Geohazard Mapping Research Articles

Geohazard Mapping and Mitigations of Landslides Along the Road Corridor Gasera–Indeto, Oromia Regional State, Southeast Ethiopia

Geohazard Mapping and Mitigations of Landslides Along the Road Corridor Gasera–Indeto, Oromia Regional State, Southeast Ethiopia

Escape routes and safe points in natural hazards. A case study for soil

The scope of this work was to develop soil liquefaction and landslide hazard maps, by computing Peak Ground Acceleration (PGA) for a return period of 475 years, to outline safe points and escape routes designed for schools situated in the drainage basin of Xerias River at northeastern Peloponnese, Greece. The school locations were spatially correlated with each geohazard map to assess the potential hazards to schools within the study area. Web maps illustrating safe and unsafe areas for both geohazards were developed though the GIS online platform for the town of Corinth and four settlements of the study area, where schools exist. Safe points were identified, and routes for evacuation from schools to these locations were mapped using the shortest paths on the existing road network for each geohazard. All the data was integrated into the GIS application available to the public. The findings indicated that 75% of the schools in the study area are situated in areas susceptible to soil liquefaction and landslide hazards. The northern part of the town of Corinth, which includes 62% of the town's schools, is situated in unsafe areas to soil liquefaction. Unsafe areas for landslides are located in the central and northern section of the town, encompassing 35% of the schools. The school buildings in one settlement within the study area are situated in safe areas for both geohazards. The schools in the remaining settlements are sited in areas considered unsafe due to either liquefaction or landslides hazard. A total of 12 safe points were proposed for soil liquefaction hazard and 17 for landslide hazard across all the studied urban areas. The suggested escape routes for both geohazards range from 200 to 1094 m, distances that teachers and students can easily walk. The proposed method can rapidly and effectively identify safe locations and evacuation routes, facilitating authorities in planning for evacuations.

Ultra-dense nodal seismic acquisition for automated geohazard analysis

In this paper, we address the problem of shallow geohazard mapping by developing an integrated workflow consisting of nodal seismic acquisition and automated data analysis. Densely spaced point-receiver land nodes and point sources are adopted as an alternative to receiver arrays or groups conventionally used in seismic exploration. The recently developed methodology of surface-consistent decomposition of the transmitted wavefield is applied to the nodal data. Ultra-resolution mapping of surface-consistent attributes in amplitude and phase is obtained, together with the generation of high-energy signal beams at the common midpoint positions, forming virtual (shot) supergathers (VSGs). The VSG preserves frequency and true-amplitude content thanks to the surface-consistent corrections applied before beam forming. Traveltimes, waveforms, and surface waves are then analyzed in the high signal-to-noise ratio VSG, leading to inversion of the subsurface parameters. Human-intensive interpretation of surface-wave dispersion curves in the f-k spectra is facilitated by the introduction of efficient machine learning algorithms for the unsupervised and supervised paradigms. The ultra-dense nodal acquisition and automated data analysis are effectively applied to the analysis of several cases of near-surface geohazards.

Geohazard Mapping of Eleme Spill Impacted Areas in Rivers State Using Remote Sensing and GIS

Geohazard Mapping of Eleme Spill Impacted Areas in Rivers State Using Remote Sensing and GIS

Identification of Precursors in InSAR Time Series Using Functional Data Analysis Post-Processing: Demonstration on Mud Volcano Eruptions

One of the most promising applications of satellite data is providing users in charge of land and emergency management with information and data to support decision making for geohazard mapping, monitoring and early warning. In this work, we consider ground displacement data obtained via interferometric processing of satellite radar imagery, and we provide a novel post-processing approach based on a Functional Data Analysis paradigm capable of detecting precursors in displacement time series. The proposed approach appropriately accounts for the spatial and temporal dependencies of the data and does not require prior assumptions on the deformation trend. As an illustrative case, we apply the developed method to the identification of precursors to a mud volcano eruption in the Santa Barbara village in Sicily, southern Italy, showing the advantages of using a Functional Data Analysis framework for anticipating the warning signal. Indeed, the proposed approach is able to detect precursors of the paroxysmal event in the time series of the locations close to the eruption vent and provides a warning signal months before a scalar approach would. The method presented can potentially be applied to a wide range of geological events, thus representing a valuable and far-reaching monitoring tool.

