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Urmia Lake in Northern Iran is drying up, which is causing significant environmental problems in the region, including saline storms that devastate agricultural land. We developed a remote sensing-based monitoring application to detect and map the location of saline flow sources with a novel automated deep learning convolutional neural network (DL-CCN). In order to train the model, we derived a normalised difference dust index (NDDI) from MODIS satellite images and collected ground control points (GCPs). These GCPs were randomly split for training (70%) and accuracy assessment (30%). We identified the following seven predisposing factors for saline flow source modelling: normalised difference vegetation index (NDVI), humidity percentage, temperature, wind speed, geomorphology, soil and land use/cover. In order to train the DL-CNN, we used ReLu, the root mean square error function, and Stochastic Gradient Descent (SGD) for the activation, loss/cost function, and optimization, respectively. Finally, we used the frequency ratio (FR) method to identify the most effective variable for the prediction of saline storm occurrences. The results reveal a high confidence (91.86% overall accuracy and a Kappa of 90.26) for the detection of saline flow sources. According to the FR model, the NDVI (0.982), humidity percentage (0.963), and land use/cover (0.925) are the most relevant factors for detecting the occurrence of saline storms in the Urmia Lake basin. In addition, we carried out a spatial uncertainty analysis of the results based on the Dempster Shafer Theory. The results will help the local stakeholders and decision-makers to better understating the saline flow sources and their respective environmental impacts.
Bakhtiar Feizizadeh; Mohammad Kazemi Garajeh; Tobia Lakes; Thomas Blaschke. A deep learning convolutional neural network algorithm for detecting saline flow sources and mapping the environmental impacts of the Urmia Lake drought in Iran. CATENA 2021, 207, 105585 .
AMA StyleBakhtiar Feizizadeh, Mohammad Kazemi Garajeh, Tobia Lakes, Thomas Blaschke. A deep learning convolutional neural network algorithm for detecting saline flow sources and mapping the environmental impacts of the Urmia Lake drought in Iran. CATENA. 2021; 207 ():105585.
Chicago/Turabian StyleBakhtiar Feizizadeh; Mohammad Kazemi Garajeh; Tobia Lakes; Thomas Blaschke. 2021. "A deep learning convolutional neural network algorithm for detecting saline flow sources and mapping the environmental impacts of the Urmia Lake drought in Iran." CATENA 207, no. : 105585.
Landslide susceptibility analysis is beneficial information for a wide range of applications, including land use management plans. The present attempt has shed light on an efficient landslide susceptibility mapping framework that involves an adaptive neural-fuzzy inference system (ANFIS), which incorporates three metaheuristic methods including grey wolf optimization (GWO), particle swarm optimization (PSO), and shuffled frog leaping algorithm (SFLA) in the East Azerbaijan of Iran. To achieve this goal, 10 landslide occurrence-related influencing factors were pondered. A sum of 766 locations with landslide potentiality was recognized in the context of the study, and the Pearson correlation technique utilized in order to select the influencing factors in landslide models. The association between landslides and conditioning factors was also evaluated using a probability certainty factor (PCF) model. In the next phase, three data mining techniques were united with the ANFIS model, comprising ANFIS-grey wolf algorithm (ANFIS-GW), ANFIS-particle swarm optimization (ANFIS-PSO), and ANFIS-shuffled frog leaping algorithm (ANFIS-SFLA), were structured by the training dataset. Lastly, the receiver operating characteristic (ROC) and statistical procedures were utilized with the aim of validating and contrasting the predictive capability of the models. The findings of the study in terms of the Pearson correlation technique method for the importance ranking of conditioning factors in the context area uncovered that slope, aspect, normalized difference vegetation index (NDVI), and elevation have the highest impact on the occurrence of the landslide. All in all, the ANFIS-PSO model had high performance on both the training (RMSE = 0.288, MAE = 0.069, AUC = 0.89) and validation dataset (RMSE = 0.309, MAE = 0.097, AUC = 0.89), after which, the ANFIS-GWO model and the ANFIS-SFLA model demonstrated the second and third rates. Besides, the study revealed that benefiting the optimization algorithm with the proper selection of the techniques could facilitate landslide susceptibility modeling.
Solmaz Abdollahizad; Mohammad Ali Balafar; Bakhtiar Feizizadeh; Amin Babazadeh Sangar; Karim Samadzamini. Using hybrid artificial intelligence approach based on a neuro-fuzzy system and evolutionary algorithms for modeling landslide susceptibility in East Azerbaijan Province, Iran. Earth Science Informatics 2021, 1 -22.
AMA StyleSolmaz Abdollahizad, Mohammad Ali Balafar, Bakhtiar Feizizadeh, Amin Babazadeh Sangar, Karim Samadzamini. Using hybrid artificial intelligence approach based on a neuro-fuzzy system and evolutionary algorithms for modeling landslide susceptibility in East Azerbaijan Province, Iran. Earth Science Informatics. 2021; ():1-22.
Chicago/Turabian StyleSolmaz Abdollahizad; Mohammad Ali Balafar; Bakhtiar Feizizadeh; Amin Babazadeh Sangar; Karim Samadzamini. 2021. "Using hybrid artificial intelligence approach based on a neuro-fuzzy system and evolutionary algorithms for modeling landslide susceptibility in East Azerbaijan Province, Iran." Earth Science Informatics , no. : 1-22.
