Final Susceptibility Map Research Articles

Quantitative susceptibility mapping as an early neuroimaging biomarker for amyotrophic lateral sclerosis: A review

AbstractQuantitative susceptibility mapping (QSM) is an advanced post‐processing technique in magnetic resonance imaging that offers precise measurements of tissue magnetic susceptibility with impressive spatial resolution and sensitivity. This review examines the potential of QSM as a biomarker for early detection and monitoring of amyotrophic lateral sclerosis (ALS). Since 2015, studies have consistently reported increased QSM values in the motor regions of individuals with ALS, indicating significant iron deposition. Iron accumulation is associated with dysfunction of the upper motor neurons and faster disease progression. Notably, increased QSM values were also observed in the critical subcortical areas responsible for motor function and cognitive control. However, standardizing optimized protocols, including background field removal algorithms, phase unwrapping approaches, and methods for final susceptibility map reconstruction, has the potential to enhance the consistency and reliability of QSM as an ALS biomarker. Overall, the current body of evidence strongly supports QSM in detecting iron dysregulation associated with neurodegeneration in both motor and extra‐motor regions in ALS. Furthermore, QSM's remarkable sensitivity to early pathological iron changes and its high specificity in distinguishing ALS positions make it a promising diagnostic and progression‐tracking biomarker.

Assessing classification system for landslide susceptibility using frequency ratio, analytical hierarchical process and geospatial technology mapping in Aizawl district, NE India

Landslides are a severe geological hazard in northeastern India, particularly in Mizoram’s Aizawl district. To assess this hazard, we conducted a landslide susceptibility mapping study using the frequency ratio (FR) and analytical hierarchy process (AHP) methods. Twelve factors, including slope, elevation, aspect, curvature, topographic wetness index, topographic ruggedness index, rainfall erosivity factor, geology, land use land cover, normalized difference vegetation index (NDVI), and distance to road, were considered. The final susceptibility maps were created by combining these factors, each weighted according to its importance. Since no standard classification guidelines existed, we compared different systems like GIS-based geometric interval (GIC), natural breaks (NBC), and quantile interval (QIC) with mean-standard deviation (MSC). The area was divided into five susceptibility zones: very high, high, moderate, low, and very low. We evaluated the accuracy of these systems using success rate curve (SRC) and quality sum ratio (Qs) techniques. SRC results showed similar performance for GIC, NBC, QIC, and MSC classifications. The AHP method also yielded comparable results. After analyzing the AUC under SRC and Qs, we selected the MSC classification system as the optimal approach for landslide susceptibility mapping. Notably, the distance to roads factor had the highest predictive value, highlighting its importance in identifying slope instability for effective management strategies. As landslides pose significant risks in northeastern India, and our study using FR and AHP methods, along with MSC classification, provides valuable insights for mitigating this geological hazard.

A Geo-Statistical Analysis of the Impact of Ecological and Environmental Risks on Epidemiology in the South-west, Nigeria

Introduction: The probability of contamination is frequently elevated in scenarios where a well and pit latrine coexist, or in situations where heavy rain causes the overflow of open excreta dumps, which in turn flush into wells and surface water. Many possible negative health effects might arise from exposure to various ecological and biological agents in the environment. Therefore, there is a need to examine the risk of disease transmission in Ife North Local Government Area (LGA) of Osun state, using epidemiological, environmental, and ecological factors.
 Materials and Methods: Geostatistical analysis was used to examine the epidemiological risk, based on various environmental, biological, and ecological variables. The technique employed demonstrated the complexity and multiple parameters that raise the probability of an epidemic. The Shapiro-Wilk test was used to determine whether or not the data were normally distributed. Fuzzy logic, correlation, and spline surface interpolation analysis were conducted using ArcGIS 10.3 and ENVI 5.0 software. Three levels of epidemic risk were used to construct the disease surveillance and projection maps.
 Results: According to the final susceptibility map, 8.08 km2 of 460.12 km2 of the research area were considered to be at very low risk for an epidemic, followed by 364.98km2 of low risk and 87.06km2 of moderate risk areas, with percentages of 1.75%, 79.32%, and 18.92%, respectively.
 Conclusion: A very substantial correlation was observed between biological and ecological components and water-borne diseases. It is, therefore, advised that all water sources be treated before consumption, and community involvement be encouraged in environmental sanitation programs.