Landslide Susceptibility Assessment for Maragheh County, Iran, Using the Logistic Regression Algorithm

Landslide susceptibility assessment is the globally approved procedure to prepare geo-hazard maps of landslide-prone areas, which are highly used in urban management and minimizing the possible disasters due to landslides. Multiple approaches to providing susceptibility maps for landslides have one specification. Logistic regression is a statistical-based model that investigates the probabilities of the events which is received extensive success in landslide susceptibility assessment. The presented study attempted to use a logistic regression application to prepare the Maragheh County hazard risk map. In this regard, several predisposing factors (e.g., elevation, slope aspect, slope angle, rainfall, land use, lithology, weathering, distance from faults, distance from the river, distance from the road, and distance from cities) are identified as main responsible for landslide occurrence and 20 historical sliding events which used to prepare hazard risk maps. As verification, the models were controlled by operating relative characteristics (ROC) curves which reported the overall accuracy for susceptibility assessment. According to the results, the region is located in a moderate to high-hazard risk zone. The north and northeast parts of Maragheh County show high suitability for landslides. Verification results of the model indicated that the AUC estimated for the training set is 0.885, and the AUC estimated for the testing set is 0.769. To justify the model, the results of the LR were comparatively checked with several benchmark learning models. Results indicated that LR model performance is reasonable.

How could students be safe during flood and tsunami events?

Reduction of the impact of natural hazards and enhancement of the resilience of population involves access to straightforward information about safe and non-safe areas during the occurrence of a natural phenomenon. The present study focusses on the drainage basin of the Xerias River, at northeastern Peloponnese, Greece, an area highly prone to flooding, while tsunami inundation has also been documented. Firstly, the hazard assessment of the study area to the two phenomena was evaluated by using the Analytic Hierarchy Process and Geographical Information System along with geological, geomorphological, and meteorological data. A sensitivity analysis was used to calculate the uncertainties of both produced geohazard maps, while validation procedure was carried out to determine their accuracy. This study focused on the settlements that host primary and secondary schools to identify safe locations, as well as safe routes from schools to these locations. To this end, the hazard maps were used as input that estimated and divided the settlements into safe and non-safe areas concerning flooding and tsunami inundation. Then, safe locations and routes were defined for the evacuation of the population in case of occurrence of these two natural hazards and incorporated all aforementioned information into an open access application. Regarding flood hazard, the most prone areas are distributed at the northern part of the study area. The analysis for the hazard to tsunami inundation indicated that the most susceptible area is a narrow coastal zone across the city of Corinth. The proposed method appears to be quite accurate, and both geohazard maps is of excellent quality, according to the uncertainty analysis and validation findings. Τhe northern part of city of Corinth along with the nearby schools is threatened by both geohazards. The recommended safe locations for flood hazard are distant from the northern part of the city, while the recognized safe areas for tsunami hazard are not close to the coast. The schools of Athikia and Chiliomodi are located in non-safe areas regarding flooding and authorities should ensure the construction of flood shelter near schools. In the settlements of Solomos and Examilia, the school buildings are located in safe areas regarding flooding. This study highlights the importance of scientific analysis in safe location and route planning, brings together science and society, and provides useful information that can save human lives.

Twenty Years of CSEM Exploration in the Brazilian Continental Margin

The controlled source electromagnetic (CSEM) method is frequently used as a risk reduction tool in hydrocarbon exploration. This paper aims to provide a comprehensive historical review of the CSEM method’s twenty-year history in the Brazilian continental margin. Since 2003, we have significantly improved our understanding of CSEM resistivity data across various geological scenarios. This review presents a roadmap of the technical advancements in acquisition design and interpretation techniques. As a result, our understanding of the methodology has broadened from traditional to more general use, such as salt imaging, gas hydrates, geohazard mapping, and reservoir characterization. Finally, we indicate the potential upcoming CSEM applications in new energy resources and carbon capture and storage.