The concept of smart cities has gained significant momentum in science and policy circles over the past decade. This study aims to provide an overview of the structure and trends in the literature on smart cities. Bibliometric analysis and science mapping techniques using VOSviewer and CiteSpace are used to identify the thematic focus of over 5000 articles indexed in the Web of Science since 1991. In addition to providing insights into the thematic evolution of the field, the three-decade study period is divided into two sub-periods (1991–2015 and 2016–2021). While splitting the dataset into more sub-periods would have been desirable, we decided to only examine two sub-periods as only very few papers have been published until 2010. The annual number of publications has progressively increased since then, with a surge in the annual number of publications observable from 2015 onwards. The thematic analysis showed that the intellectual base of the field has been very limited during the first period, but has expanded significantly since 2015. Over time, some thematic evolutions, such as further attention to linkages to climate change and resilience, and more emphasis on security and privacy issues, have been made. The thematic analysis shows that existing research on smart cities is dominated by either conceptual issues or underlying technical aspects. It is, therefore, essential to do more research on the implementation of smart cities and actual and/or potential contributions of smart cities to solving societal issues. In addition to elaborating on thematic focus, the study also highlights major authors, journals, references, countries, and institutions that have contributed to the development of the smart cities literature.
Ayyoob Sharifi; Zaheer Allam; Bakhtiar Feizizadeh; Hessam Ghamari. Three Decades of Research on Smart Cities: Mapping Knowledge Structure and Trends. Sustainability 2021, 13, 7140 .
AMA StyleAyyoob Sharifi, Zaheer Allam, Bakhtiar Feizizadeh, Hessam Ghamari. Three Decades of Research on Smart Cities: Mapping Knowledge Structure and Trends. Sustainability. 2021; 13 (13):7140.
Chicago/Turabian StyleAyyoob Sharifi; Zaheer Allam; Bakhtiar Feizizadeh; Hessam Ghamari. 2021. "Three Decades of Research on Smart Cities: Mapping Knowledge Structure and Trends." Sustainability 13, no. 13: 7140.
In this study, ASTER imagery, geochemical, lithological, and structural data are exploited for Mineral Potential Mapping (MPM) of the Astaneh granitic pluton and its surrounding area. The independent component analysis (ICA) and Matched Filtering (MF) techniques are applied to ASTER data for detecting alteration mineral assemblages. Sericitically argillically altered minerals associated with jarosite and chlorite/epidote are mapped using the ICA technique. MF fraction images derived from n-dimensional visualisation (n-DV) tool facilitated detecting goethite, haematite, limonite, muscovite, kaolinite, illite, chlorite and epidote associated with gold occurrences. The distribution of Cu, Pb, Zn and Au is considered for generating geochemical anomaly layers. Strong Cu, Zn and Au anomalies are found to be associated with gold mineralisation. The lithological units hosting gold mineralisation and intersection of NE–SW and NW–SE trending lineaments are also considered. Fuzzy Logic Model (FLM) was used to generate gold prospectivity map for the study area by fusing the alteration, geochemical, geology and structural layers. Several high prospective zones are identified in the central and southeastern part of the study area. A majority of delineated exploration targets are either linked to the plutonic body or its surrounding metamorphic rocks. This study demonstrated a viable approach for future gold prospecting in the study area.
Hooman Moradpour; Ghodratollah Rostami Paydar; Bakhtiar Feizizadeh; Thomas Blaschke; Amin Beiranvand Pour; Khalil Valizadeh Kamran; Aidy M Muslim; Mohammad Shawkat Hossain. Fusion of ASTER satellite imagery, geochemical and geology data for gold prospecting in the Astaneh granite intrusive, West Central Iran. International Journal of Image and Data Fusion 2021, 1 -24.
AMA StyleHooman Moradpour, Ghodratollah Rostami Paydar, Bakhtiar Feizizadeh, Thomas Blaschke, Amin Beiranvand Pour, Khalil Valizadeh Kamran, Aidy M Muslim, Mohammad Shawkat Hossain. Fusion of ASTER satellite imagery, geochemical and geology data for gold prospecting in the Astaneh granite intrusive, West Central Iran. International Journal of Image and Data Fusion. 2021; ():1-24.
Chicago/Turabian StyleHooman Moradpour; Ghodratollah Rostami Paydar; Bakhtiar Feizizadeh; Thomas Blaschke; Amin Beiranvand Pour; Khalil Valizadeh Kamran; Aidy M Muslim; Mohammad Shawkat Hossain. 2021. "Fusion of ASTER satellite imagery, geochemical and geology data for gold prospecting in the Astaneh granite intrusive, West Central Iran." International Journal of Image and Data Fusion , no. : 1-24.