Integrating landslide magnitude in the susceptibility assessment of the City of Doboj, using machine learning and heuristic approach

ABSTRACT In this work, landslide assessment of the Doboj City area was modeled by combining machine learning and heuristic tools. The machine learning part was used to map the Morphometric factor. i.e. probability of landslides based on relation between the magnitude of events and morphometric parameters: elevation, distance to streams, slope, profile curvature, and aspect. The Random Forest and Support Vector Machines algorithms were implemented in the learning protocol, which included several strategies: balancing of the training/testing set size, algorithm optimization via cross-validation, and cross-scaling. The best performing Morphometric factor ap was created by learning on 50 m and testing on 25 m dataset. The heuristic part was used for modeling of Lithological factor and Land Cover factor maps, by expert-driven scoring of their units, within 0-1 range of values. The final Susceptibility map was obtained by multiplying all three factor maps resulting in a high-performing model with AUC=0.97 and acc=92%.

Modelling and Mapping of Soil Erosion Susceptibility of Murree, Sub-Himalayas Using GIS and RS-Based Models

Soil erosion is one of Pakistan’s most serious environmental threats. This study used geospatial modelling to identify the distinct zones susceptible to soil erosion in Murree, Pakistan. Using a machine learning technique in the Google Earth engine (GEE) and Google Earth, we identified 1250 soil erosion events. The inventory (dependent variable) was separated into two datasets, one for training (70%) and one for testing (30%). Elevation, slope, aspect, curvature, stream, precipitation, LULC, lithology, soil, NDVI, and distance to road were prepared in ArcGIS and considered as independent variables in the current research. GIS and RS-based models such as WOE, FR, and IV were used to assess the relationship between both variables and produce soil erosion susceptibility maps. Finally, the Area Under Curve (AUC) approach was used to confirm the research results. According to the validation data, the SRC for WOE, FR, and IV were 88%, 91%, and 87%, respectively. The present study’s validation results show that the PRC for WOE, FR, and IV are 92%, 94%, and 90%, respectively. Based on the AUC validation approach, we determined that the FR model had the highest accuracy when compared to the other two techniques, the WOE and IV models. The current analysis and final susceptibility maps of soil erosion could be useful for decision-makers in the future to prevent soil erosion and its negative repercussions.

An Integrated Approach of Machine Learning, Remote Sensing, and GIS Data for the Landslide Susceptibility Mapping

Landslides triggered in mountainous areas can have catastrophic consequences, threaten human life, and cause billions of dollars in economic losses. Hence, it is imperative to map the areas susceptible to landslides to minimize their risk. Around Abbottabad, a large city in northern Pakistan, a large number of landslides can be found. This study aimed to map the landslide susceptibility over these regions in Pakistan by using three Machine Learning (ML) techniques, specifically Linear Regression (LiR), Logistic Regression (LoR), and Support Vector Machine (SVM). Several influencing factors were used to identify the potential landslide areas, including elevation, slope degree, slope aspect, general curvature, plan curvature, profile curvature, landcover classification system, Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), soil, lithology, fault density, topographic roughness index, and road density. The weights of these factors were calculated using ML techniques. The weightage overlay tool is adopted to map the final output. According to three ML models, lithology, NDWI, slope, and LCCS significantly impact landslide occurrence. The area under the ROC curve (AUC) is applied to validate the performance of models, and the results show the AUC value of LiR (88%) is better than SVM (86%) and LoR (85%) models. ML models and final susceptibility map gives good accuracy, which can be reliable for the results. The study’s outcome provides baselines for policymakers to propose adequate protection and mitigation measures against the landslides in the region, and any other researcher can adopt this methodology to map the landslide susceptibility in another area having similar characteristics.

Impact and a Novel Representation of Spatial Data Uncertainty in Debris Flow Susceptibility Analysis

In a study of debris flow susceptibility on the European continent, an analysis of the impact between known location and a location accuracy offset for 99 debris flows demonstrates the impact of uncertainty in defining appropriate predisposing factors and consequent analysis for areas of susceptibility. The dominant predisposing environmental factors, as determined through Maximum Entropy modeling, are presented and analyzed with respect to the values found at debris flow event points versus a buffered distance of locational uncertainty around each point. Maximum Entropy susceptibility models are developed utilizing the original debris flow inventory of points, randomly generated points, and two models utilizing a subset of points with an uncertainty of 5 km, 1 km, and a model utilizing only points with a known location of “exact”. The AUCs are 0.891, 0.893, 0.896, 0.921, and 0.93, respectively. The “exact” model, with the highest AUC, is ignored in final analyses due to the small number of points and localized distribution, and hence susceptibility results are likely non-representational of the continent. Each model is analyzed with respect to the AUC, highest contributing factors, factor classes, susceptibility impact, and comparisons of the susceptibility distributions and susceptibility value differences. Based on model comparisons, geographic extent, and the context of this study, the models utilizing points with a location uncertainty of less than or equal to 5 km best represent debris flow susceptibility for the continent of Europe. A novel representation of the uncertainty is expressed and included in a final susceptibility map, as an overlay of standard deviation and mean of susceptibility values for the two best models, providing additional insight for subsequent action.