Geo-hazards assessment of the new-found industrial communities: an example from the 10th of Ramadan Industrial Region, Egypt

The impacts of Geo-hazard events are main obstacles to the use of land in numerous arid and semi-arid provinces of the World. Furthermore, the various activities of industrial communities, such as Egypt's 10th Ramadan Industrial Region (10th RIR), are the most dangerous examples of waste's impact on soil, surface water, and groundwater. The current study uses image processing and GIS tools to identify, delineate, monitor, and assess some of the environmental hazards caused by both human and natural activities in the 10th RIR. The findings revealed that there are rapid changes in land cover, which could be attributed to both anthropogenic and natural activities. Furthermore, the results revealed that waterlogging, salt-affected soils, water pollution, and flash floods were the most serious environmental threats to the 10th RIR. The obtained geo-hazard map showed that the 10th RIR has high flash flood hazards zones that are geospatially distributed in the southern parts of the Wadi Gafra basin and the western parts of 10th of Ramadan City. Furthermore, salinized soil zones are viewed as geospatially restricted zone in the eastern portion of 10th of Ramadan City and/or near the oxidation ponds. Additionally, vast areas of highly contaminated groundwater exist beneath industrial, urban, and oxidation pond zones. The current work provides decision-makers with numerous mitigation measures and recommendations required to minimize the impacts of geo-environmental hazards endangering the 10th RIR.

Editorial for the Special Issue: “Integrated Applications of Geo-Information in Environmental Monitoring”

Geo-information technology has been playing an increasingly important role in environmental monitoring in recent decades. With the continuous improvement in the spatial resolution of remote sensing images, the diversification of sensors and the development of processing packages, applications of a variety of geo-information, in particular, multi-resolution remote sensing and geographical data, have become momentous in environmental research, including land cover change detection and modeling, land degradation assessment, geohazard mapping and disaster damage assessment, mining and restoration monitoring, etc. In addition, machine learning algorithms such as Random Forests (RF) and Convolutional Neural Networks (CNN) have improved and deepened the applications of geo-information technology in environmental monitoring and assessment. The purpose of this Special Issue is to provide a platform for communication of high-quality research in the world in the domain of comprehensive application of geo-information technology. It contains 10 high-level scientific papers on the following topics such as desertification monitoring, governance of mining areas, identification of marine dynamic targets, extraction of buildings, and so on.

Annual Soil Loss Estimation in a Tropical River Basin of Southern India Using RUSLE Model and AHP Techniques

Remote sensing and geographic information system (GIS) techniques have been shown to be effective tools for geohazard mapping. The Western Ghats of southern India are a classic terrain with a variety of geohazards such as landslides and soil erosion. The current study employs RUSLE methodology in conjunction with remote sensing and GIS to estimate annual soil loss and identify zones of high erosion potential in one of southern India's major tropical river basins, the Chalakkudy River Basin. The resulting map elaborates on the area's annual soil erosion to the tune of 14.7008 t ha1 year1. The map also divides the terrain into zones based on the likelihood of soil erosion. According to the study, approximately 4.70 percent of the total area (67.13 km2) is prone to severe soil erosion, while the remaining 67.13 percent (972.32 km2) is prone to high erosion. Soil erosion prognosis analysis assists decision-makers in developing a proper conservation planning programme to reduce soil erosion. Analytical Hierarchy Process (AHP) was used to identify critical soil erosion- prone areas by integrating geo-environmental variables such as land use/land cover, geomorphology, lithology, drainage density, lineament density, slope, aspect, elevation, LS factor, rainfall, soil texture, and soil depth after determining their relative contribution in conditioning the terrain susceptible to soil erosion. Only 4.70% of the total area is highly prone to soil erosion. Keywords: Remote Sensing, GIS, RUSLE, Soil Erosion, AHP, Chalakkudy

Multiple Linear Regression Analysis Applied for Modeling in Torrens Community Disaster Resilience Score Card for Flood-Prone Schools in the Schools Division of Misamis Oriental, Northern Mindanao – the Philippines

