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Thomas Blaschke
Department of Geoinformatics, University of Salzburg, Austria

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Short Biography

Thomas Blaschke is a full Professor at the University of Salzburg, Deputy Chair of the Department of Geoinformatics – Z_GIS and Director of the Doctoral College GIScience. His research interests include methodological issues of the integration of GIS, remote sensing, and image processing and aspects of participation and human-environment interaction. His academic record includes several temporary affiliations as guest professor and visiting scientist in Germany, the UK, and the US, and 470 scientific publications. He is author, co-author, or editor of 16 books and received academic prices and awards, including the Christian-Doppler Prize 1995.

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Research article
Published: 19 April 2021 in International Journal of Image and Data Fusion
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Hooman 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.

Journal article
Published: 07 April 2021 in ISPRS International Journal of Geo-Information
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Global sensitivity analysis, like variance-based methods for massive raster datasets, is especially computationally costly and memory-intensive, limiting its applicability for commodity cluster computing. The computational effort depends mainly on the number of model runs, the spatial, spectral, and temporal resolutions, the number of criterion maps, and the model complexity. The current Spatially-Explicit Uncertainty and Sensitivity Analysis (SEUSA) approach employs a cluster-based parallel and distributed Python–Dask solution for large-scale spatial problems, which validates and quantifies the robustness of spatial model solutions. This paper presents the design of a framework to perform SEUSA as a Service in a cloud-based environment scalable to very large raster datasets and applicable to various domains, such as landscape assessment, site selection, risk assessment, and land-use management. It incorporates an automated Kubernetes service for container virtualization, comprising a set of microservices to perform SEUSA as a Service. Implementing the proposed framework will contribute to a more robust assessment of spatial multi-criteria decision-making applications, facilitating a broader access to SEUSA by the research community and, consequently, leading to higher quality decision analysis.

ACS Style

Christoph Erlacher; Karl-Heinrich Anders; Piotr Jankowski; Gernot Paulus; Thomas Blaschke. A Framework for Cloud-Based Spatially-Explicit Uncertainty and Sensitivity Analysis in Spatial Multi-Criteria Models. ISPRS International Journal of Geo-Information 2021, 10, 244 .

AMA Style

Christoph Erlacher, Karl-Heinrich Anders, Piotr Jankowski, Gernot Paulus, Thomas Blaschke. A Framework for Cloud-Based Spatially-Explicit Uncertainty and Sensitivity Analysis in Spatial Multi-Criteria Models. ISPRS International Journal of Geo-Information. 2021; 10 (4):244.

Chicago/Turabian Style

Christoph Erlacher; Karl-Heinrich Anders; Piotr Jankowski; Gernot Paulus; Thomas Blaschke. 2021. "A Framework for Cloud-Based Spatially-Explicit Uncertainty and Sensitivity Analysis in Spatial Multi-Criteria Models." ISPRS International Journal of Geo-Information 10, no. 4: 244.

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: 17 February 2021 in Remote Sensing
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The morphological characteristics of yardangs are the direct evidence that reveals the wind and fluvial erosion for lacustrine sediments in arid areas. These features can be critical indicators in reconstructing local wind directions and environment conditions. Thus, the fast and accurate extraction of yardangs is key to studying their regional distribution and evolution process. However, the existing automated methods to characterize yardangs are of limited generalization that may only be feasible for specific types of yardangs in certain areas. Deep learning methods, which are superior in representation learning, provide potential solutions for mapping yardangs with complex and variable features. In this study, we apply Mask region-based convolutional neural networks (Mask R-CNN) to automatically delineate and classify yardangs using very high spatial resolution images from Google Earth. The yardang field in the Qaidam Basin, northwestern China is selected to conduct the experiments and the method yields mean average precisions of 0.869 and 0.671 for intersection of union (IoU) thresholds of 0.5 and 0.75, respectively. The manual validation results on images of additional study sites show an overall detection accuracy of 74%, while more than 90% of the detected yardangs can be correctly classified and delineated. We then conclude that Mask R-CNN is a robust model to characterize multi-scale yardangs of various types and allows for the research of the morphological and evolutionary aspects of aeolian landform.

