Natural Hazard Susceptibility Research Articles

GIS-Based Integrated Multi-Hazard Vulnerability Assessment in Makedonska Kamenica Municipality, North Macedonia

This study presents a comprehensive analysis of natural hazard susceptibility in the Makedonska Kamenica municipality of North Macedonia, encompassing erosion assessment, landslides, flash floods, and forest fire vulnerability. Employing advanced GIS and remote sensing (RS) methodologies, hazard models were meticulously developed and integrated to discern areas facing concurrent vulnerabilities. Findings unveil substantial vulnerabilities prevalent across the area, notably along steep terrain gradients, river valleys, and deforested landscapes. Erosion assessment reveals elevated rates, with a mean erosion coefficient (Z) of 0.61 and an annual erosion production of 182,712.9 m3, equivalent to a specific erosion rate of 961.6 m3/km2/year. Landslide susceptibility analysis identifies 31.8% of the municipality exhibiting a very high probability of landslides, while flash flood susceptibility models depict 3.3% of the area prone to very high flash flood potential. Forest fire susceptibility mapping emphasizes slightly less than one-third of the municipality’s forested area is highly or very highly susceptible to fires. Integration of these hazard models elucidates multi-hazard zones, revealing that 11.0% of the municipality’s territory faces concurrent vulnerabilities from excessive erosion, landslides, flash floods, and forest fires. These zones are predominantly located in upstream areas, valleys of river tributaries, and the estuary region. The identification of multi-hazard zones underscores the critical need for targeted preventive measures and robust land management strategies to mitigate potential disasters and safeguard both human infrastructure and natural ecosystems. Recommendations include the implementation of enhanced monitoring systems, validation methodologies, and community engagement initiatives to bolster hazard preparedness and response capabilities effectively.

A novel index to evaluate discretization methods: A case study of flood susceptibility assessment based on random forest

The selection of a suitable discretization method (DM) to discretize spatially continuous variables (SCVs) is critical in ML-based natural hazard susceptibility assessment. However, few studies start to consider the influence due to the selected DMs and how to efficiently select a suitable DM for each SCV. These issues were well addressed in this study. The information loss rate (ILR), an index based on the information entropy, seems can be used to select optimal DM for each SCV. However, the ILR fails to show the actual influence of discretization because such index only considers the total amount of information of the discretized variables departing from the original SCV. Facing this issue, we propose an index, information change rate (ICR), that focuses on the changed amount of information due to the discretization based on each cell, enabling the identification of the optimal DM. We develop a case study with Random Forest (training/testing ratio of 7 : 3) to assess flood susceptibility in Wanan County, China. The area under the curve-based and susceptibility maps-based approaches were presented to compare the ILR and ICR. The results show the ICR-based optimal DMs are more rational than the ILR-based ones in both cases. Moreover, we observed the ILR values are unnaturally small (<1%), whereas the ICR values are obviously more in line with general recognition (usually 10%–30%). The above results all demonstrate the superiority of the ICR. We consider this study fills up the existing research gaps, improving the ML-based natural hazard susceptibility assessments.

Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors.

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.

Flood susceptibility assessment based on a novel random Naïve Bayes method: A comparison between different factor discretization methods

Random Naïve Bayes (RNB) is a machine learning method that uses the Random Forest (RF) structure to optimize Naïve Bayes (NB). It is interesting to see whether RNB could optimize NB and achieve satisfied assessment results like RF in the flood susceptibility assessment study. RNB has rarely been used in study of using machine learning methods to spatially analyze natural disasters, and thus it was selected as the analysis method. Based on the data feasibility, 12 spatial factors that affect the occurrence and spatial distribution of floods were selected. To avoid the influence of subjective equal-interval classification method, natural breaks and quantile method were used to discretize factors with continuous values, respectively. Here, a recently proposed repeatedly random sampling method was adopted to select negative samples for RNB to generate a most accurate classifier (MAC) that was employed to compute the probability of flood occurrence in the study area. Consequently, this paper adopted the integrated framework of GIS and RNB to spatially assess the flood susceptibility using the Wanan County in China as an instance. The results demonstrated that when integrated with the repeatedly random sampling method, the MAC-based flood susceptibility maps corresponding to different factor discretization methods were similar, meaning this framework can effectively avoid the effects caused by different factor discretization methods. Also, to testify the classification performance of RNB, RF and NB were chosen to compare the classification performance with it. The results indicated the classification performance in the order of RF > RNB > NB. This means RNB is able to achieve better classification performance than NB, but it exists limitations when compared with traditional strong classifiers like RF. The findings of this paper proved that RNB is a feasible approach for natural hazard susceptibility assessment.