A Hybrid Physical and Machine Learning Model for Assessing Landslide Spatial Probability Caused by Raising of Ground Water Table and Earthquake in Atsuma, Japan — Case Study

Landslides are catastrophic natural events primed and/or triggered by extreme rainfalls and strong earthquakes. Simultaneous occurrence of rainfall and seismic activity increases the likelihood of landslides. However, the researchers focused on this aspect are not much. In the present research, a hybrid model was developed to predict the landslide occurrences probability in Atsuma, Japan triggered by rainfalls and earthquakes. The proposed model is a combination of a physical and machine learning model for improving the accuracy of the landslide susceptibility mapping. The proposed model consisted of a physical module, a machine learning module and a matrix approach module. The physical module assessed the effects of rainfall and peak ground acceleration (PGA) on landslide occurrence probability based on a pseudo-static model. The machine learning module applied Multilayer Perceptron Neural Networks to assess landslide susceptibility, using 611 landslide events caused by strong earthquakes and extreme typhoons. The landslide susceptibility maps obtained from these two modules were then combined into final susceptibility map through a matrix approach. The final susceptibility map included five susceptible levels: very low, low, moderate, high, and very high. To evaluate the proposed model performance, the resulting models were assessed using the areas under the receiver operating characteristic curves. The areas under the success rate curves from the physical module, machine learning module and matrix-based approach showed 79.2%, 82.7% and 83.9% accuracy, respectively. Furthermore, the predicted rate curves showed that the areas under the curve for physical module, machine learning module and matrix-based approach were 78.4%, 82.3% and 83.4%, respectively. These results suggest that the proposed hybrid model improves the prediction capability compared to physically-based method or machine learning model and can be readily used to assess spatial probability of landslide.

Landslide variation with morphometric factors using the GIS techniques: The case of Shaqlawa Forest

This study deals with the application of geographical information system (GIS) datasets and methods to assess the landslide susceptibility in Wadi Hujran. The area has a rocky terrain and belongs to the Shaqlawa district of the Kurdistan Region of Iraq. The region is placed towards the Northeast side of Erbil city. The region covers an area of 18.56 Km2 (1856.1 ha) and consists of rough broken and stones. The watershed area is surrounded by North latitudes 36° 21' 53.514" to 36° 17' 49.7796" and East longitudes 44° 17' 5.658" to 44° 20' 9.06". Three factors, namely the morphometric, geological, and environmental, were used to prepare the landslide susceptibility index. The study made use of AHP method and prepared a landslide susceptibility map. Data related to geology, topography, hydrology, rainfall, and land use were used to prepare the map. Physical and statistical methods were used to validate the map. A heuristic approach was incorporated to produce the final susceptibility map. ArcGIS software was used to generate the landslide zones. A total of five landslide zones were generated, which varied from very low landslide zones (80.5) to very high landslide zone (84.5). The zones also included low landslide zone (1262.2), moderate landslide zone (1505.9), and high landslide zone (566.8), and the ratio of consistency in the present study was 0.06 AHP less than 1, and all the five zones in the study were compiled landslide zonation estimated.

Flood Susceptibility Mapping of Makera District and Environs in Kaduna South Local Government Area of Kaduna State-Nigeria

The increased flood incidences experienced all over the world due to climate change dynamics call for a concerted effort towards forestalling future hazards. This study thus, identified the areas that are susceptibility to floods in parts of the Makera district of the Kaduna South Local Government Area in Nigeria using geospatial techniques. Geographic Information System (GIS) was used to produce thematic layers of the factors contributing to flooding (elevation, slope, drainage density, rainfall, land use/land cover); and a multi-criteria evaluation particularly the “Analytical Hierarchical process” (AHP) was applied to determine the locations at risk. The various thematic layers were integrated into the weighted overlay tool in the ArcGIS 10.3 environment to generate the final susceptibility map. The overlay tool was also used to determine the elements at risk of flood in the study area. The results show that the areas that were highly susceptible to flood constituted about 39% of the study area, while moderate and low vulnerable areas constituted about 26% and 35%, respectively. The result of the multi-criteria analysis revealed that land use/land cover (0.601) was the factor that contributed the most to flooding in the study area based on the criteria weights followed by rainfall (0.470), drainage density (0.326), elevation (0.144), and slope (0.099), respectively. The study recommends that authorities concerned should ensure strict adherence to land use planning act, such that floodplains are avoided during development of any type.