ACS Style

Bowen Gao; Ninghua Chen; Thomas Blaschke; Chase Wu; Jianyu Chen; Yaochen Xu; Xiaoping Yang; Zhenhong Du. Automated Characterization of Yardangs Using Deep Convolutional Neural Networks. Remote Sensing 2021, 13, 733 .

AMA Style

Bowen Gao, Ninghua Chen, Thomas Blaschke, Chase Wu, Jianyu Chen, Yaochen Xu, Xiaoping Yang, Zhenhong Du. Automated Characterization of Yardangs Using Deep Convolutional Neural Networks. Remote Sensing. 2021; 13 (4):733.

Chicago/Turabian Style

Bowen Gao; Ninghua Chen; Thomas Blaschke; Chase Wu; Jianyu Chen; Yaochen Xu; Xiaoping Yang; Zhenhong Du. 2021. "Automated Characterization of Yardangs Using Deep Convolutional Neural Networks." Remote Sensing 13, no. 4: 733.

Journal article
Published: 05 February 2021 in Journal of Environmental Management
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Land subsidence (LS) in arid and semi-arid areas, such as Iran, is a significant threat to sustainable land management. The purpose of this study is to predict the LS distribution by generating land subsidence susceptibility models (LSSMs) for the Shahroud plain in Iran using three different multi-criteria decision making (MCDM) and five different artificial intelligence (AI) models. The MCDM models we used are the VlseKriterijumska Optimizacija IKompromisno Resenje (VIKOR), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Complex Proportional Assessment (COPRAS), and the AI models are the extreme gradient boosting (XGBoost), Cubist, Elasticnet, Bayesian multivariate adaptive regression spline (BMARS) and conditional random forest (Cforest) methods. We used the Receiver Operating Characteristic (ROC) curve, Area Under Curve (AUC) and different statistical indices,i.e. accuracy, sensitivity, specificity, F score, Kappa, Mean Absolute Error (MAE) and Nash-Sutcliffe Criteria (NSC)to validate and evaluate the methods. Based on the different validation techniques, the Cforest method yielded the best results with minimum and maximum values of 0.04 and 0.99, respectively. According to the Cforest model, 30.55% of the study area is extremely vulnerable to land subsidence. The results of our research will be of great help to planners and policy makers in the identification of the most vulnerable regions and the implementation of appropriate development strategies in this area.

ACS Style

Alireza Arabameri; Subodh Chandra Pal; Fatemeh Rezaie; Rabin Chakrabortty; Indrajit Chowdhuri; Thomas Blaschke; Phuong Thao Thi Ngo. Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation. Journal of Environmental Management 2021, 284, 112067 .

AMA Style

Alireza Arabameri, Subodh Chandra Pal, Fatemeh Rezaie, Rabin Chakrabortty, Indrajit Chowdhuri, Thomas Blaschke, Phuong Thao Thi Ngo. Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation. Journal of Environmental Management. 2021; 284 ():112067.

Chicago/Turabian Style

Alireza Arabameri; Subodh Chandra Pal; Fatemeh Rezaie; Rabin Chakrabortty; Indrajit Chowdhuri; Thomas Blaschke; Phuong Thao Thi Ngo. 2021. "Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation." Journal of Environmental Management 284, no. : 112067.

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.

Journal article
Published: 19 January 2021 in Water
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Recurrent floods are one of the major global threats among people, particularly in developing countries like India, as this nation has a tropical monsoon type of climate. Therefore, flood susceptibility (FS) mapping is indeed necessary to overcome this type of natural hazard phenomena. With this in mind, we evaluated the prediction performance of FS mapping in the Koiya River basin, Eastern India. The present research work was done through preparation of a sophisticated flood inventory map; eight flood conditioning variables were selected based on the topography and hydro-climatological condition, and by applying the novel ensemble approach of hyperpipes (HP) and support vector regression (SVR) machine learning (ML) algorithms. The ensemble approach of HP-SVR was also compared with the stand-alone ML algorithms of HP and SVR. In relative importance of variables, distance to river was the most dominant factor for flood occurrences followed by rainfall, land use land cover (LULC), and normalized difference vegetation index (NDVI). The validation and accuracy assessment of FS maps was done through five popular statistical methods. The result of accuracy evaluation showed that the ensemble approach is the most optimal model (AUC = 0.915, sensitivity = 0.932, specificity = 0.902, accuracy = 0.928 and Kappa = 0.835) in FS assessment, followed by HP (AUC = 0.885) and SVR (AUC = 0.871).