Ecotourism is a major tourism dimension that has received significant attention in recent years. It is widely known that the tourism industry, and in particular ecotourism, makes a significant contribution to sustainable development. In this context, the West Azerbaijan province is one of most important tourist destinations in Iran for attracting tourists interested in nature. The main purpose of this study was to analyze and map the potential for sustainable ecotourism development. The research methodology was developed based on GIS multi-criteria decision analysis (GIS-MCDA) techniques, using 28 spatial indicators. For this purpose, areas with the potential for attracting tourists were identified and the effective factors for increasing and decreasing tourism development activities were evaluated by GIS analysis. The method used as a multi-criteria spatial decision-making technique is based on the network analysis process and its combination with fuzzy logic is very effective for increasing the accuracy of the model and obtaining more realistic results. According to the results of this study, West Azerbaijan, due to its potential, including some areas unknown to tourists and even tourism planners, could be introduced as a nature tourism hub in the northwest of the country. The detailed tourism sustainability map obtained, at the scale of 1/25,000, could be used as a basis for regional planning and sustainable ecotourism development. In this context, the results could also be critical for tourism companies, agencies and local stakeholders and organizations for various applications, such as investment and development of tourism hospitality facilities and infrastructure in high potential areas. The research can also be considered as progressive in tourism research and supports future research on the selection of beneficial criteria and the application of efficient methods for tourism sustainability assessment and mapping.
Davoud Omarzadeh; Samereh Pourmoradian; Bakhtiar Feizizadeh; Hoda Khallaghi; Ayyoob Sharifi; Khalil Valizadeh Kamran. A GIS-based multiple ecotourism sustainability assessment of West Azerbaijan province, Iran. Journal of Environmental Planning and Management 2021, 1 -24.
AMA StyleDavoud Omarzadeh, Samereh Pourmoradian, Bakhtiar Feizizadeh, Hoda Khallaghi, Ayyoob Sharifi, Khalil Valizadeh Kamran. A GIS-based multiple ecotourism sustainability assessment of West Azerbaijan province, Iran. Journal of Environmental Planning and Management. 2021; ():1-24.
Chicago/Turabian StyleDavoud Omarzadeh; Samereh Pourmoradian; Bakhtiar Feizizadeh; Hoda Khallaghi; Ayyoob Sharifi; Khalil Valizadeh Kamran. 2021. "A GIS-based multiple ecotourism sustainability assessment of West Azerbaijan province, Iran." Journal of Environmental Planning and Management , no. : 1-24.
The primary objective is to propose and verify an ensemble approach based on fuzzy system and bivariate statistics for landslide susceptibility assessment (LSA) at Azarshahr Chay Basin (Iran). In this regard, various integrations of fuzzy membership value (FMV), frequency ratio (FR), and information value (IV) with index of entropy (IOE) were investigated. Aerial photograph interpretations and substantial field checking were used to identify the landslide locations. Out of 75 identified landslides, 52 (≈70%) locations were utilized for the training of the models, whereas the remaining 23 (≈30%) cases were employed for the validation of the models. Fourteen landslide conditioning factors including altitude, slope aspect, slope degree, lithology, distance to fault, curvature, land use, distance to river, topographic position index (TPI), topographic wetness index (TWI), stream power index (SPI), normalized difference vegetation index (NDVI), distance to road, and rainfall were prepared and utilized during the analysis. The \(\mathrm{FMV}\_\mathrm{IOE}\), \(\mathrm{FR}\_\mathrm{IOE}\), and \(\mathrm{IV}\_\mathrm{IOE}\) models were designed utilizing the dataset for training. Finally, to validate as well as to compare the model’s predictive abilities, the statistical measures of receiver operating characteristic (ROC), including sensitivity, accuracy, and specificity, were employed. The accuracy of 92.7, 92.5, and 91.8% of the models such as \(\mathrm{FMV}\_\mathrm{IOE}\), \(\mathrm{FR}\_\mathrm{IOE}\), and \(\mathrm{IV}\_\mathrm{IOE}\) ensembles, respectively, was by the area under the receiver operating characteristic (AUROC) values developed from the ROC curve. For the validation dataset, the \(\mathrm{FMV}\_\mathrm{IOE}\) model had the maximum sensitivity, accuracy, and specificity values of 95.7, 91.3, and 87.0%, respectively. Thus, the ensemble of FMV_IOE was introduced as a promising and premier approach that could be used for LSA in the study area. Also, IOE results indicated that altitude, lithology, and slope degree were main drivers of landslide occurrence. The results of the present research can be employed as a platform for appropriate basined management practices in order to plan the highly susceptible zones to landslide and hence minimize the expected losses.
Hassan Abedi Gheshlaghi; Bakhtiar Feizizadeh. GIS-based ensemble modelling of fuzzy system and bivariate statistics as a tool to improve the accuracy of landslide susceptibility mapping. Natural Hazards 2021, 107, 1981 -2014.
AMA StyleHassan Abedi Gheshlaghi, Bakhtiar Feizizadeh. GIS-based ensemble modelling of fuzzy system and bivariate statistics as a tool to improve the accuracy of landslide susceptibility mapping. Natural Hazards. 2021; 107 (2):1981-2014.
Chicago/Turabian StyleHassan Abedi Gheshlaghi; Bakhtiar Feizizadeh. 2021. "GIS-based ensemble modelling of fuzzy system and bivariate statistics as a tool to improve the accuracy of landslide susceptibility mapping." Natural Hazards 107, no. 2: 1981-2014.