Landslide variation with morphometric factors using the GIS techniques: The case of Shaqlawa Forest

This study deals with the application of geographical information system (GIS) datasets and methods to assess the landslide susceptibility in Wadi Hujran. The area has a rocky terrain and belongs to the Shaqlawa district of the Kurdistan Region of Iraq. The region is placed towards the Northeast side of Erbil city. The region covers an area of 18.56 Km2 (1856.1 ha) and consists of rough broken and stones. The watershed area is surrounded by North latitudes 36° 21' 53.514" to 36° 17' 49.7796" and East longitudes 44° 17' 5.658" to 44° 20' 9.06". Three factors, namely the morphometric, geological, and environmental, were used to prepare the landslide susceptibility index. The study made use of AHP method and prepared a landslide susceptibility map. Data related to geology, topography, hydrology, rainfall, and land use were used to prepare the map. Physical and statistical methods were used to validate the map. A heuristic approach was incorporated to produce the final susceptibility map. ArcGIS software was used to generate the landslide zones. A total of five landslide zones were generated, which varied from very low landslide zones (80.5) to very high landslide zone (84.5). The zones also included low landslide zone (1262.2), moderate landslide zone (1505.9), and high landslide zone (566.8), and the ratio of consistency in the present study was 0.06 AHP less than 1, and all the five zones in the study were compiled landslide zonation estimated.

Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models

The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE), Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale.

Landslide detection and susceptibility mapping using geological and remote sensing data: A case study of Azad Kashmir, NW Sub-Himalayas

Azad Kashmir has undergone sever landslide episodes in the past. The main reasons behind the occurrence of landslides in this area are heavy rainfall, weak geological strata, seismic zones and active faults. Several episodes of roads blockage due to landslide is one of the major problem in this area, therefore, this study is carried out for landslide susceptibility analysis along the roads of Kotli to Trar Khel, Azad Kashmir, NW Sub-Himalayas. Geographic information system (GIS) is used to interpret remote sensing, geological strata and topographical data. Total ten anthropogenic and physical factors (aspects, fault, lithology, drainage density, curvature, distance to roads, landuse landcover, slope, seismic and elevation) were examined by Multi-criteria Decision Analysis (MCDA) technique. The thematic layers of all factors were prepared for the preparation of the final susceptibility map by using a weighted overlay method (WOM). The susceptibility map classified the study area into four susceptible classes of low, moderate, high and very high. The spatial analysis and result outcomes depicted that active faults, slope gradient and landuse landcover may facilitate landslide phenomena. The results were verified by landslide density analyses (LDA), receiver operator characteristics (ROC) and area under curve (AUC) with predictive accuracy of 77 % that is rated as satisfactory by many researchers.

Ultrashort echo time quantitative susceptibility mapping (UTE-QSM) for detection of hemosiderin deposition in hemophilic arthropathy: A feasibility study.

The purpose of this study was to investigate the feasibility of ultrashort echo time quantitative susceptibility mapping (UTE-QSM) for assessment of hemosiderin deposition in the joints of hemophilic patients. The UTE-QSM technique was based on three sets of dual-echo 3D UTE Cones data acquired with TEs of 0.032/2.8, 0.2/3.6, and 0.4/4.4 ms. The images were processed with iterative decomposition of water and fat with echo asymmetry and least-squares estimation to estimate the B0 field map in the presence of fat. Then, the projection onto dipole field (PDF) algorithm was applied to acquire a local field map generated by tissues, followed by application of the morphology-enabled dipole inversion (MEDI) algorithm to estimate a final susceptibility map. Three healthy volunteers and three hemophilic patients were recruited to evaluate the UTE-QSM technique's ability to assess hemosiderin in the knee or ankle joint at 3T. One patient subsequently underwent total knee arthroplasty after the MR scan. The synovial tissues harvested from the knee joint during surgery were processed for histological analysis to confirm iron deposition. UTE-QSM successfully yielded tissue susceptibility maps of joints in both volunteers and patients. Multiple regions with high susceptibility over 1 ppm were detected in the affected joints of hemophilic patients, while no localized regions with high susceptibility were detected in asymptomatic healthy volunteers. Histology confirmed the presence of iron in regions where high susceptibility was detected by UTE-QSM. The UTE-QSM technique can detect hemosiderin deposition in the joint, and provides a potential sensitive biomarker for the diagnosis and prognosis of hemophilic arthropathy.