ACS Style

Asish Saha; Subodh Pal; Alireza Arabameri; Thomas Blaschke; Somayeh Panahi; Indrajit Chowdhuri; Rabin Chakrabortty; Romulus Costache; Aman Arora. Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms. Water 2021, 13, 241 .

AMA Style

Asish Saha, Subodh Pal, Alireza Arabameri, Thomas Blaschke, Somayeh Panahi, Indrajit Chowdhuri, Rabin Chakrabortty, Romulus Costache, Aman Arora. Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms. Water. 2021; 13 (2):241.

Chicago/Turabian Style

Asish Saha; Subodh Pal; Alireza Arabameri; Thomas Blaschke; Somayeh Panahi; Indrajit Chowdhuri; Rabin Chakrabortty; Romulus Costache; Aman Arora. 2021. "Flood Susceptibility Assessment Using Novel Ensemble of Hyperpipes and Support Vector Regression Algorithms." Water 13, no. 2: 241.

Research article
Published: 17 January 2021 in GIScience & Remote Sensing
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Construction of transportation infrastructure is a vital step in boosting economic and societal opportunities and often results in land use changes. In this study, we focus on the land use dynamics of the urban agglomeration around Hangzhou Bay, where the Qiantang River flows into the East China Sea. The Hangzhou Bay Bridge crosses the bay since 2008. We used Interrupted Time Series Analysis (ITSA) to analyze the influence of the bridge on the land use and land cover (LULC) time series of the surrounding areas and on socio-economic indicators. We applied the Random Forest method to classify Landsat imagery from 2000 to 2017, thus enabling us to quantify LULC changes before and after the construction of the Hangzhou Bay Bridge. Google Earth Engine (GEE) was used for data acquisition, pre-processing, and classification. The results showed that during the period from 2000 to 2017, impervious surface areas expanded rapidly at the expense of agricultural land, and this transformation continued even more rapidly after 2008. ITSA showed that the driver behind the impervious surface area expansion switched from residential and industrial area growth in 2000–2008, to exclusively infrastructure area growth in 2008–2017. The construction of the bridge accelerated the expansion of impervious surface in the joint area of the bridge-connected cities of Ningbo and Jiaxing. With the Hangzhou Bay Bridge connection, various socio-economic factors, including tourism, GDP, tertiary industry, real estate investment, and highway freight, increased rapidly. The outcomes of this research could contribute to policymaking and impact assessments for sustainable urban development and land management. The methods used in this study are universal and therefore can also be used to assess the effect of any notable event that may impact LULC change.

ACS Style

Lixia Chu; Yuting Zou; Dainius Masiliūnas; Thomas Blaschke; Jan Verbesselt. Assessing the impact of bridge construction on the land use/cover and socio-economic indicator time series: A case study of Hangzhou Bay Bridge. GIScience & Remote Sensing 2021, 58, 199 -216.

AMA Style

Lixia Chu, Yuting Zou, Dainius Masiliūnas, Thomas Blaschke, Jan Verbesselt. Assessing the impact of bridge construction on the land use/cover and socio-economic indicator time series: A case study of Hangzhou Bay Bridge. GIScience & Remote Sensing. 2021; 58 (2):199-216.

Chicago/Turabian Style

Lixia Chu; Yuting Zou; Dainius Masiliūnas; Thomas Blaschke; Jan Verbesselt. 2021. "Assessing the impact of bridge construction on the land use/cover and socio-economic indicator time series: A case study of Hangzhou Bay Bridge." GIScience & Remote Sensing 58, no. 2: 199-216.