Accurate assessment of the level and extent of disaster resilience of flood-prone schools and their communities is a complicated task. This is given the various means to measure disaster resilience. This is further complicated by the fact that disaster resilience is context-specific. Therefore, applying one of these measures for disaster resilience modeling proves to be a practical approach, as this model has the advantage of having context-specific viability in predicting disaster resilience. One of the statistical methods that can be used for testing a model in disaster resilience is Multiple Linear Regression (MLR). Therefore, this work aims to test a model for disaster resilience. For this, 26 schools in the Schools Division of Misamis Oriental were identified as flood-prone areas based on the geo-hazard maps of the Department of Environment and Natural Resources. With the use of the Torrens Community Disaster Resilience Score Card, the schools and their communities were rated by their respective School Administrator/Principal; School Disaster Risk Reduction Management Coordinator; Municipal Disaster Risk Reduction Management Officer; President of the General Parents-Teachers Association; President of the Student School Government; and the Barangay Chairperson. The ratings served as the data. Multiple Linear Regression was applied to generate a new model with four explanatory variables, i.e. community-connectedness, risk and vulnerability, planning and procedures, and available resources; and the response variable is Disaster Resilience. The MLR model obtained an Adjusted R Square of 0.965, which means that the four explanatory variables explained 96.5% of the variability of Disaster Resilience. The Goodness of Fit of the model had shown a right-tailed, F=175.9, p-value=7.77156e-16, thus, rejecting the null hypothesis that the model is not a good fit. All four variables are significant predictors of Disaster Resilience, hence, attesting to the adequacy of the model. Thus indicative that in the context of these flood-prone school and their communities, the model justifies that the application of the said scorecard is of great importance to these schools and their communities as well as those people responsible for disaster risk and reduction management regular monitoring and assessment for disaster resilience.

Improved prediction of the vertical distribution of ground ice in Arctic-Antarctic permafrost sediments

Global warming and permafrost degradation are impacting landscapes, ecosystems and the climate-carbon system. Current ground ice and geohazard maps rely on the frost susceptibility of surficial sediments, and substantial areas underestimate ice abundance. Here we use a soil environmental model to show the importance of considering unfrozen water content (dependent on sediment type, soil water chemistry, and temperature) when assessing the frost susceptibility of sediments. Our ensemble modeling of the vertical structure and evolution of ground ice for fine to coarse-grained sediments matches reasonably well with field measurements at sites from the low Arctic to the cold and hyper-arid Dry Valleys of Antarctica. Our modeling indicates a need to re-evaluate how frost-susceptible sediments are identified when mapping ice-rich permafrost landscapes and provides a framework for the development of quantitative estimates of the vertical distribution of ground ice in permafrost sediments at regional scale.

Explainable Boosting Machines for Slope Failure Spatial Predictive Modeling

Machine learning (ML) methods, such as artificial neural networks (ANN), k-nearest neighbors (kNN), random forests (RF), support vector machines (SVM), and boosted decision trees (DTs), may offer stronger predictive performance than more traditional, parametric methods, such as linear regression, multiple linear regression, and logistic regression (LR), for specific mapping and modeling tasks. However, this increased performance is often accompanied by increased model complexity and decreased interpretability, resulting in critiques of their “black box” nature, which highlights the need for algorithms that can offer both strong predictive performance and interpretability. This is especially true when the global model and predictions for specific data points need to be explainable in order for the model to be of use. Explainable boosting machines (EBM), an augmentation and refinement of generalize additive models (GAMs), has been proposed as an empirical modeling method that offers both interpretable results and strong predictive performance. The trained model can be graphically summarized as a set of functions relating each predictor variable to the dependent variable along with heat maps representing interactions between selected pairs of predictor variables. In this study, we assess EBMs for predicting the likelihood or probability of slope failure occurrence based on digital terrain characteristics in four separate Major Land Resource Areas (MLRAs) in the state of West Virginia, USA and compare the results to those obtained with LR, kNN, RF, and SVM. EBM provided predictive accuracies comparable to RF and SVM and better than LR and kNN. The generated functions and visualizations for each predictor variable and included interactions between pairs of predictor variables, estimation of variable importance based on average mean absolute scores, and provided scores for each predictor variable for new predictions add interpretability, but additional work is needed to quantify how these outputs may be impacted by variable correlation, inclusion of interaction terms, and large feature spaces. Further exploration of EBM is merited for geohazard mapping and modeling in particular and spatial predictive mapping and modeling in general, especially when the value or use of the resulting predictions would be greatly enhanced by improved interpretability globally and availability of prediction explanations at each cell or aggregating unit within the mapped or modeled extent.

