Search Results (200)

Wireless sensor networks have become a vital technology that is extensively applied across multiple industries, including agriculture, industrial operations, and smart cities, as well as residential smart homes and environmental monitoring systems. Security threats emerge in these systems through hidden routing-level attacks such as Wormhole and Sinkhole attacks. The aim of this research was to develop a methodology for detecting security incidents in WSNs by conducting real-time analysis of Wormhole and Sinkhole attacks. Furthermore, the paper proposes a novel detection methodology combined with architectural enhancements to improve network robustness, measured by hop counts, delays, false data ratios, and route integrity. A real-time WSN infrastructure was developed using ZigBee and Global System for Mobile Communications/General Packet Radio Service (GSM/GPRS) technologies. To realistically simulate Wormhole and Sinkhole attack scenarios and conduct evaluations, we developed a modular cyber–physical architecture that supports real-time monitoring, repeatability, and integration of ZigBee- and GSM/GPRS-based attacker nodes. During the experimentation, Wormhole attacks caused the hop count to decrease from 4 to 3, while the average delay increased by 40%, and false sensor readings were introduced in over 30% of cases. Additionally, Sinkhole attacks led to a 27% increase in traffic concentration at the malicious node, disrupting load balancing and route integrity. The proposed multi-stage methodology includes data collection, preprocessing, anomaly detection using the 3-sigma rule, and risk-based decision making. Simulation results demonstrated that the methodology successfully detected route shortening, packet loss, and data manipulation in real time. Thus, the integration of anomaly-based detection with ZigBee and GSM/GPRS enables a timely response to security threats in critical WSN deployments. Full article

The objective of this study is to define groundwater management zones for a complex deformed and fissured Precambrian karst aquifer, which underlies one of the most important agricultural areas in the semi-arid region of Irecê, Bahia, Brazil. It is an unconfined aquifer, hundreds of meters thick, resulting from a large sequence of carbonates piled up by thrust faults during tectonic plate collisions. Groundwater recharge and flow in this aquifer are greatly influenced by karst features, through the high density of sinkholes and vertical wells. Over the past four decades, population and agricultural activities have increased in the region, resulting in unsustainable groundwater withdrawal and, at the same time, water quality degradation. Therefore, it is important to develop legal and environmental management strategies. This work proposes the division of the karst area into three well-defined management zones by mapping karst structures, land use, and urban occupation, as well as the concentrations of chloride and nitrate in the region’s groundwater. Zone 1 in the north possesses the lowest levels of karstification, anthropization, and contamination, while zone 2 in the central region has the highest levels and zone 3 in the south ranging in-between (except for stronger karstification). The delimitation of management zones will contribute to the development and implementation of optimized zone-specific groundwater preservation and restoration strategies. Full article

Internal soil erosion caused by water infiltration around defective buried pipes poses a significant threat to the long-term stability of underground infrastructures such as pipelines and highway culverts. This study employs a coupled computational fluid dynamics–discrete element method (CFD–DEM) framework to simulate the detachment, transport, and redistribution of soil particles under varying infiltration pressures and pipe defect geometries. Using ANSYS Fluent (CFD) and Rocky (DEM), the simulation resolves both the fluid flow field and granular particle dynamics, capturing erosion cavity formation, void evolution, and soil particle transport in three dimensions. The results reveal that increased infiltration pressure and defect size in the buried pipe significantly accelerate the process of erosion and sinkhole formation, leading to potentially unstable subsurface conditions. Visualization of particle migration, sinkhole development, and soil velocity distributions provides insight into the mechanisms driving localized failure. The findings highlight the importance of considering fluid–particle interactions and defect characteristics in the design and maintenance of buried structures, offering a predictive basis for assessing erosion risk and infrastructure vulnerability. Full article

A clear understanding of drought perceptions and adaptation behaviors adopted by farmers is an important way to cope with climate change and achieve sustainable agricultural development. Karst is a type of landscape where the dissolving of the bedrock has created sinkholes, sinking streams, caves, springs, and other characteristic features. The study took the Huajiang karst dry-hot river valley area located in the southwestern part of Guizhou as the study area and used questionnaire survey method, the index of perception and the diversity index of adaptation strategy to explore the risk perception, adaptation perception and adaptation behavior of farmers to non-climatic droughts in the subtropical karst dry-hot valleys. A total of 530 questionnaires were distributed and 520 were returned. The results show that (1) the farmers’ risk perception of drought is stronger than adaptation perception, which shows that although farmers are well aware of the possible risks posed by drought, their subjective initiative and motivation to adapt to drought are weaker; (2) in the face of drought, farmers prioritize selected non-farm measures for adaptation, followed by crop management and finally water resource management; and (3) compared to farmers in arid and semi-arid regions, those in karst hot-dry river valleys exhibit distinct adaptive behaviors in response to drought, particularly in water resource management. Full article