Journal article
Published: 04 January 2021 in Sensors
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There is an evident increase in the importance that remote sensing sensors play in the monitoring and evaluation of natural hazards susceptibility and risk. The present study aims to assess the flash-flood potential values, in a small catchment from Romania, using information provided remote sensing sensors and Geographic Informational Systems (GIS) databases which were involved as input data into a number of four ensemble models. In a first phase, with the help of high-resolution satellite images from the Google Earth application, 481 points affected by torrential processes were acquired, another 481 points being randomly positioned in areas without torrential processes. Seventy percent of the dataset was kept as training data, while the other 30% was assigned to validating sample. Further, in order to train the machine learning models, information regarding the 10 flash-flood predictors was extracted in the training sample locations. Finally, the following four ensembles were used to calculate the Flash-Flood Potential Index across the Bâsca Chiojdului river basin: Deep Learning Neural Network–Frequency Ratio (DLNN-FR), Deep Learning Neural Network–Weights of Evidence (DLNN-WOE), Alternating Decision Trees–Frequency Ratio (ADT-FR) and Alternating Decision Trees–Weights of Evidence (ADT-WOE). The model’s performances were assessed using several statistical metrics. Thus, in terms of Sensitivity, the highest value of 0.985 was achieved by the DLNN-FR model, meanwhile the lowest one (0.866) was assigned to ADT-FR ensemble. Moreover, the specificity analysis shows that the highest value (0.991) was attributed to DLNN-WOE algorithm, while the lowest value (0.892) was achieved by ADT-FR. During the training procedure, the models achieved overall accuracies between 0.878 (ADT-FR) and 0.985 (DLNN-WOE). K-index shows again that the most performant model was DLNN-WOE (0.97). The Flash-Flood Potential Index (FFPI) values revealed that the surfaces with high and very high flash-flood susceptibility cover between 46.57% (DLNN-FR) and 59.38% (ADT-FR) of the study zone. The use of the Receiver Operating Characteristic (ROC) curve for results validation highlights the fact that FFPIDLNN-WOE is characterized by the most precise results with an Area Under Curve of 0.96.

ACS Style

Romulus Costache; Alireza Arabameri; Thomas Blaschke; Quoc Pham; Binh Pham; Manish Pandey; Aman Arora; Nguyen Linh; Iulia Costache. Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors. Sensors 2021, 21, 280 .

AMA Style

Romulus Costache, Alireza Arabameri, Thomas Blaschke, Quoc Pham, Binh Pham, Manish Pandey, Aman Arora, Nguyen Linh, Iulia Costache. Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors. Sensors. 2021; 21 (1):280.

Chicago/Turabian Style

Romulus Costache; Alireza Arabameri; Thomas Blaschke; Quoc Pham; Binh Pham; Manish Pandey; Aman Arora; Nguyen Linh; Iulia Costache. 2021. "Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors." Sensors 21, no. 1: 280.

Journal article
Published: 15 December 2020 in ISPRS International Journal of Geo-Information
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Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytics to get a better overview of the whole ‘forest’ of these new data sources and analysis methods. The main discussion revolves around the reliability of using big data from social media platforms or sensors, and how information can be extracted from massive amounts of data through novel analysis methods, such as machine learning, for better-informed decision making aiming at urban livability improvement.

ACS Style

Anna Kovacs-Györi; Alina Ristea; Clemens Havas; Michael Mehaffy; Hartwig H. Hochmair; Bernd Resch; Levente Juhasz; Arthur Lehner; Laxmi Ramasubramanian; Thomas Blaschke. Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning. ISPRS International Journal of Geo-Information 2020, 9, 752 .

AMA Style

Anna Kovacs-Györi, Alina Ristea, Clemens Havas, Michael Mehaffy, Hartwig H. Hochmair, Bernd Resch, Levente Juhasz, Arthur Lehner, Laxmi Ramasubramanian, Thomas Blaschke. Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning. ISPRS International Journal of Geo-Information. 2020; 9 (12):752.

Chicago/Turabian Style

Anna Kovacs-Györi; Alina Ristea; Clemens Havas; Michael Mehaffy; Hartwig H. Hochmair; Bernd Resch; Levente Juhasz; Arthur Lehner; Laxmi Ramasubramanian; Thomas Blaschke. 2020. "Opportunities and Challenges of Geospatial Analysis for Promoting Urban Livability in the Era of Big Data and Machine Learning." ISPRS International Journal of Geo-Information 9, no. 12: 752.