Urbanization is an inevitable process all around the world especially in developing countries like Turkey. Istanbul has been experiencing rapid urban expansion for the past 60 years. This urban expansion is leading to the replacement of forests by various artificial surfaces. This situation has a critical impact on the natural surfaces due to the alteration of heat energy balance. In this study, the authors tried to investigate the extent of urbanization of Istanbul within the past decades to unearth its impacts on the urban heat island (UHI) severity and the level of its ecological consequences in terms of decreased thermal comfort. To this end, land use/cover (LULC) and land surface temperature (LST) maps were generated using Landsat imageries based on random forest (RF) classifier (as a machine learning tool) and radiometric image processing algorithms, respectively, for four different dates from 1989 to 2019. The statistical and spectral indicators were calculated for the study area to evaluate the association between urban development and UHI. Results indicate that Istanbul has suffered a continuous land transformation from forest to urban and croplands so that the area of forest has diminished by 373.3 km2, and the artificial surfaces have increased by 260 km2. Skin temperatures over all the LULC classes show an increase during the study period with the highest values estimated over artificial surfaces. The statistical analysis of urbanization indicators (ULI, PD, UGSI, NDVI, and NDBI) and UHI indicator (UTFVI) resulted in good correlation coefficients with the best agreement found between NDBI and UTFVI which stresses the strong link between the expansion of built-up areas as a result of urbanization and the severity of UHI and its ecological impacts in Istanbul. Thus, it is a must for policy-makers and officials of the city to take accurate measures regarding the urban planning to mitigate the harsh environmental impacts of growing urbanization of Istanbul in upcoming years.
Behnam Khorrami; Hadi Beygi Heidarlou; Bakhtiar Feizizadeh. Evaluation of the environmental impacts of urbanization from the viewpoint of increased skin temperatures: a case study from Istanbul, Turkey. Applied Geomatics 2021, 13, 311 -324.
AMA StyleBehnam Khorrami, Hadi Beygi Heidarlou, Bakhtiar Feizizadeh. Evaluation of the environmental impacts of urbanization from the viewpoint of increased skin temperatures: a case study from Istanbul, Turkey. Applied Geomatics. 2021; 13 (3):311-324.
Chicago/Turabian StyleBehnam Khorrami; Hadi Beygi Heidarlou; Bakhtiar Feizizadeh. 2021. "Evaluation of the environmental impacts of urbanization from the viewpoint of increased skin temperatures: a case study from Istanbul, Turkey." Applied Geomatics 13, no. 3: 311-324.
Landform mapping has increasingly become part of the digital domain. While the majority of approaches evaluates Digital Elevation Models (DEM) on a per-pixel basis, some examples exist were object-based image analysis (OBIA) has been applied to terrain data to identify a variety of landforms, including glacial landforms. The main objective of this study is to develop a semi-automated object-based rule set for detecting and delineating volcanic and glacier landforms in the area of the Sahand Mountain, Northern Iran. First, we applied a multi-resolution segmentation algorithm on a freely available Sentinel-2 optical satellite image and then selected the relevant features to define appropriate segmentation scales for each landform category. Object-based rule sets were then developed using spatial (DEM and its derivatives, e.g. slope, aspect, curvature and flow accumulation) and spectral information. Volcanic and glacial landforms were detected and classified into eight classes: caldera, volcanic cone, tuff formation, andesite lava, dacite lava, glacier valley, suspension valley, glacier cirque. An accuracy assessment was applied based on the fuzzy synthetic evaluation technique, together with the error matrix and kappa coefficient, using field data and geomorphological units derived from geological maps and very high resolution aerial photographs. The resulting overall accuracies for each class were 96.2%, 93.3%, 92.4%, 94.2%, 93.01, 95.1, 90.1 and 90.5, respectively. Our research demonstrated that spatial (e.g. density, shape index, length/width) and spectral (e.g. mean, brightness and standard deviation) algorithms together with a grey-level co-occurrence matrix (GLCMs) were efficient features for detecting and mapping volcanic and glacial landforms. We conclude that the OBIA-based algorithms and features provide a high potential for detecting and classifying landforms. Results of this study are of great importance for geomorphology and geology as well as geo-tourism societies and the semi-automated landform mapping contributes to the framework of GIScience.
Bakhtiar Feizizadeh; Mohammad Kazemi Garajeh; Thomas Blaschke; Tobia Lakes. An object based image analysis applied for volcanic and glacial landforms mapping in Sahand Mountain, Iran. CATENA 2020, 198, 105073 .
AMA StyleBakhtiar Feizizadeh, Mohammad Kazemi Garajeh, Thomas Blaschke, Tobia Lakes. An object based image analysis applied for volcanic and glacial landforms mapping in Sahand Mountain, Iran. CATENA. 2020; 198 ():105073.
Chicago/Turabian StyleBakhtiar Feizizadeh; Mohammad Kazemi Garajeh; Thomas Blaschke; Tobia Lakes. 2020. "An object based image analysis applied for volcanic and glacial landforms mapping in Sahand Mountain, Iran." CATENA 198, no. : 105073.
We propose an efficient integrated approach of spatial decision-making systems and geographical information science for spatially explicit sustainable development mapping. The approach was developed, and its efficiency examined for sustainability assessment in East Azerbaijan Province, Iran. To achieve this goal, sustainable development indicators were employed through GIS decision rule and spatial analysis. Accordingly, 13 main criteria and 44 sub-criteria were identified and prepared as GIS dataset. The fuzzy analytical network process (FANP) method was employed to derive the criteria weights and their significance. We also applied the Global Sensitivity Analysis (GSA) for minimizing the uncertainties associated with the FANP weights. The Ordered Weighted Averaging (OWA) method was applied to aggregate the indicators and develop the sustainable development maps. Results confirmed that integrated GIS-based decision rules can be applied for any sustainable development mapping efficiently. Results of this research present an approach for sustainable development assessment and can be applied for similar research effectually. In the case of East Azerbaijan Province, the detailed results represent the unbalanced sustainable development within the different counties of this province. This requires taking necessary actions to ensure more balanced and just economic development in the province. The degree of sustainable development shows a significant spatial correlation with the industrial activities, employment, demography, poverty and infrastructure properties. The obtained results are of great importance for decision makers to identify efficient approaches in light of sustainable development mapping.