Use of Mamdani Fuzzy Algorithm for Multi-Hazard Susceptibility Assessment in a Developing Urban Settlement (Mamak, Ankara, Turkey)

Urban areas may be affected by multiple hazards, and integrated hazard susceptibility maps are needed for suitable site selection and planning. Furthermore, geological–geotechnical parameters, construction costs, and the spatial distribution of existing infrastructure should be taken into account for this purpose. Up-to-date land-use and land-cover (LULC) maps, as well as natural hazard susceptibility maps, can be frequently obtained from high-resolution satellite sensors. In this study, an integrated hazard susceptibility assessment was performed for a developing urban settlement (Mamak District of Ankara City, Turkey) considering landslide and flood potential. The flood susceptibility map of Ankara City was produced in a previous study using modified analytical hierarchical process (M-AHP) approach. The landslide susceptibility map was produced using the logistic regression technique in this study. Sentinel-2 images were employed for generating LULC data with the random forest classification method. Topographical derivatives obtained from a high-resolution digital elevation model and lithological parameters were employed for the production of landslide susceptibility maps. For the integrated hazard susceptibility assessment, the Mamdani fuzzy algorithm was considered, and the results are discussed in the present study. The results demonstrate that multi-hazard susceptibility assessment maps for urban planning can be obtained by combining a set of expert-based and ensemble learning methods.

Geotechnical Facets of Quaternary Sediments of Nubra Valley, Trans-Himalaya, India

In the present study, attempts have been made to evaluate the geotechnical characteristics of Quaternary sediments of the Nubra Valley located in high-altitude Ladakh Himalaya to demarcate the active sediment (soil) zones and their bearing on natural hazards susceptibility and mitigation measures. The experimental works have been carried out on collected sediment (soils) samples from different locations and depths to determine their grain-size-sensitive parameters and geotechnical responses. The results reveal that the majority of uniformly graded sediments (soils) of size 0.02–2.0 mm fall in wide range of values of shear strengths, cohesions (c) and angle of internal frictions, which may be due to distinct variations in their bulk and dry densities, void ratios, and liquid and plastic limits. In view of evaluated geotechnical parameters, the sediment sequences of the valley are classified in normal and active sediment zones. The sediment layers of active zone exhibit a high rate of reduction in shear strengths and cohesions in comparison with normal zone due to higher liquid and plastic limits of sediments subjected to temperature-sensitive freezing and thawing actions and may responsible for slope instabilities, differential settlements and mass flow hazards. Hence, such sediment layers require serious and careful investigations for safe settlements and mitigation measures of natural hazards.

Sensitivity of rainstorm-triggered shallow mass movements on gully slopes to topographical factors on the Chinese Loess Plateau

Topographical factor is one of the most important factors in understanding the formation mechanism of shallow mass movements on gully slopes on the Loess Plateau, China. However, the sensitivity of shallow mass movements on gully slopes to topography is poorly understood. Here, a series of rainfall simulation experiments was performed to quantitatively explore the influences of slope height (1.0 and 1.5 m) and gradient (60° and 70°) on shallow mass movements on the natural loess slopes on the Loess Plateau. Our research observed shallow mass movement processes on undisturbed slopes by using a Topography Meter and collected data that well reflected the nature of the processes. The conducted study has advantageous over traditional methods that are usually used in laboratory tests of backfilled soil. Results showed that the amount of shallow mass movement increased with increased slope height and gradient. The shallow mass movement rates (i.e., gravity erosion rates) increased by approximately 3–8 times with increased slope height from 1.0 m to 1.5 m, and increased by 148%–525% with increased slope gradient from 60° to 70°. The sensitivity coefficient of slope gradients for shallow mass movements was 2.1 times larger than that of slope heights for the shallow mass movements. Slope heights had the greatest effect on earthflow relative to slide and avalanche, whereas slope gradients had the greatest effect on slide. Our results provided insights into natural hazard susceptibility assessment and control erosion processes.

