A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping

Ghorbanzadeh, Omid; Blaschke, Thomas; Aryal, Jagannath; Gholaminia, Khalil

doi:10.1080/14498596.2018.1505564

Cited by 90 publications

(51 citation statements)

References 65 publications

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“…Artificial neural network impersonates the performance of the human brain through a set of nodes that are interconnected [34]. The ANN imitates the human brain in two main respects: firstly, it obtains knowledge through a learning procedure; and secondly, the knowledge gained is stored through synaptic weights [35].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping

et al. 2020

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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.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping

et al. 2020

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“…The area under the curve (AUC) is the measure that indicates the accuracy of the landslide susceptibility maps. The resulting AUCs indicate the probability that more pixels were correctly labelled than incorrectly labelled [27]. Therefore, greater AUC values indicate a higher accuracy of the resulting susceptibility map.…”

Section: Receiver Operating Characteristics (Roc)mentioning

confidence: 99%

“…Currently, data-based models show fairly good results in the natural hazard susceptibility assessments [27][28][29]. We used a data-based frequency ratio (FR) model for LSM in the Gorkha region.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Statistics-Based Landslide Susceptibility Models: A Case Study of the Region Affected by the Gorkha Earthquake in Nepal

Meena

Ghorbanzadeh

Blaschke

2019

IJGI

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As a result of the Gorkha earthquake in 2015, about 9000 people lost their lives and many more were injured. Most of these losses were caused by earthquake-induced landslides. Sustainable planning and decision-making are required to reduce the losses caused by earthquakes and related hazards. The use of remote sensing and geographic information systems (GIS) for landslide susceptibility mapping can help planning authorities to prepare for and mitigate the consequences of future hazards. In this study, we developed landslide susceptibility maps using GIS-based statistical models at the regional level in central Nepal. Our study area included the districts affected by landslides after the Gorkha earthquake and its aftershocks. We used the 23,439 landslide locations obtained from high-resolution satellite imagery to evaluate the differences in landslide susceptibility using analytical hierarchy process (AHP), frequency ratio (FR) and hybrid spatial multi-criteria evaluation (SMCE) models. The nine landslide conditioning factors of lithology, land cover, precipitation, slope, aspect, elevation, distance to roads, distance to drainage and distance to faults were used as the input data for the applied landslide susceptibility mapping (LSM) models. The spatial correlation of landslides and these factors were identified using GIS-based statistical models. We divided the inventory into data used for training the statistical models (70%) and data used for validation (30%). Receiver operating characteristics (ROC) and the relative landslide density index (R-index) were used to validate the results. The area under the curve (AUC) values obtained from the ROC approach for AHP, FR and hybrid SMCE were 0.902, 0.905 and 0.91, respectively. The index of relative landslide density, R-index, values in sample datasets of AHP, FR and hybrid SMCE maps were 53%, 58% and 59% for the very high hazard classes. The final susceptibility results will be beneficial for regional planning and sustainable hazard mitigation.

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“…The y-axis indicates the TPR while the x-axis indicates the FPR. The TPRs are the pixels that are correctly referred to as landslide areas, and FPR are the pixels that are incorrectly referred to as landslide areas [58]. The AUC is the measure which indicates the accuracy of the LSM.…”

Section: Receiver Operating Characteristics (Roc)mentioning

confidence: 99%

Impact of Spatial Resolution of Digital Elevation Model on Landslide Susceptibility Mapping: A Case Study in Kullu Valley, Himalayas

Meena

Nachappa

2019

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.

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A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping

Cited by 90 publications

References 65 publications

Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping

Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping

A Comparative Study of Statistics-Based Landslide Susceptibility Models: A Case Study of the Region Affected by the Gorkha Earthquake in Nepal

Impact of Spatial Resolution of Digital Elevation Model on Landslide Susceptibility Mapping: A Case Study in Kullu Valley, Himalayas

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