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: 10 November 2020 in Remote Sensing
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Gully formation through water-induced soil erosion and related to devastating land degradation is often a quasi-normal threat to human life, as it is responsible for huge loss of surface soil. Therefore, gully erosion susceptibility (GES) mapping is necessary in order to reduce the adverse effect of land degradation and diminishes this type of harmful consequences. The principle goal of the present research study is to develop GES maps for the Garhbeta I Community Development (C.D.) Block; West Bengal, India, by using a machine learning algorithm (MLA) of boosted regression tree (BRT), bagging and the ensemble of BRT-bagging with K-fold cross validation (CV) resampling techniques. The combination of the aforementioned MLAs with resampling approaches is state-of-the-art soft computing, not often used in GES evaluation. In further progress of our research work, here we used a total of 20 gully erosion conditioning factors (GECFs) and a total of 199 gully head cut points for modelling GES. The variables’ importance, which is responsible for gully erosion, was determined based on the random forest (RF) algorithm among the several GECFs used in this study. The output result of the model’s performance was validated through a receiver operating characteristics-area under curve (ROC-AUC), sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) statistical analysis. The predicted result shows that the ensemble of BRT-bagging is the most well fitted for GES where AUC value in K-3 fold is 0.972, whereas the value of AUC in sensitivity, specificity, PPV and NPV is 0.94, 0.93, 0.96 and 0.93, respectively, in a training dataset, and followed by the bagging and BRT model. Thus, from the predictive performance of this research study it is concluded that the ensemble of BRT-Bagging can be applied as a new approach for further studies in spatial prediction of GES. The outcome of this work can be helpful to policy makers in implementing remedial measures to minimize damages caused by gully erosion.

ACS Style

Subodh Pal; Alireza Arabameri; Thomas Blaschke; Indrajit Chowdhuri; Asish Saha; Rabin Chakrabortty; Saro Lee; Shahab. Band. Ensemble of Machine-Learning Methods for Predicting Gully Erosion Susceptibility. Remote Sensing 2020, 12, 3675 .

AMA Style

Subodh Pal, Alireza Arabameri, Thomas Blaschke, Indrajit Chowdhuri, Asish Saha, Rabin Chakrabortty, Saro Lee, Shahab. Band. Ensemble of Machine-Learning Methods for Predicting Gully Erosion Susceptibility. Remote Sensing. 2020; 12 (22):3675.

Chicago/Turabian Style

Subodh Pal; Alireza Arabameri; Thomas Blaschke; Indrajit Chowdhuri; Asish Saha; Rabin Chakrabortty; Saro Lee; Shahab. Band. 2020. "Ensemble of Machine-Learning Methods for Predicting Gully Erosion Susceptibility." Remote Sensing 12, no. 22: 3675.

Journal article
Published: 04 November 2020 in Remote Sensing
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The Rarh Bengal region in West Bengal, particularly the eastern fringe area of the Chotanagpur plateau, is highly prone to water-induced gully erosion. In this study, we analyzed the spatial patterns of a potential gully erosion in the Gandheswari watershed. This area is highly affected by monsoon rainfall and ongoing land-use changes. This combination causes intensive gully erosion and land degradation. Therefore, we developed gully erosion susceptibility maps (GESMs) using the machine learning (ML) algorithms boosted regression tree (BRT), Bayesian additive regression tree (BART), support vector regression (SVR), and the ensemble of the SVR-Bee algorithm. The gully erosion inventory maps are based on a total of 178 gully head-cutting points, taken as the dependent factor, and gully erosion conditioning factors, which serve as the independent factors. We validated the ML model results using the area under the curve (AUC), accuracy (ACC), true skill statistic (TSS), and Kappa coefficient index. The AUC result of the BRT, BART, SVR, and SVR-Bee models are 0.895, 0.902, 0.927, and 0.960, respectively, which show very good GESM accuracies. The ensemble model provides more accurate prediction results than any single ML model used in this study.

ACS Style

Indrajit Chowdhuri; Subodh Pal; Alireza Arabameri; Asish Saha; Rabin Chakrabortty; Thomas Blaschke; Biswajeet Pradhan; Shahab. Band. Implementation of Artificial Intelligence Based Ensemble Models for Gully Erosion Susceptibility Assessment. Remote Sensing 2020, 12, 3620 .

AMA Style

Indrajit Chowdhuri, Subodh Pal, Alireza Arabameri, Asish Saha, Rabin Chakrabortty, Thomas Blaschke, Biswajeet Pradhan, Shahab. Band. Implementation of Artificial Intelligence Based Ensemble Models for Gully Erosion Susceptibility Assessment. Remote Sensing. 2020; 12 (21):3620.