Parviz Mohamadzadeh; Samereh Pourmoradian; Bakhtiar Feizizadeh; Ayyoob Sharifi; Mathias Vogdrup-Schmidt. A GIS-Based Approach for Spatially-Explicit Sustainable Development Assessments in East Azerbaijan Province, Iran. Sustainability 2020, 12, 10413 .
AMA StyleParviz Mohamadzadeh, Samereh Pourmoradian, Bakhtiar Feizizadeh, Ayyoob Sharifi, Mathias Vogdrup-Schmidt. A GIS-Based Approach for Spatially-Explicit Sustainable Development Assessments in East Azerbaijan Province, Iran. Sustainability. 2020; 12 (24):10413.
Chicago/Turabian StyleParviz Mohamadzadeh; Samereh Pourmoradian; Bakhtiar Feizizadeh; Ayyoob Sharifi; Mathias Vogdrup-Schmidt. 2020. "A GIS-Based Approach for Spatially-Explicit Sustainable Development Assessments in East Azerbaijan Province, Iran." Sustainability 12, no. 24: 10413.
The importance of freshwater for human societies and sustainable urban development is paramount. This study presents a new approach and framework for spatial modelling of urban drinking water consumption patterns (UDWCP) in light of a drinking water sustainability assessment. The approach was developed based on the GIS multi-criteria decision analysis (MCDA) and its efficiency was evaluated for spatial UDWPC mapping in Tabriz city, Iran. To achieve this goal, we identified the main water consumption indicators (WCI) (e.g. urban fields, population, land use/cover and their related sub-spatial factors), and determined their significance using the analytical network process. In addition, the sensitivity and uncertainty analyses was applied to reduce the inherent error in criteria weights. A GIS-based aggregation function was applied to discern the UDWCP map. Finally, regression analysis was used to determine the spatial correlation of the water consumption with different urban fields as well as the ratio of each WCI for the sustainability assessment objects were computed. According to results, urban fields, texture and demographic properties have a significant impact on the water consumption ratio. In the context of the urban texture, this study revealed that water consumption rates in both run-down and slums/informal settlements are relatively high. Results of this research provide a comprehensive understanding of UDWCP and their contribution to urban water consumption in Tabriz city. The importance of fresh water for human societies and the numerous issues related to water scarcity around the world is undisputed, and our proposed approach can be employed to efficiently model UDWCP and provide critical information for decision-makers and authorities worldwide to assist with the sustainable development of urban environments.
Bakhtiar Feizizadeh; Zahra Ronagh; Samreh Pourmoradian; Hassan Abedi Gheshlaghi; Tobia Lakes; Thomas Blaschke. An efficient GIS-based approach for sustainability assessment of urban drinking water consumption patterns: A study in Tabriz city, Iran. Sustainable Cities and Society 2020, 64, 102584 .
AMA StyleBakhtiar Feizizadeh, Zahra Ronagh, Samreh Pourmoradian, Hassan Abedi Gheshlaghi, Tobia Lakes, Thomas Blaschke. An efficient GIS-based approach for sustainability assessment of urban drinking water consumption patterns: A study in Tabriz city, Iran. Sustainable Cities and Society. 2020; 64 ():102584.
Chicago/Turabian StyleBakhtiar Feizizadeh; Zahra Ronagh; Samreh Pourmoradian; Hassan Abedi Gheshlaghi; Tobia Lakes; Thomas Blaschke. 2020. "An efficient GIS-based approach for sustainability assessment of urban drinking water consumption patterns: A study in Tabriz city, Iran." Sustainable Cities and Society 64, no. : 102584.
Forest fires are considered one of the most highly damaging and devastating of natural disasters, causing considerable casualties and financial losses every year. Hence, it is important to produce susceptibility maps for the management of forest fires so as to reduce their harmful effects. The purpose of this study is to map the susceptibility to forest fires over Nowshahr County in Iran, using an integrated approach of index of entropy (IOE) with fuzzy membership value (FMV), frequency ratio (FR), and information value (IV) with a comparison of their precision. The spatial database incorporated the inventory of forest fire and conditioning factors. As a whole, 41 forest fire locations were identified. Out of these, 29 locations (≈70%) were randomly chosen for the forest fire susceptibility modeling (FFSM), and the remaining 12 locations (≈30%) were utilized for the validation of the models. Subsequently, utilizing FMV‐IOE, FR‐IOE, and IV‐IOE models, forest fire susceptibility maps were acquired. Finally, the modeling ability of the models for FFSM was assessed using an area under the receiver operating characteristic (AUROC) curve. The results manifested that the prediction accuracy of the FMV‐IOE model is slightly higher than that of the FR‐IOE and IV‐IOE models. The incorporation of IOE with FMV, FR, and IV models had AUROC values of 0.890, 0.887, and 0.878, respectively. The resulting FFSM can be effective in fire repression resource planning, sustainable development, and primary warning in regions with similar conditions.