Stratigraphic and sedimentologic investigation of collapse features in the Mount Baldy Dune in the Indiana Dunes National Park

Mount Baldy dune is one of the largest active eolian dunes along the southern shore of Lake Michigan and a famous tourist attraction within the Indiana Dunes National Park. It is an anthropogenically disturbed landform where restoration efforts include native beach-grass plantings and restricted foot traffic. In July 2013, a 6-year-old boy was buried more than 3 m below the dune stoss surface after falling into a hole, or “collapse feature.” While previous work indicated that holes formed through the decay of buried oak trees (Quercus spp.), neither the origin of this hole in relation to the dune’s stratigraphy nor its significance as a potential geological hazard were understood. Groundpenetrating radar, sediment cores, and radiocarbon and optically stimulated luminescence dates were collected to define sedimentary packages and facies, and interpret the dune’s depositional history. The data show that an alongshore migrating coastal dune field stabilized ∼3000 ya atop backbarrier lagoonal deposits. Modern Mount Baldy evolved as sands were mobilized and transported onshore over the relict dune field, triggered by ongoing downdrift coastal erosion related to the development of the Michigan City Harbor. The internal architecture of the dune reveals how the topography of the forested landscape, now buried beneath the modern dune form, contributes to variations in the thickness of the upper dune package, controlling the relation of buried and decayed trees to the surface of the stoss slope. The data on sand thicknesses, were used with maps and aerial photographs to create a geohazard map of the dune.

High-Resolution Mining-Induced Geo-Hazard Mapping Using Random Forest: A Case Study of Liaojiaping Orefield, Central China

Mining-induced geo-hazard mapping (MGM) is a critical step for reducing and avoiding tremendous losses of human life, mine production, and property that are caused by ore mining. Due to the restriction of the survey techniques and data sources, high-resolution MGM remains a big challenge. To overcome this problem, in this research, such an MGM was conducted using detailed geological exploration and topographic survey data as well as Gaofen-1 satellite imagery as multi-source geoscience datasets and machine learning technique taking Liaojiaping Orefield, Central China as an example. First, using Gaofen-1 panchromatic and multispectral (PMS) sensor data and Random Forest (RF) non-parametric ensemble classifier, a seven-class land cover map was generated for the study area with an overall accuracy (OA) and Kappa coefficient (KC) of 99.69% and 98.37%, respectively. Next, several environmental drivers including land cover, topography (aspect and slope), lithology, distance from fault, elevation difference between surface and underground excavation, and the difference of spectral information from PMS multispectral data of different years were integrated as predictors to construct an RF-based MGM model. The constructed model showed an excellent prediction performance, with an OA of 98.53%, KC of 97.06%, and AUC of 0.998, and the 85.60% of the observed geo-disaster that have occurred in the predicted high susceptibility class (encompassing 2.82% of the study area). The results suggested that the changes in environmental factors in the high susceptibility areas can be used as indicators for monitoring and early-warning of the geo-disaster occurrence.

Modeling the Environmental Hazards of El-Kharga Oasis Sand Dunes, Western Desert of Egypt, using Remote Sensing and GIS Techniques

In this study, an integrated suite of Remote Sensing (RS) data and Geographic Information System (GIS) techniques supported by fieldwork is used to assess the sand dunes movement hazards at El-Kharga Oasis. Digital Elevation Model (DEM) data obtained from the Shuttle Radar Topography Mission (SRTM) and Advanced Spaceborne Thermal Emission and Reflection (ASTER) were integrated with GIS techniques to model the vulnerable locations and to study the terrain characteristics (slope angles and aspects) in the studied area. The risk assessment model output was verified with the field investigations using multi-temporal satellite images recorded between 1990 and 2019. Monthly wind roses showed that the sand drifts in the southeastern direction differed widely from one direction to another depending on the wind direction and velocity. The most important output of the spatial model’s, results was a geo-hazard map that classified the sand dunes hazard zones into low, slight, moderate, and high-risk zones. It is concluded that, the sand dunes pose a serious hazard because of their fast movement and accumulation near the monumental sites, over roads and invading the agricultural fields. The obtained results can serve as a basis for planners and decision-makers to take the necessary precautions and measures to minimize the sand dune hazard’s impact on the monumental sites (e.g., Hibis, El-Nadura, El-Ghueita and El-Zayyan), roads, and the agricultural fields at El-Kharga Oasis and lead to a sustainable development plan. Keywords Environmental Hazards; Sand Dunes Movement; Remote Sensing; Risk Assessment, Egypt