Chicago/Turabian Style

Indrajit Chowdhuri; Subodh Pal; Alireza Arabameri; Asish Saha; Rabin Chakrabortty; Thomas Blaschke; Biswajeet Pradhan; Shahab. Band. 2020. "Implementation of Artificial Intelligence Based Ensemble Models for Gully Erosion Susceptibility Assessment." Remote Sensing 12, no. 21: 3620.

Journal article
Published: 18 October 2020 in Remote Sensing
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The uncertainty of flash flood makes them highly difficult to predict through conventional models. The physical hydrologic models of flash flood prediction of any large area is very difficult to compute as it requires lot of data and time. Therefore remote sensing data based models (from statistical to machine learning) have become highly popular due to open data access and lesser prediction times. There is a continuous effort to improve the prediction accuracy of these models through introducing new methods. This study is focused on flash flood modeling through novel hybrid machine learning models, which can improve the prediction accuracy. The hybrid machine learning ensemble approaches that combine the three meta-classifiers (Real AdaBoost, Random Subspace, and MultiBoosting) with J48 (a tree-based algorithm that can be used to evaluate the behavior of the attribute vector for any defined number of instances) were used in the Gorganroud River Basin of Iran to assess flood susceptibility (FS). A total of 426 flood positions as dependent variables and a total of 14 flood conditioning factors (FCFs) as independent variables were used to model the FS. Several threshold-dependent and independent statistical tests were applied to verify the performance and predictive capability of these machine learning models, such as the receiver operating characteristic (ROC) curve of the success rate curve (SRC) and prediction rate curve (PRC), efficiency (E), root-mean square-error (RMSE), and true skill statistics (TSS). The valuation of the FCFs was done using AdaBoost, frequency ratio (FR), and Boosted Regression Tree (BRT) models. In the flooding of the study area, altitude, land use/land cover (LU/LC), distance to stream, normalized differential vegetation index (NDVI), and rainfall played important roles. The Random Subspace J48 (RSJ48) ensemble method with an area under the curve (AUC) of 0.931 (SRC), 0.951 (PRC), E of 0.89, sensitivity of 0.87, and TSS of 0.78, has become the most effective ensemble in predicting the FS. The FR technique also showed good performance and reliability for all models. Map removal sensitivity analysis (MRSA) revealed that the FS maps have the highest sensitivity to elevation. Based on the findings of the validation methods, the FS maps prepared using the machine learning ensemble techniques have high robustness and can be used to advise flood management initiatives in flood-prone areas.

ACS Style

Alireza Arabameri; Sunil Saha; Kaustuv Mukherjee; Thomas Blaschke; Wei Chen; Phuong Ngo; Shahab Band. Modeling Spatial Flood using Novel Ensemble Artificial Intelligence Approaches in Northern Iran. Remote Sensing 2020, 12, 3423 .

AMA Style

Alireza Arabameri, Sunil Saha, Kaustuv Mukherjee, Thomas Blaschke, Wei Chen, Phuong Ngo, Shahab Band. Modeling Spatial Flood using Novel Ensemble Artificial Intelligence Approaches in Northern Iran. Remote Sensing. 2020; 12 (20):3423.

Chicago/Turabian Style

Alireza Arabameri; Sunil Saha; Kaustuv Mukherjee; Thomas Blaschke; Wei Chen; Phuong Ngo; Shahab Band. 2020. "Modeling Spatial Flood using Novel Ensemble Artificial Intelligence Approaches in Northern Iran." Remote Sensing 12, no. 20: 3423.