Atmospheric parameter acquisition along the vertical profile of the troposphere across different locations on the Earth is of primary importance in gaining knowledge of the evolution of large-scale meteorological systems and the relative movements of air masses. Normally, this happens thanks to the launch, into the atmosphere, of radiosondes connected to balloons filled with helium gas. However, on a small scale, and in particular geomorphological contexts, different and peculiar meteorological situations may arise, in which the air column in the lower layers can behave differently from normal, giving rise to the so-called thermal inversions. In this work, in a particular sinkhole in the Apulia region, the use of a multi-rotor UAV (Unmanned Aerial Vehicle) equipped with a temperature data logger was tested. The flight along the vertical, starting from the lowest point of the sinkhole, made it possible to archive the temperature data of the air column in the first 80 m of altitude. The data validation confirmed the goodness of the UAV acquisitions and their subsequent processing made it possible to extrapolate the vertical temperature profile of the sinkhole during the winter thermal inversion phenomenon. In addition to confirming the predisposition of this sinkhole to strong thermal inversions, the preliminary results of this work have highlighted the efficiency of this new methodology. It has proved to be useful in assessing small-scale vertical profiles of atmospheric variables in a relatively low altitude range. Furthermore, this methodology can represent a strong scientific and technological innovation applicable in the meteorological field and in that of environmental monitoring. Full article

Alcalá de Ebro is located 35 km northwest of the city of Zaragoza, on the right bank of the Ebro River at the outlet of a ravine (Juan Gastón) towards the river, with a catchment area of more than 230 km². Over time, urbanisation and agricultural development have eliminated the last stretch of the drainage channel, and these water inputs have been channelled underground, filtering through the ground. This section of the Ebro Valley rests on a marly tertiary substratum, which promotes dissolution-subbing processes that can lead to sinkholes. The ground tends to sink gradually or suddenly collapse. Many studies have been carried out to understand not only the origin of the phenomenon but also its geometry and the area affected by it in the town of Alcalá de Ebro. In this sense, it has been possible to model an area around the main access road, where numerous collapsing sinkholes have been found, blocking the road and affecting houses. It also affects the embankment that protects the town from the floods of the river Ebro. These studies have provided specific knowledge, enabling us to evaluate and implement underground consolidation measures, which have shown apparent success. Several injection campaigns have been carried out, initially with expansion resins and finally with columnar development, using special low-mobility mortars to fill and consolidate the undermined areas and prevent new subsidence. These technical solutions propose a method of ground treatment that we believe is novel for this type of geological process. The results have been satisfactory, but it is considered necessary to continue monitoring the situation and to extend attention to a wider area to prevent, as far as possible, new problems of subsidence and collapse. In this sense, the objective is to continue the control and monitoring of possible phenomena related to subsidence problems in the affected area and its immediate surroundings, to detect and, if necessary, anticipate subsidence or collapse phenomena that could affect the body of the embankment. Full article

The presented article is focused on a spatial analysis and identification of high and medium risk areas and their impact on potential for geotourism development in the Central Spiš region in Slovakia. To achieve this goal, we used a combination of two methods: the multi-hazard assessment method and the quantitative Geosite Assessment Model. The research results show that the geosites with the highest potential for the development of geotourism are also located in the low treat zone. These are mainly GS₉ adit Pavol (overall point value 16.25), GS₁ adit Rochus (15.25) and GS₈ adit Peter (14.00). On the other hand, geosites with a low point value for the development of geotourism, such as GS₁₀ sinkhole Baniská (7.75) and GS₅ tailings impoundment Markušovce (10.50), are located in the high treat zone. The obtained results show that even in the significantly anthropogenically burdened and economically underdeveloped post-mining landscape of the studied area, it is possible to identify positive impulses for further development. This concerns in particular the use of evaluated geosites in geotourism, with the aim of simultaneously supporting the protection of local cultural heritage, the natural environment and the socioeconomic development of the local community. Full article