Spatio-Statistical Analysis of Flood Susceptibility Assessment Using Bivariate Model in the Floodplain of River Swat, District Charsadda, Pakistan

Flood is one of the most predominant disasters around the globe and frequently occurring phenomena in the northern part of Pakistan. In this study, the effects of various divisions of flood inventory and combinations of conditioning factors were assessed for the preparation of final susceptibility map. The flood inventory map was prepared for Charsadda by visual interpretation of Landsat-7 image alongside the field survey and a total of 161 flood locations were mapped. The flood inventory was subsequently divided into training and validation datasets, 129 (80%) and 112 (70%) locations for training the model and 32 (20%) and 49 (30%) for validation of the model. In this study, nine conditioning factors were used (Elevation, Slope, Aspect, Curvature, Plan curvature, Profile curvature, Proximity to river, roads, and Land use/land cover) for the development of flood susceptibility map. All the conditioning factors were correlated with flood inventory map using the information value method. The final susceptibility maps were validated using prediction rate and success rate curve. The results from validation showed that the areas under curve in the prediction rate curve for the models are: Model A (99.47%), Model B (95.04%), and Model C (94.06%), respectively. The Area under curve (AUC) in the success rate curve obtained for the three models are: Model A (95.03%), Model B (86.91%), and Model C (89.67%), respectively. Eventually, the susceptibility maps were classified into five susceptibility zones. The success rate and prediction rate curve indicated that model A has more accuracy in comparison to model B and model C; though, the results obtained from prediction and success rate curve indicated that all the models are reliable and has no significant difference between the susceptibility maps. Consequently, results obtained from this study are useful for researchers, disaster managers, and decision-makers to manage the flood-prone areas in the study area to mitigate the flood damages.

A comparative study on the integrative ability of the analytical hierarchy process, weights of evidence and logistic regression methods with the Flow-R model for landslide susceptibility assessment

The main purpose of the present study is to compare the results of the integrated models, the weights of evidence (WoE), analytical hierarchical process (AHP) and logistic regression (LR) models combine with a Flow-R model for landslide susceptibility assessment in Ha Giang city, Vietnam and the surrounding areas. First, three landslide susceptibility index (LSI) maps were calculated using thirteen landslide conditioning factor maps. Then, a runout map was generated by Flow-R model using the digital elevation model and the slope map of the study area. Secondly, the success rate curve, prediction rate curve and area under the curves (AUC) were calculated to assess prediction capability. The final landslide susceptibility maps were produced by the integration of the LSI maps with the runout map that was extracted from FlowR model. The validation results showed that the AUC of LSI maps in the integrated models, AHP-FlowR, WoE-FlowR and LR-FlowR, were 82.89%, 88.15% and 86.53% with prediction accuracies of 80.41%, 84.94% and 85.71%, respectively. The combined models of WoE-FlowR (88.15%) and LR-FlowR (86.53%) have shown the highest prediction capability. In general, the integrated models provide reasonable results that may be aided in prevention planning and land use planning purposes.

GIS-based evaluation of diagnostic areas in landslide susceptibility analysis of Bahluieț River Basin (Moldavian Plateau, NE Romania). Are Neolithic sites in danger?

The aim of this study is to compare the predictive strenghtness of different diagnostic areas in determining landslide susceptibility using frequency ratio (FR), statistical index (SI), and analytic hierarchy process (AHP) models in a catchment from the northeastern part of Romania. Scarps (point), landslide areas (polygon), and middle of the landslide (point) have been tested and checked in regards to their performance. The three statistical models have been employed to assess the landslide susceptibility using eleven conditioning factors (slope angle, elevation, curvature, lithology, precipitations, land use, topographic wetness index (TWI), landforms, aspect, plan curvature and distance to river). The three models were validated using the receiver operating characteristic (ROC) curves and the seed cell area index (SCAI) methods. The predictive capability of each model was established from the area under the curve (AUC), for FR, SI and AHP; the values are 0.75, 0.81 and 0.78 (using polygon as diagnostic area), respectively. Among the three methods used, SI had a better predictability. When it comes to the predictability values regarding the diagnostic areas, the landslide area (polygon) proves to have the highest values. This results from the entire surface of the landslide being taken into account when validating the data. Approximately 70% of the Neolithic sites are located in areas with high and very high susceptibility to landslides, meaning that they are in danger of being destroyed in the future. The final susceptibility maps are useful in hazard mitigation, risk reduction, a sustainable land use planning, evaluation of cultural heritage integrity, and to highlight the most endangered sites that are likely to be destroyed in the future.