Natural hazards - their drivers, mechanisms and impacts in the Shyok-Nubra Valley, NW Himalaya, India

The Shyok-Nubra Valley situated in the NW Himalaya of India represents a dynamic environment where continuous tectonics has influenced the landscape, geologic and geomorphic processes. With existence of communities in such an environment it becomes very essential to document and understand the natural hazards. This paper provides a preliminary documentation and understanding of the natural hazards and their drivers, mechanisms and impacts in this region. Here, seismicity is poorly understood, and the earthquake hazards are primarily associated with collapse of building (Type-I and II) structures. Intense deformation has made the mountainous terrain very fragile, weak and highly susceptible to mass movements. The region is susceptible to floods and GLOFs, based on OSL dating, we report two flooding events (bracketed between 8.5 ± 0.4 and 7.5 ± 0.4 ka) and the increase in sedimentation rate over a period of 4.3 ka. Here, major settlements are on active fans that are highly vulnerable to numerous hazards. Fan hazard risk assessment in the Diskit area enabled identification of four hazard prone zones that should be avoided for any developmental work. Although the frequency of cloudburst and rain-storms has increased, very little is understood about the climate and its variability in this region. The region is prone to snow avalanche risk, but most of the settlements are located in the low susceptibility zone. The aeolian geomorphic processes are dominant in the region and desertification is prominent in some areas. This work provides a suitable context for resolving problems related to natural hazards susceptibility and developmental works in this region.

Geotechnical Facets of Active Sediments and its Bearing on Natural Hazards and Mitigation Measures: An Approach on Sediments of the Nubra Valley (Ladakh), Trans-Himalaya, India

The paper highlights the geotechnical attributes of Quaternary stratified sediments in Trans-Himalayan zone around the Nubra Valley (Ladakh), India for classifying the sediment’s sequences and characterizing their participation in natural hazards susceptibility and mitigation measures. The study is based on the experimental works performed on collected samples of sediments at different sites and depths (1.5 to 5.5 m) of sequences for determination of grain size sensitive parameters and geotechnical characteristics as per American Society for Testing and Materials standard and with help of empirical relationships. The results reveal that the majority of sediments are bimodal distributed with wide variation in uniformity coefficients (1.84-5.0), low variation in coefficient of curvatures (0.5-0.61) and sorting coefficient (1.12-1.69) exhibit wide range of shear strengths (25 kPa to 130 kPa), cohesions (0.02-0.13 kg/cm2) and angles of internal frictions (36°- 45°) which may be due to distinct differences in their densities, void ratios, liquid and plastic limits. Based on geotechnical values, the sediments strata can be categorized as foot-hill debris zone, normal sediment (soil) zone and active sediment (soil) zone. The foot-hill debris zone is found more prone to masswasting due to influence of gravity and their low binding habits. The active sediment (soil) zone exhibits lower shear strengths and cohesions in comparison to normal sediments due to higher liquid and plastic limits of sediments exposed to freezing and thawing actions. This zone is found more susceptible to slope instabilities and differential settlements hazards, hence requires specific care during engineering establishments and mitigation measures in the valley.

The Gravitational Process Path (GPP) model (v1.0) – a GIS-based simulation framework for gravitational processes

Abstract. The Gravitational Process Path (GPP) model can be used to simulate the process path and run-out area of gravitational processes based on a digital terrain model (DTM). The conceptual model combines several components (process path, run-out length, sink filling and material deposition) to simulate the movement of a mass point from an initiation site to the deposition area. For each component several modeling approaches are provided, which makes the tool configurable for different processes such as rockfall, debris flows or snow avalanches. The tool can be applied to regional-scale studies such as natural hazard susceptibility mapping but also contains components for scenario-based modeling of single events. Both the modeling approaches and precursor implementations of the tool have proven their applicability in numerous studies, also including geomorphological research questions such as the delineation of sediment cascades or the study of process connectivity. This is the first open-source implementation, completely re-written, extended and improved in many ways. The tool has been committed to the main repository of the System for Automated Geoscientific Analyses (SAGA) and thus will be available with every SAGA release.