The municipality of Grotte di Castro (Lazio Region, Italy) has, for some time, been the subject of various studies concerning the census of artificial cavities. Recent combined applications of LiDAR and photogrammetric surveys have made it possible to develop specific methods for locating entrances and producing 3D models and georeferenced plans. The combined use of these models with geomechanical surveys supported by surface seismic surveys makes it possible to understand the state of health of these buried structures and whether, in the event of an earthquake or as a result of natural decay, they could pose a danger to the population. In this work, which builds on recent studies, a method for assessing the state of these cavities is proposed and tested, essentially to evaluate the risks of collapse and sinkholes. The final objective is to census and discover as many cavities as possible, not only for the mitigation of risk but also for the valorisation of these cavities, which represent a true historical and archaeological heritage—testimonies to the history and evolution of ancient Castrum Criptarum. Full article

Sinkholes are geohazards that commonly form in karstifiable terrain and are an ever-present danger to infrastructure and human life. This paper aims to answer the question: Can a cover–subsidence sinkhole’s volume be determined using fibre Bragg grating sensor strain data and machine-learning techniques? Exploratory data analysis was conducted on fibre Bragg grating sensor strain data collected from an experimental test rig whereby a cover–subsidence sinkhole was formed. It was found that statistical techniques and machine-learning algorithms that assume normality are inappropriate when performing phase classification and volume regression tasks on the cover–subsidence sinkhole when given fibre Bragg grating sensor’s strain data. Weighted Least Squares regression, Support Vector Regression, and eXtreme Gradient Boosting were implemented on the data during phase two of the cover–subsidence sinkhole formation to determine the volume of the sinkhole. Weighted Least Squares regression obtained the lowest $R^2$ values for training and testing. Support Vector Regression had significantly improved results over Weighted Least Squares regression, while eXtreme Gradient Boosting obtained the highest $R^2$ values for training and testing. The highest $R^2$ values for eXtreme Gradient Boosting obtained were $1.00$ for training and $0.97$ for testing. In addition, eXtreme Gradient Boosting had the lowest root mean squared errors compared to Weighted Least Squares regression and Support Vector Regression. It was found that eXtreme Gradient Boosting is a strong candidate for determining the volume of the C–S sinkhole when using fibre Bragg grating strain data. Full article

Sinkholes, naturally occurring formations in karst regions, represent a significant environmental hazard, threatening infrastructure, agricultural lands, and human safety. In recent years, machine learning (ML) techniques have been extensively employed for sinkhole susceptibility mapping (SSM). However, the lack of explainability inherent in these methods remains a critical issue for decision-makers. In this study, sinkhole susceptibility in the Konya Closed Basin was mapped using an interpretable machine learning model based on SHapley Additive exPlanations (SHAP). The Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) algorithms were employed, and the interpretability of the model results was enhanced through SHAP analysis. Among the compared models, the RF model demonstrated the highest performance, achieving an accuracy of 95.5% and an AUC score of 98.8%, and was consequently selected for the development of the final susceptibility map. SHAP analyses revealed that factors such as proximity to fault lines, mean annual precipitation, and bicarbonate concentration difference are the most significant variables influencing sinkhole formation. Additionally, specific threshold values were quantified, and the critical effects of these contributing factors were analyzed in detail. This study underscores the importance of employing eXplainable Artificial Intelligence (XAI) techniques in natural hazard modeling, using SSM as an example, thereby providing decision-makers with a more reliable and comparable risk assessment. Full article

The Dead Sea ecosystem, with its hypersaline conditions, base-level fluctuations, and active tectonics, presents a unique challenge for geological studies. Its equilibrium is increasingly unbalanced due to overexploitation of water and mineral resources. Remote sensing, including drone-based photogrammetry and satellite imaging, monitors large-scale surface changes, while geophysical methods like electromagnetic and seismic surveys reveal subsurface structures. The integration of these methods has transformed our understanding. Combined studies now monitor hazards such as sinkholes, subsidence, and landslides with greater precision. Advances in artificial intelligence further enhance analysis by processing vast datasets to uncover previously undetectable trends. This synergy between remote sensing, geophysics, and AI offers efficient solutions for studying the disrupted ecosystem. Critical challenges include environmental degradation, rapid water loss, and sinkhole formation, threatening infrastructure, industries, and habitats. Remote sensing has been pivotal in monitoring and mitigating these hazards. Together with geophysics, it provides a robust framework for addressing these extreme conditions. By combining these methods, researchers gain valuable insights into the unique dynamics of the Dead Sea ecosystem, advancing scientific knowledge and supporting sustainable management strategies. Full article