Application of analytic hierarchy process, frequency ratio, and statistical index to landslide susceptibility: an approach to endangered cultural heritage

This paper presents the applicability of the analytic hierarchy process, frequency ratio, and statistical index in landslide susceptibility mapping for an area from north-eastern Romania. The dependent factor (in this case the landslides) was determined by combining seven conditioning factors: elevation, slope angle, curvature, normalised difference vegetation index, roughness, distance to rivers, and landforms. A landslide inventory was prepared using topographic plans and orthophotos and by carrying out field surveys with the total station (Leica TCR1201) and geodetic GPS (Leica System 1200) in RTK mode. The final susceptibility maps were made with the help of GIS and validated by carrying out the receiver operating characteristics (ROC) curves. ROC plots for training data have shown that the susceptibility map generated using the analytic hierarchy process with AUC value of 79.46 has a better prediction accuracy compared with frequency ratio method (AUC = 75.24), and statistical index method, which had an AUC value of 61.46. The prediction rate of the three models is over 50% (79.64% for the analytic hierarchy process model, 75.65% for the frequency ratio model, and 66.36% for the statistical index model). More than a half of the cultural heritage sites are located in areas with a very high and high vulnerability to landslides. Taking into consideration that multi-stratified settlements are located in areas with low and medium landslide susceptibility, this fact can indicate a possible awareness of prehistoric people from natural hazards. The final susceptibility maps can be used for land use planning, risk reduction, hazard mitigation, to evaluate the present state of cultural heritage sites in danger and to predict which of the sites will be in more danger in the future.

A bivariate statistical technique with knowledge-based analytical hierarchy process for landslide susceptibility assessment in naryn river basin, Kyrgyzstan

The model used statistical analysis for eight causal factors (slope, curvature, elevation, TWI, NDVI, NDSI, land cover, precipitation) of landslide occurrence. All factors were weighted to apply two-dimensional statistical method of a knowledge-based analytic hierarchical process with data extracted from the spatial database and then converted into a map. Final susceptibility maps showed a close agreement between the two models. The models predicted 72.1% and 69% of the empirical data used for the analysis respectively. These maps can be used to demonstrate the effectiveness of two-dimensional statistical model through the relationship between each factor with a resultant landslide susceptibility. The proposed model can be used to reproduce the relationship between each conditional factor without having to resort to multivariate statistics. The models are a powerful tool for assessing natural hazards, and to produce landslide probability maps for a better definition of risk zones.

EVALUATING THE VARIATIONS IN THE FLOOD SUSCEPTIBILITY MAPS ACCURACIES DUE TO THE ALTERATIONS IN THE TYPE AND EXTENT OF THE FLOOD INVENTORY

Abstract. This paper explores the influence of the extent and density of the inventory data on the final outcomes. This study aimed to examine the impact of different formats and extents of the flood inventory data on the final susceptibility map. An extreme 2011 Brisbane flood event was used as the case study. LR model was applied using polygon and point formats of the inventory data. Random points of 1000, 700, 500, 300, 100 and 50 were selected and susceptibility mapping was undertaken using each group of random points. To perform the modelling Logistic Regression (LR) method was selected as it is a very well-known algorithm in natural hazard modelling due to its easily understandable, rapid processing time and accurate measurement approach. The resultant maps were assessed visually and statistically using Area under Curve (AUC) method. The prediction rates measured for susceptibility maps produced by polygon, 1000, 700, 500, 300, 100 and 50 random points were 63 %, 76 %, 88 %, 80 %, 74 %, 71 % and 65 % respectively. Evidently, using the polygon format of the inventory data didn’t lead to the reasonable outcomes. In the case of random points, raising the number of points consequently increased the prediction rates, except for 1000 points. Hence, the minimum and maximum thresholds for the extent of the inventory must be set prior to the analysis. It is concluded that the extent and format of the inventory data are also two of the influential components in the precision of the modelling.