The Application of Terrestrial LiDAR for Geohazard Mapping, Monitoring and Modelling in the British Geological Survey

Geomatics is the discipline of electronically gathering, storing, processing, and delivering spatially related digital information; it continues to be one of the fastest expanding global markets, driven by technology. The British Geological Survey (BGS) geomatics capabilities have been utilized in a variety of scientific studies such as the monitoring of actively growing volcanic lava domes and rapidly retreating glaciers; coastal erosion and platform evolution; inland and coastal landslide modelling; mapping of geological structures and fault boundaries; rock stability and subsidence feature analysis, and geo-conservation. In 2000, the BGS became the first organization outside the mining industry to use Terrestrial LiDAR Scanning (TLS) as a tool for measuring change; paired with a Global Navigation Satellite System (GNSS), BGS were able to measure, monitor, and model geomorphological features of landslides in the United Kingdom (UK) digitally. Many technologies are used by the BGS to monitor the earth, employed on satellites, airplanes, drones, and ground-based equipment, in both research and commercial settings to carry out mapping, monitoring, and modelling of earth surfaces and processes. Outside BGS, these technologies are used for close-range, high-accuracy applications such as bridge and dam monitoring, crime and accident scene analysis, forest canopy and biomass measurements and military applications.

Integration of remotely sensed and seismicity data for geo-natural hazard assessment along the Red Sea Coast, Egypt

The flooding and seismic activity analysis clearly shows the urgent need for an evaluation planning program to mitigate hazard for the vital areas. The Red Sea coastal area is always exposed to the risk of flash floods and earthquakes; therefore, the management and determination of geo-hazard in these areas are critically important. A risk map was produced for the study area by integrated flash flooding and seismicity data. Several valuable data have been applied to accomplish this work which include ALOS-PALSAR, satellite images, and earthquake data. Seismic hazard assessment was performed using probabilistic earthquake hazard assessment; Results of the iso-acceleration maps and hazard curves for a return period of 475 years showed that the seismic hazard values increase from southern to northern parts. The morphometric parameter interpretations illustrate that 6 sub-basins (40% of total sub-basins) are of high-flooding hazardous, 5 sub-basins of moderate hazard (33.33%), and 4 of low hazard (26.66%). Finally, geo-hazard map results markedly increase from south to north in the study area. So, we recommend that some dams and channels should be constructed in vital sites along and at the end of the channel Wadi to protect it against flood hazards along the Red Sea Coast for long-term sustainable development without problems. These findings can be a potential guide for decision-makers in disaster geo-hazard reduction in this interest coastal area.

A Multi-Spatial Assessment Framework To Geological Hazard For High-Rise Building Project In Metro Manila, Philippines

On Metro Manila Earthquake Impact Reduction Study (MMEIRS) conducted from 2002 to 2004 by Japan International Cooperation Agency (JICA) together with the Philippine Institute for Volcanology and Seismology (PHIVOLCS) and the Metro Manila Development Authority (MMDA), a worst-case scenario with a 7.2 earthquake magnitude in Makati, would have structural impacts on 50% of infrastructures from partial to heavy damage, 18.3% would be heavily damaged, 32.8% would be partially damaged, and major lifelines and utilities that provide electricity, telephones, and water could be cut off. There will be 2,300 casualties, 7,700 injuries, and 156,000 displacements. Geological hazard threatens the City residents and its attractiveness as the preferred residential and business location in the country. In this study, we model the earthquake risk of high-rise building projects in Global City, located at the eastern portion of Makati City near Marikina Valley Fault System. To accomplish this, we identified important geological hazard-related factors and distributed survey question to experts to gather estimation of the importance of each factor, and Monte Carlo Analytic Hierarchy Process (MCAHP) was used to determine the consistency of the expert’s judgment. These factor weights from the MCAHP are applied to the gathered data, and a Quantum GIS software tool was utilized for visualization, producing a geo-hazard map that is color-coded representing weighted-simulation levels of estimated earthquake hazard risks.