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Mr. Thimmaiah Gudiyangada Nachappa
Department of Geoinformatics - Z_GIS, University of Salzburg

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

ACS Style

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

AMA Style

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

Chicago/Turabian Style

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

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

ACS Style

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

AMA Style

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

Chicago/Turabian Style

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

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

ACS Style

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

AMA Style

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

Chicago/Turabian Style

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

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

ACS Style

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

AMA Style

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

Chicago/Turabian Style

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

Article
Published: 28 March 2020 in Geomatics, Natural Hazards and Risk
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ACS Style

Thimmaiah Gudiyangada Nachappa; Stefan Kienberger; Sansar Raj Meena; Daniel Hölbling; Thomas Blaschke. Comparison and validation of per-pixel and object-based approaches for landslide susceptibility mapping. Geomatics, Natural Hazards and Risk 2020, 11, 572 -600.

AMA Style

Thimmaiah Gudiyangada Nachappa, Stefan Kienberger, Sansar Raj Meena, Daniel Hölbling, Thomas Blaschke. Comparison and validation of per-pixel and object-based approaches for landslide susceptibility mapping. Geomatics, Natural Hazards and Risk. 2020; 11 (1):572-600.

Chicago/Turabian Style

Thimmaiah Gudiyangada Nachappa; Stefan Kienberger; Sansar Raj Meena; Daniel Hölbling; Thomas Blaschke. 2020. "Comparison and validation of per-pixel and object-based approaches for landslide susceptibility mapping." Geomatics, Natural Hazards and Risk 11, no. 1: 572-600.

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

Thimmaiah Gudiyangada Nachappa; Sansar Raj Meena. A novel per pixel and object-based ensemble approach for flood susceptibility mapping. Geomatics, Natural Hazards and Risk 2020, 11, 2147 -2175.

AMA Style

Thimmaiah Gudiyangada Nachappa, Sansar Raj Meena. A novel per pixel and object-based ensemble approach for flood susceptibility mapping. Geomatics, Natural Hazards and Risk. 2020; 11 (1):2147-2175.

Chicago/Turabian Style

Thimmaiah Gudiyangada Nachappa; Sansar Raj Meena. 2020. "A novel per pixel and object-based ensemble approach for flood susceptibility mapping." Geomatics, Natural Hazards and Risk 11, no. 1: 2147-2175.

Journal article
Published: 10 December 2019 in Applied Sciences
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Landslide susceptibility mapping (LSM) can serve as a basis for analyzing and assessing the degree of landslide susceptibility in a region. This study uses the object-based geons aggregation model to map landslide susceptibility for all of Austria and evaluates whether an additional implementation of the Dempster–Shafer theory (DST) could improve the results. For the whole of Austria, we used nine conditioning factors: elevation, slope, aspect, land cover, rainfall, distance to drainage, distance to faults, distance to roads, and lithology, and assessed the performance and accuracy of the model using the area under the curve (AUC) for the receiver operating characteristics (ROC). We used three scale parameters for the geons model to evaluate the impact of the scale parameter on the performance of LSM. The results were similar for the three scale parameters. Applying the Dempster–Shafer theory could significantly improve the results of the object-based geons model. The accuracy of the DST-derived LSM for Austria improved and the respective AUC value increased from 0.84 to 0.93. The resulting LSMs from the geons model provide meaningful units independent of administrative boundaries, which can be beneficial to planners and policymakers.

ACS Style

Thimmaiah Gudiyangada Nachappa; Sepideh Tavakkoli Piralilou; Omid Ghorbanzadeh; Hejar Shahabi; Thomas Blaschke. Landslide Susceptibility Mapping for Austria Using Geons and Optimization with the Dempster-Shafer Theory. Applied Sciences 2019, 9, 5393 .

AMA Style

Thimmaiah Gudiyangada Nachappa, Sepideh Tavakkoli Piralilou, Omid Ghorbanzadeh, Hejar Shahabi, Thomas Blaschke. Landslide Susceptibility Mapping for Austria Using Geons and Optimization with the Dempster-Shafer Theory. Applied Sciences. 2019; 9 (24):5393.

Chicago/Turabian Style

Thimmaiah Gudiyangada Nachappa; Sepideh Tavakkoli Piralilou; Omid Ghorbanzadeh; Hejar Shahabi; Thomas Blaschke. 2019. "Landslide Susceptibility Mapping for Austria Using Geons and Optimization with the Dempster-Shafer Theory." Applied Sciences 9, no. 24: 5393.

Journal article
Published: 17 August 2019 in Geosciences
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Landslides are one of the most damaging geological hazards in mountainous regions such as the Himalayas. The Himalayan region is, tectonically, the most active region in the world that is highly vulnerable to landslides and associated hazards. Landslide susceptibility mapping (LSM) is a useful tool for understanding the probability of the spatial distribution of future landslide regions. In this research, the landslide inventory datasets were collected during the field study of the Kullu valley in July 2018, and 149 landslide locations were collected as global positioning system (GPS) points. The present study evaluates the LSM using three different spatial resolution of the digital elevation model (DEM) derived from three different sources. The data-driven traditional frequency ratio (FR) model was used for this study. The FR model was used for this research to assess the impact of the different spatial resolution of DEMs on the LSM. DEM data was derived from Advanced Land Observing Satellite-1 (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) ALOS-PALSAR for 12.5 m, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global for 30 m, and the Shuttle Radar Topography Mission (SRTM) for 90 m. As an input, we used eight landslide conditioning factors based on the study area and topographic features of the Kullu valley in the Himalayas. The ASTER-Global 30m DEM showed higher accuracy of 0.910 compared to 0.839 for 12.5 m and 0.824 for 90 m DEM resolution. This study shows that that 30 m resolution is better suited for LSM for the Kullu valley region in the Himalayas. The LSM can be used for mitigation and future planning for spatial planners and developmental authorities in the region.

ACS Style

Sansar Raj Meena; Thimmaiah Gudiyangada Nachappa. Impact of Spatial Resolution of Digital Elevation Model on Landslide Susceptibility Mapping: A case Study in Kullu Valley, Himalayas. Geosciences 2019, 9, 360 .

AMA Style

Sansar Raj Meena, Thimmaiah Gudiyangada Nachappa. Impact of Spatial Resolution of Digital Elevation Model on Landslide Susceptibility Mapping: A case Study in Kullu Valley, Himalayas. Geosciences. 2019; 9 (8):360.

Chicago/Turabian Style

Sansar Raj Meena; Thimmaiah Gudiyangada Nachappa. 2019. "Impact of Spatial Resolution of Digital Elevation Model on Landslide Susceptibility Mapping: A case Study in Kullu Valley, Himalayas." Geosciences 9, no. 8: 360.