Hassan Abedi Gheshlaghi; Bakhtiar Feizizadeh; Thomas Blaschke; Tobia Lakes; Sapna Tajbar. Forest fire susceptibility modeling using hybrid approaches. Transactions in GIS 2020, 25, 311 -333.
AMA StyleHassan Abedi Gheshlaghi, Bakhtiar Feizizadeh, Thomas Blaschke, Tobia Lakes, Sapna Tajbar. Forest fire susceptibility modeling using hybrid approaches. Transactions in GIS. 2020; 25 (1):311-333.
Chicago/Turabian StyleHassan Abedi Gheshlaghi; Bakhtiar Feizizadeh; Thomas Blaschke; Tobia Lakes; Sapna Tajbar. 2020. "Forest fire susceptibility modeling using hybrid approaches." Transactions in GIS 25, no. 1: 311-333.
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.
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 StyleOmid 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 StyleOmid 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.
The use of satellite remote sensing imagery for mineral exploration is a fast and low-cost approach for indicating high potential zones. Exploration of iron skarn mineralization in metamorphic regions is challenging during the fieldwork campaign. In this study, Landsat-7 and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery were used for mapping alteration zones and geological structures associated with iron skarn mineralization in the Galali region, NW, Iran. Band ratio and Directed Principal Component Analysis (DPCA) and Automatic Lineament Extraction methods were applied to Landsat-7 data. Relative Absorption Band Depth (RBD) and Constrained Energy Minimization (CEM) algorithms were implemented to ASTER data. Fieldwork and laboratory analysis were conducted to verify the remote sensing results. Results indicate that propylitic alteration zone and intersection of N–S and NW–SE and curvilinear features are typically associated with iron skarn mineralization and some high potential zones are identified for future exploration projects.
Hooman Moradpour; Ghodratolah Rosatmi Paydar; Amin Biranvandi Pour; Khalil Valizadeh Kamran; Bakhtiar Feizizadeh; Aidy M Muslim; Mohammad Shawkat Hossain. Landsat-7 and ASTER remote sensing satellite imagery for identification of iron skarn mineralization in metamorphic regions. Geocarto International 2020, 1 -24.
AMA StyleHooman Moradpour, Ghodratolah Rosatmi Paydar, Amin Biranvandi Pour, Khalil Valizadeh Kamran, Bakhtiar Feizizadeh, Aidy M Muslim, Mohammad Shawkat Hossain. Landsat-7 and ASTER remote sensing satellite imagery for identification of iron skarn mineralization in metamorphic regions. Geocarto International. 2020; ():1-24.
Chicago/Turabian StyleHooman Moradpour; Ghodratolah Rosatmi Paydar; Amin Biranvandi Pour; Khalil Valizadeh Kamran; Bakhtiar Feizizadeh; Aidy M Muslim; Mohammad Shawkat Hossain. 2020. "Landsat-7 and ASTER remote sensing satellite imagery for identification of iron skarn mineralization in metamorphic regions." Geocarto International , no. : 1-24.
Flood is a typical natural disaster, which results in huge economic damage and human loss; therefore, accurately predicting flood-prone areas is important for preventing and mitigating the impacts of floods. The main objective of this study is to present new ensemble models, which are based on Index of Entropy (IOE), Fuzzy Membership Value (FMV), Frequency Ratio (FR), and Information Value (IV) for assessing flood susceptibility. For this purpose, data from a total of 78 flood events were taken into account as basic data for the training model and validation of results. Location and spatial characteristics of these historical flood events were used to identify the relevant criteria for flood susceptibility modeling (FSM) and in acquiring the contribution of each criterion in susceptibility of the region toward flood. The FMV-IOE, FR-IOE, and IV-IOE models were used to distinguish between presence and absence of flood and its mapping. These models were also employed to perform feature selection in order to reveal the variables, which may contribute for flood occurrence extensively. Finally, for the validation of results, the Area Under the Receiver Operating Characteristic (AUROC) was computed for each flood susceptibility map. The validation of results indicated that AUROC for three mentioned models varies from 0.963 to 0.969 (AUROC FMV-IOE = 96.9%, AUROC FR-IOE = 96.8%, and AUROC IV-IOE = 96.3%). Results acknowledged that the main drivers of flood occurrence were soil, land use, and SPI factors. The results of this research are of great importance for the task of mitigation and in reduction of the impacts of future losses, including land-use planning for the region under study. Current research makes a significant contribution to developing GISciences by means of proposing a new approach for GIS-based decision makings systems. From the methodological perspective, the results of this research are of great importance in analyzing the capability of hybrid intelligence techniques and their integration with fuzzy and GIS decision-making systems.
Bakhtiar Feizizadeh; Hassan Abedi Gheshlaghi; Dieu Tien Bui. An integrated approach of GIS and hybrid intelligence techniques applied for flood risk modeling. Journal of Environmental Planning and Management 2020, 64, 485 -516.
AMA StyleBakhtiar Feizizadeh, Hassan Abedi Gheshlaghi, Dieu Tien Bui. An integrated approach of GIS and hybrid intelligence techniques applied for flood risk modeling. Journal of Environmental Planning and Management. 2020; 64 (3):485-516.