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: 30 August 2020 in Remote Sensing
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Tourism is a primary socio-economic factor on many coastal islands. Tourism contributes to the livelihoods of the residents, but also influences natural resources and energy consumption and can become a significant driver of land conversion and environmental change. Understanding the influence of tourist-related activities is vital for sustainable tourism development. We chose Hainan Island in South China as a research area to study the influence of tourist-driven activities on environmental variables (as Land Surface Temperatures (LST) and related ecosystem variables) during the period of 2000 to 2019. In Hainan, the local economy relies heavily on tourism, with an ever-growing influx of tourists each year. We categorised location-based points of interest (POIs) into two classes, non-tourism sites and tourism-related sites, and utilised satellite data from the cloud-based platform Google Earth Engine (GEE) to extract LST and Normalized Difference Vegetation Index (NDVI) data. We analysed the LST variations, NDVI changes and the land use/land cover (LULC) changes and compared the relative difference in LST and NDVI between the tourism-related sites and non-tourism-related sites. The main findings of this study were: (1) The median LST in the tourism-related sites was relatively higher (1.3) than the LST in the non-tourism-related sites for the 20 years. Moreover, every annual mean LST of tourism-related sites was higher than the LST values in non-tourism-related sites, with an average difference of 1.2 °C for the 20 years and a maximum difference of 1.7 °C. We found higher annual LST anomalies for tourist-related sites compared to non-tourism sites after 2010, which indicated the likely positive differences in LST above the average LST during 20 years for tourism-related sites when compared against the non-tourism related sites, thus highlighting the potential influence of tourism activities on LST. (2) The annual mean NDVI value for tourism-related sites was significantly lower than for non-tourism places every year, with an average NDVI difference of 0.26 between the two sites. (3) The land cover changed significantly: croplands and forests reduced by 3.5% and 2.8% respectively, while the areas covered by orchards and urban areas increased by 2% and 72.3% respectively. These results indicate the influence of the tourism-driven activities includes the relatively high LST, vegetation degradation and land-use conversion particular to urban cover type. The outcome of this work provides a method that combines cloud-based satellite-derived data with location-based POIs data for quantifying the long-term influence of tourism-related activities on sensitive coastal ecosystems. It contributes to designing evidence-driven management plans and policies for the sustainable tourism development in coastal areas.

ACS Style

Lixia Chu; Francis Oloo; Bin Chen; Miaomiao Xie; Thomas Blaschke. Assessing the Influence of Tourism-Driven Activities on Environmental Variables on Hainan Island, China. Remote Sensing 2020, 12, 2813 .

AMA Style

Lixia Chu, Francis Oloo, Bin Chen, Miaomiao Xie, Thomas Blaschke. Assessing the Influence of Tourism-Driven Activities on Environmental Variables on Hainan Island, China. Remote Sensing. 2020; 12 (17):2813.

Chicago/Turabian Style

Lixia Chu; Francis Oloo; Bin Chen; Miaomiao Xie; Thomas Blaschke. 2020. "Assessing the Influence of Tourism-Driven Activities on Environmental Variables on Hainan Island, China." Remote Sensing 12, no. 17: 2813.

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.

Journal article
Published: 25 August 2020 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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The 2030 Agenda for Sustainable Development is widely appreciated and increasingly known by a wider public. However, less obvious are the enormous coordination and harmonization efforts to reify these goals into 169 targets and 232 indicators. We exemplarily outline a tangible pathway to address SDG11 and one associated indicator 11.7.1 “Average share of the built-up area of cities that is open space for public use for all, by sex, age and persons with disabilities”. We highlight some specific problems for reporting on indicators related to urban green spaces (UGS) and make suggestions for this indicator by illustrating the potential of Earth Observation data and spatial accessibility analysis.

ACS Style

T. Blaschke; A. Kovács-Győri. EARTH OBSERVATION TO SUBSTANTIATE THE SUSTAINABLE DEVELOPMENT GOAL 11: PRACTICAL CONSIDERATIONS AND EXPERIENCES FROM AUSTRIA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2020, XLIII-B4-2, 769 -774.

AMA Style

T. Blaschke, A. Kovács-Győri. EARTH OBSERVATION TO SUBSTANTIATE THE SUSTAINABLE DEVELOPMENT GOAL 11: PRACTICAL CONSIDERATIONS AND EXPERIENCES FROM AUSTRIA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2020; XLIII-B4-2 ():769-774.

Chicago/Turabian Style

T. Blaschke; A. Kovács-Győri. 2020. "EARTH OBSERVATION TO SUBSTANTIATE THE SUSTAINABLE DEVELOPMENT GOAL 11: PRACTICAL CONSIDERATIONS AND EXPERIENCES FROM AUSTRIA." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2, no. : 769-774.

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.