Natural Hazard Susceptibility Assessment for Road Planning Using Spatial Multi-Criteria Analysis

Inadequate infrastructural networks can be detrimental to society if transport between locations becomes hindered or delayed, especially due to natural hazards which are difficult to control. Thus determining natural hazard susceptible areas and incorporating them in the initial planning process, may reduce infrastructural damages in the long run. The objective of this study was to evaluate the usefulness of expert judgments for assessing natural hazard susceptibility through a spatial multi-criteria analysis approach using hydrological, geological, and land use factors. To utilize spatial multi-criteria analysis for decision support, an analytic hierarchy process was adopted where expert judgments were evaluated individually and in an aggregated manner. The estimates of susceptible areas were then compared with the methods weighted linear combination using equal weights and factor interaction method. Results showed that inundation received the highest susceptibility. Using expert judgment showed to perform almost the same as equal weighting where the difference in susceptibility between the two for inundation was around 4%. The results also showed that downscaling could negatively affect the susceptibility assessment and be highly misleading. Susceptibility assessment through spatial multi-criteria analysis is useful for decision support in early road planning despite its limitation to the selection and use of decision rules and criteria. A natural hazard spatial multi-criteria analysis could be used to indicate areas where more investigations need to be undertaken from a natural hazard point of view, and to identify areas thought to have higher susceptibility along existing roads where mitigation measures could be targeted after in-situ investigations.

Geotechnical Attributes of Active Sediments and its Prospective in Hazards Susceptibility and Mitigation Measures: A Study on Soils from Leh Valley, Ladakh Himalaya, India

In the present study, attempts have been made to evaluate the geotechnical characteristics of Quaternary sediments from high altitude Ladakh Himalaya to demarcate the active sediment (soil) for its bearing on natural hazards susceptibility and mitigation measures. The experimental works have been carried out on collected sediments (soils) from different locations and depths during year 2014 for determination of grain size sensitive parameters and geotechnical attributes as per American Society for Testing and Materials standard and empirical relationships. The results reveal that the majority of uniformly graded sediments (soils) of size 0.02–2.0 mm exhibit wide range of shear, cohesions (c) and angle of internal frictions may be due to difference in their bulk and dry densities, void ratios, liquid limit, plastic limit and liquid index. Thus, the sediments of Leh Valley can be classified in normal sediment (soil) unit and normally active sediment (soil) unit. The normally active unit exhibits lower shear strengths and cohesions in comparison to normal unit due to higher liquid and plastic limits of sediment subjected to temperature sensitive freezing and thawing actions. The active sediment (soil) unit has been found more prone to slope instabilities, differential settlements and mass flow hazards, hence requires special care during developments and mitigation measures.

Natural hazards susceptibility mapping in Kuala Lumpur, Malaysia: an assessment using remote sensing and geographic information system (GIS)

Intensive geological fractures and their associated karst features in tropical regions are often associated with unpredictable environmental and geotechnical engineering problems. This requires precise modelling using modern techniques. A 20 m spatial resolution Digital Elevation Model (DEM), a Geographic Information System (GIS) and Weighted Spatial Probability Modelling (WSPM) were integrated to predict the occurrence of flooding, landslides, sinkholes and earth subsidence in Kuala Lumpur, Malaysia. Five essential thematic layers were extracted using set of automated algorithms and then they were weighted. Layers included were geological fractures, stream network, micro-depressions, slope and lithological contact. All these thematic layers were assigned weights according to their level of contributions to the occurrence of flooding, landslide, sinkholes and earth subsidence. The map demonstrated that areas having very high geohazard susceptibility show an area of 4.155 km2 (8%), whereas the areas characterized by low susceptibility for the occurrence of geohazard is approximately 16.394 (31.5%) of total Kuala Lumpur area. The natural hazard probability model was validated by comparing its results with the published landslide locations and geophysical and geotechnical maps of Kuala Lumpur and the comparison showed strong agreement.

Characterization and Hazard Study of Two Areas of Penang Island, Malaysia

ABSTRACT Penang Island of Malaysia is vulnerable to various natural hazards due to its hilly terrain and weather. Due to recent rapid population and economic growth, the probability of disaster associated with natural hazards has increased. This article characterizes geological and geotechnical parameters collected from field data that relate to natural hazard susceptibility. The areas of investigation were chosen so that each area lay on different geological formations. The variations in selected parameters with depth were obtained. The correlation within parameters was established and was found to be in agreement with that from other researchers. The investigation identifies parameters that can be used to evaluate the degree of hazard susceptibility.