Sinkholes are significant geohazards in karst regions that pose risks to landscapes and infrastructure by disrupting geological stability. Usually, sinkholes are mapped by field surveys, which is very cost-intensive with regard to vast coverages. One possible solution to derive sinkholes without entering the area is the use of high-resolution digital terrain models, which are also expensive with respect to remote areas. Therefore, this study focusses on the mapping of sinkholes in arid regions from open-access remote sensing data. The case study involves data from the Sentinel missions over the Mangystau region in Kazakhstan provided by the European Space Agency free of cost. The core of the technique is a multi-scale curvature filter bank that highlights sinkholes (and takyrs) by their very special illumination pattern in Sentinel-2 images. Marginal confusions with vegetation shadows are excluded by consulting the newly developed Combined Vegetation Doline Index based on Sentinel-1 and Sentinel-2. The geospatial analysis reveals distinct spatial correlations among sinkholes, takyrs, vegetation, and possible surface discharge. The generic and, therefore, transferable approach reached an accuracy of 92%. However, extensive reference data or comparable methods are not currently available. Full article

This article presents an example of the treatment of rock mass disturbed by shallow mining of hard coal in the Małopolska voivodeship, Poland. Considering various methods of rock mass recognition and ways of eliminating shallow voids, recipes for sealing slurries containing mainly liquefiers were developed and used in drilling and injection works in a 10 m-long hole. The course and intensity of rock layer deformation phenomena depend on both natural conditions and the mining method used. At a small depth of hard coal mining (up to 100 m below ground level), the fracture zone may reach the ground surface. In such conditions, sinkholes of various sizes may form on the ground surface. The proposed recipes for sealing slurries, as well as the presented technology for carrying out backfilling works, can be very useful at the stage of selecting the method for liquidation of shallow-lying voids in the carboniferous rock mass. Full article

Underground coal mining induces surface subsidence, which in turn impacts the stability of slopes in mountainous regions. However, research that investigates the coupling relationship between surface subsidence in mountainous regions and the occurrence of multiple surface hazards is scarce. Taking a coal mine in southwestern China as a case study, a detailed catalog of the surface hazards in the study area was created based on multi-temporal satellite imagery interpretation and Unmanned aerial vehicle (UAV) surveys. Using interferometric synthetic aperture radar (InSAR) technology and the logistic subsidence prediction method, this study investigated the evolution of surface subsidence induced by underground mining activities and its impact on the triggering of multiple surface hazards. We found that the study area experienced various types of surface hazards, including subsidence, landslides, debris flows, sinkholes, and ground fissures, due to the effects of underground mining activities. The InSAR monitoring results showed that the maximum subsidence at the back edge of the slope terrace was 98.2 mm, with the most severe deformation occurring at the mid-slope of the mountain, where the maximum subsidence reached 139.8 mm. The surface subsidence process followed an S-shaped curve, comprising the stages of initial subsidence, accelerated subsidence, and residual subsidence. Additionally, the subsidence continued even after coal mining operations concluded. Predictions derived from the logistic model indicate that the duration of residual surface subsidence in the study area is approximately 1 to 2 years. This study aimed to provide a scientific foundation for elucidating the temporal and spatial variation patterns of subsidence induced by underground coal mining in mountainous regions and its impact on the formation of multiple surface hazards. Full article

Monitoring reclaimed landfills is essential for ensuring their stability and monitoring the regularity of facility settlement. Insufficient recognition of the magnitude and directions of these changes can lead to serious damage to the body of the landfill (landslides, sinkholes) and, consequently, threaten the environment and the life and health of people near landfills. This study focuses on using UAV photogrammetry to monitor geometric changes in reclaimed landfills. This approach highlights the advantages of UAVs in expanding the monitoring and providing precise information critical for decision-making in the reclamation process. This study presents the result of annual photogrammetry measurements at the Słabomierz–Krzyżówka reclaimed landfill, located in the central part of Poland. The Multiscale Model to Model Cloud Comparison (M3C2) algorithm was used to determine deformation at the landfill. The results were simultaneously compared with the landfill’s reference (angular–linear) measurements. The mean vertical displacement error determined by the photogrammetric method was ±2.3 cm. The results showed that, with an appropriate measurement methodology, it is possible to decide on changes in geometry reliably. The collected 3D data also gives the possibility to improve the decision-making process related to repairing damage or determining the reclamation direction of the landfill, as well as preparing further development plans. Full article