Chicago/Turabian StyleBakhtiar Feizizadeh; Hassan Abedi Gheshlaghi; Dieu Tien Bui. 2020. "An integrated approach of GIS and hybrid intelligence techniques applied for flood risk modeling." Journal of Environmental Planning and Management 64, no. 3: 485-516.
Land subsidence occurrence in the Tasuj plane is becoming more frequent and hazardous in the near future due to the water crisis. To mitigate damage caused by land subsidence events, it is necessary to determine the susceptible or prone areas. This study focuses on producing and comparing land subsidence susceptibility map (LSSM) using boosted regression tree (BRT), random forest (RF), and classification and regression tree (CART) approaches with twelve influencing variables, namely altitude, slope angle, aspect, groundwater level, groundwater level change, land cover, lithology, distance to fault, distance to stream, stream power index, topographic wetness index, and plan curvature. Moreover, by implementing the Relief-F feature selection method, the most important variables in LSSM procedure were identified. The performance of the adopted methods was assessed using the area under the receiver operating characteristics curve (AUROC) and statistical evaluation indexes. The results showed that all the employed methods performed well; in particular, the BRT model (AUROC = 0.819) yielded higher prediction accuracy than RF (AUROC = 0.798) and CART (AUROC = 0.764). Findings of this study can assist in characterizing and mitigating the related hazard of land subsidence events.
Hamid Ebrahimy; Bakhtiar Feizizadeh; Saeed Salmani; Hossein Azadi. A comparative study of land subsidence susceptibility mapping of Tasuj plane, Iran, using boosted regression tree, random forest and classification and regression tree methods. Environmental Earth Sciences 2020, 79, 1 -12.
AMA StyleHamid Ebrahimy, Bakhtiar Feizizadeh, Saeed Salmani, Hossein Azadi. A comparative study of land subsidence susceptibility mapping of Tasuj plane, Iran, using boosted regression tree, random forest and classification and regression tree methods. Environmental Earth Sciences. 2020; 79 (10):1-12.
Chicago/Turabian StyleHamid Ebrahimy; Bakhtiar Feizizadeh; Saeed Salmani; Hossein Azadi. 2020. "A comparative study of land subsidence susceptibility mapping of Tasuj plane, Iran, using boosted regression tree, random forest and classification and regression tree methods." Environmental Earth Sciences 79, no. 10: 1-12.
Declining groundwater levels due to the absence of a planning system makes aquifers vulnerable to subsidence. This paper investigates possible hotspots in terms of Subsidence Vulnerability Indices (SVI) by applying the ALPRIFT framework, introduced recently by the authors by mirroring the procedure for the DRASTIC framework. ALPRIFT is suitable to cases, where data is sparse, and is the acronym of seven data layers to be presented in due course. It is a scoring technique, in which each data layer bears an aspect of land subsidence and is prescribed with rates to account for local variability, and with prescribed weights to account for relative significance of the data layer. The inherent subjectivity in prescribed weights is treated in this paper by learning their values from site-specific data by the strategy of using artificial intelligence to learn from multiple models (AIMM). The strategy has two levels: (i) at Level 1, three fuzzy models are used to learn weight values from the local data and from observed target data, and (ii) at Level 2, genetic expression algorithm (GEP) is used to learn further, in which the outputs of the models at Level 1 are reused as its inputs and observed data as its target values. The results show that (i) the Nash-Sutcliff Efficiency (NSE) coefficient for ALPRIFT with measured land subsidence values is approx. 0.21; (ii) NSE is improved to 0.88 by learning the weights at Level 1 using fuzzy logic, and (iii) NSE is further improved to 0.94 by further learning at Level 2 using GEP.
Ata Allah Nadiri; Rahman Khatibi; Pari Khalifi; Bakhtiar Feizizadeh. A study of subsidence hotspots by mapping vulnerability indices through innovatory ‘ALPRIFT’ using artificial intelligence at two levels. Bulletin of Engineering Geology and the Environment 2020, 79, 3989 -4003.
AMA StyleAta Allah Nadiri, Rahman Khatibi, Pari Khalifi, Bakhtiar Feizizadeh. A study of subsidence hotspots by mapping vulnerability indices through innovatory ‘ALPRIFT’ using artificial intelligence at two levels. Bulletin of Engineering Geology and the Environment. 2020; 79 (8):3989-4003.
Chicago/Turabian StyleAta Allah Nadiri; Rahman Khatibi; Pari Khalifi; Bakhtiar Feizizadeh. 2020. "A study of subsidence hotspots by mapping vulnerability indices through innovatory ‘ALPRIFT’ using artificial intelligence at two levels." Bulletin of Engineering Geology and the Environment 79, no. 8: 3989-4003.
Hossein Nazmfar; Saeideh Alavi; Bakhtiar Feizizadeh; Reza Masodifar; Ali Eshghei. Spatial Analysis of Security and Insecurity in Urban Parks: A Case Study of Tehran, Iran. The Professional Geographer 2020, 72, 383 -397.
AMA StyleHossein Nazmfar, Saeideh Alavi, Bakhtiar Feizizadeh, Reza Masodifar, Ali Eshghei. Spatial Analysis of Security and Insecurity in Urban Parks: A Case Study of Tehran, Iran. The Professional Geographer. 2020; 72 (3):383-397.
