Abbaspour, Ali Torabi Haghighi, Romulus Costache, Omid Ghorbanzadeh, Saeid Janizadeh, Thomas Blaschke. Flood susceptibility mapping using an improved analytic network process with statistical models. Geomatics, Natural Hazards and Risk. 2020; 11 (1):2282-2314.

Chicago/Turabian Style

Peyman Yariyan; Mohammadtaghi Avand; Rahim Ali Abbaspour; Ali Torabi Haghighi; Romulus Costache; Omid Ghorbanzadeh; Saeid Janizadeh; Thomas Blaschke. 2020. "Flood susceptibility mapping using an improved analytic network process with statistical models." Geomatics, Natural Hazards and Risk 11, no. 1: 2282-2314.

Research article
Published: 01 January 2020 in Geomatics, Natural Hazards and Risk
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Gully erosion is a severe form of soil erosion that results in a wide range of environmental problems such as, dams’ sedimentation, destruction of transportation and energy transmission lines, decreasing and losing farmland productivity, and land degradation. The main objective of this study is to accurately map the areas prone to gully erosion, by developing two machine learning (ML) models, namely artificial neural network (ANN) and random forest (RF) models within 4-fold cross-validation (CV). Moreover, we used the multi-collinearity analysis to select 11 variables among 15 conditioning factors to train the ML models for gully erosion susceptibility mapping (GESM). Lamerd county, Iran, is chosen for a study area because Lamerd county is one of the most affected areas by gully erosion in this country. From 232 gully samples, 75% was used to train the two ML models and the rest of the samples (25%) were used to validate the generated GEMSs using 4-fold CV. The RF model produced a higher accuracy with an accuracy value of 93%. The GEMS generated by the RF model shows that the areas classified as highly vulnerable and very highly vulnerable are 1,869 ha and 5,148 ha, respectively. Results from the two models indicated that the most vulnerable land use/landcover class is bare land because of the low vegetation cover. The outcome of this study can help managers in Lamerd county to mitigate the soil erosion problem and prevent future gully erosion by taking preventive measures.

ACS Style

Omid Ghorbanzadeh; Hejar Shahabi; Fahimeh Mirchooli; Khalil Valizadeh Kamran; Samsung Lim; Jagannath Aryal; Ben Jarihani; Thomas Blaschke. Gully erosion susceptibility mapping (GESM) using machine learning methods optimized by the multi‑collinearity analysis and K-fold cross-validation. Geomatics, Natural Hazards and Risk 2020, 11, 1653 -1678.

AMA Style

Omid Ghorbanzadeh, Hejar Shahabi, Fahimeh Mirchooli, Khalil Valizadeh Kamran, Samsung Lim, Jagannath Aryal, Ben Jarihani, Thomas Blaschke. Gully erosion susceptibility mapping (GESM) using machine learning methods optimized by the multi‑collinearity analysis and K-fold cross-validation. Geomatics, Natural Hazards and Risk. 2020; 11 (1):1653-1678.

Chicago/Turabian Style

Omid Ghorbanzadeh; Hejar Shahabi; Fahimeh Mirchooli; Khalil Valizadeh Kamran; Samsung Lim; Jagannath Aryal; Ben Jarihani; Thomas Blaschke. 2020. "Gully erosion susceptibility mapping (GESM) using machine learning methods optimized by the multi‑collinearity analysis and K-fold cross-validation." Geomatics, Natural Hazards and Risk 11, no. 1: 1653-1678.