Chicago/Turabian StyleHossein Nazmfar; Saeideh Alavi; Bakhtiar Feizizadeh; Reza Masodifar; Ali Eshghei. 2020. "Spatial Analysis of Security and Insecurity in Urban Parks: A Case Study of Tehran, Iran." The Professional Geographer 72, no. 3: 383-397.
Different methods have been proposed to create seismic site condition maps. Ground-based methods are time-consuming in many places and require a lot of manual work. One method suggests topographic data as a proxy for seismic site condition of large areas. In this study, we mainly focused on the use of an ASTER 1c digital elevation model (DEM) to produce Vs30 maps throughout Iran using a GIS-based regression analysis of Vs30 measurements at 514 seismic stations. These maps were found to be comparable with those that were previously created from SRTM 30c data. The Vs30 results from ASTER 1c estimated the higher velocities better than those from SRTM 30c. In addition, a combination of ASTER 1c and SRTM 30c amplification maps can be useful for the detection of geological and geomorphological units. We also classified the terrain surface of six seismotectonic regions in Iran into 16 classes, considering three important criteria (slope, convexity and texture) to extract more information about the location and morphological characteristics of the stations. The results show that 98% of the stations are situated in six classes, 30% of which are in class 12, 27% in class 6, 17% in class 9, 16% in class 3, 4% in class 3and the rest of the stations are located in other classes.
Sadra Karimzadeh; Bakhtiar Feizizadeh; Masashi Matsuoka. DEM-Based Vs30 Map and Terrain Surface Classification in Nationwide Scale—A Case Study in Iran. ISPRS International Journal of Geo-Information 2019, 8, 537 .
AMA StyleSadra Karimzadeh, Bakhtiar Feizizadeh, Masashi Matsuoka. DEM-Based Vs30 Map and Terrain Surface Classification in Nationwide Scale—A Case Study in Iran. ISPRS International Journal of Geo-Information. 2019; 8 (12):537.
Chicago/Turabian StyleSadra Karimzadeh; Bakhtiar Feizizadeh; Masashi Matsuoka. 2019. "DEM-Based Vs30 Map and Terrain Surface Classification in Nationwide Scale—A Case Study in Iran." ISPRS International Journal of Geo-Information 8, no. 12: 537.
Soil degradation, defined as the lowering and loss of soil functions, is becoming a serious problem worldwide and threatens agricultural production and terrestrial ecosystems. The surface residue of crops is one of the most effective erosion control measures and it increases the soil moisture content. In some areas of the world, the management of soil surface residue (SSR) is crucial for increasing soil fertility, maintaining high soil carbon levels, and reducing the degradation of soil due to rain and wind erosion. Standard methods of measuring the residue cover are time and labor intensive, but remote sensing can support the monitoring of conservation tillage practices applied to large fields. We investigated the potential of per-pixel and object-based image analysis (OBIA) for detecting and estimating the coverage of SSRs after tillage and planting practices for agricultural research fields in Iran using tillage indices for Landsat-8 and novel indices for Sentinel-2A. For validation, SSR was measured in the field through line transects at the beginning of the agricultural season (prior to autumn crop planting). Per-pixel approaches for Landsat-8 satellite images using normalized difference tillage index (NDTI) and simple tillage index (STI) yielded coefficient of determination (R2) values of 0.727 and 0.722, respectively. We developed comparable novel indices for Sentinel-2A satellite data that yielded R2 values of 0.760 and 0.759 for NDTI and STI, respectively, which means that the Sentinel data better matched the ground truth data. We tested several OBIA methods and achieved very high overall accuracies of up to 0.948 for Sentinel-2A and 0.891 for Landsat-8 with a membership function method. The OBIA methods clearly outperformed per-pixel approaches in estimating SSR and bear the potential to substitute or complement ground-based techniques.
Payam Najafi; Hossein Navid; Bakhtiar Feizizadeh; Iraj Eskandari; Thomas Blaschke. Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue. Remote Sensing 2019, 11, 2583 .
AMA StylePayam Najafi, Hossein Navid, Bakhtiar Feizizadeh, Iraj Eskandari, Thomas Blaschke. Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue. Remote Sensing. 2019; 11 (21):2583.
Chicago/Turabian StylePayam Najafi; Hossein Navid; Bakhtiar Feizizadeh; Iraj Eskandari; Thomas Blaschke. 2019. "Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue." Remote Sensing 11, no. 21: 2583.
Bakhtiar Feizizadeh; Hejar Shahabi Sorman Abadi; Khalil Didehban; Thomas Blaschke; Franz Neubauer. Object-Based Thermal Remote-Sensing Analysis for Fault Detection in Mashhad County, Iran. Canadian Journal of Remote Sensing 2019, 45, 847 -861.
AMA StyleBakhtiar Feizizadeh, Hejar Shahabi Sorman Abadi, Khalil Didehban, Thomas Blaschke, Franz Neubauer. Object-Based Thermal Remote-Sensing Analysis for Fault Detection in Mashhad County, Iran. Canadian Journal of Remote Sensing. 2019; 45 (6):847-861.
Chicago/Turabian StyleBakhtiar Feizizadeh; Hejar Shahabi Sorman Abadi; Khalil Didehban; Thomas Blaschke; Franz Neubauer. 2019. "Object-Based Thermal Remote-Sensing Analysis for Fault Detection in Mashhad County, Iran." Canadian Journal of Remote Sensing 45, no. 6: 847-861.