Present-day landscapes and biomes result from interactions among climate, landform dynamics, and atmospheric processes, driving environmental risks such as soil erosion, hydrological changes, and land degradation. Geological records show transitions between cool-cold and temperate-warm climate cycles, shaping both landforms and ecosystems. The geopaleontological record, from deep geological time to present, shows climate as the cumulative result of tens of thousands of years of sub-climate phenomena, which stem from weather events unfolding over decades to centuries. These long-term records provide vital context for assessing geological hazards and guiding sustainable land and water management. Geological evidence shows that climatic fluctuations occur over millennial time scales, far beyond the lifespan of any organism and human influence. This reveals the inadequacy of current meteorological and climatic definitions, which overlook geological complexities. A precise redefinition is needed, distinguishing decadal, centennial, and millennial phenomena to prevent flawed definitions that misguide climate mitigation efforts. Revising meteorological terminology would refocus efforts on adapting to climate realities and implementing effective land and water management policies. This study emphasizes adaptive management integrating geological and climatic data. Clear definitions of climate, sub-climate, and meteorological events at global, regional, and local levels are vital for forecasting risks and promoting sustainable solutions.
Identifying and filtering vegetation from photogrammetricbased point cloud data are required for many applications, such as environmental monitoring, urban planning, forestry and hazard management. This work presents a comprehensive study on point classification using advanced tree-based machine learning models, including Decision Tree Classifier, Random Forest,AdaBoost, XGBoost, and CatBoost. Six different datasets are utilized for comparison between the machine learning models. Random Forest classifier with hyperparameter tuning outperforms other models, demonstrating superior precision in filtering vegetation points. Visible-band vegetation indices with fixed thresholds are also evaluated, but their accuracy is lower than that of machine learning models, despite being easier to implement. The findings not only show the performances of tree-based machine learning models for filtering vegetation in photogrammetric 3D point cloud data, but they also suggest promising potential to transform the binary classification task into a multi-class classification to achieve higher granularity.
The paper illustrates the 2024-geomorphological setting of the Civita di Bagnoregio area (Central Italy, Lazio region), defined on geomorphological survey, drone footage examination and bibliographic data analysis. It also emphasizes how the peculiar and enchanting geomorphological features of this area (the `geomorphological heritage') has deeply conditioned the lives of the inhabitants from Protohistory to the 20th century, therefore appearing to constitute the primary shaping factor of the identity, memory, and history (the `cultural heritage') of the communities settled on the cliff through time.
As disasters are occurring more frequently and weather extremes get harsher, communities and governments around the world are facing more and more difficulties. In order to address these problems, disaster preparedness must be done precisely. This research examines in detail the transformative impact that machine learning algorithms have on bolstering disaster preparedness and response systems. Beyond a simple synopsis, our study's Enhanced Pelican Optimization based on Disaster Prediction long short-term Memory (EPO-DPLSTM) is remarkable and shows off the advanced capabilities of Machine Learning (ML) in predicting a wide range of patterns of the weather and natural disasters, such as waves in heat, hurricanes, floods, droughts, and more. In order to assist the improved efficacy of prediction models in disaster preparedness, we made useful observations into the intricacies of application using ML. In addition to outlining the theoretical underpinnings, the study offers empirical evidence of the substantial advantages that machine learning algorithms offer. By using these precise forecasts of past geological disasters and new weather trends, preventative measures might be put in place, ultimately saving lives and lessening the extent of the damage. Regional landslide catastrophe early-warning is a crucial tool for disaster prevention and mitigation in China, where disasters are severe. A proposed approach to regional disaster warning was presented in this research. The model creation process includes warning output, model parameter optimization, sample learning and training, sample-set construction, and so forth. Eighty percent of the training sample set was used as the trained set, and twenty percent was utilized as the testing set for cross-validation in the sample learning and training process. The model parameters were optimized using the Enhanced Pelican Optimization based on the Disaster Prediction Long Short-Term Memory (LSTM) algorithm, and the accuracy, Receiver Operating Characteristic (ROC curve), and Area Under the Curve (AUC) value were utilized to confirm the model's generalization capacity and accuracy. To improve model training, five machine learning methods were used; the results indicated that the suggested algorithm was the model with the best generalization capacity (AUC was 0.989) and performed the best, with an accuracy of 99.5%. The findings of this study provide critical scientific support for policymakers, emergency planners, and local stakeholders, enabling the development of more targeted, data-driven disaster mitigation strategies and strengthening regional resilience against future geological hazards.
The aim of this study was to examine the methods, tools, and platforms used for the analysis of spatial data, as well as to assess their potential for solving environmental challenges. The study considers geostatistical methods, including kriging, variogram analysis, semivariance, the Thiessen polygon method, inverse distance weighting (IDW) interpolation, and regression models such as linear regression, multiple regression, and geographically weighted regression (GWR). A literature search turned up 52 peer-reviewed and indexed articles on methods including regression models, variogram analysis, and kriging. The selection criteria included: (1) relevance to geostatistical analysis of environmental data, (2) methodological rigor, (3) publication in high-impact peer-reviewed journals, and (4) citation frequency indicating scientific significance. These studies highlight the effectiveness of geostatistical methods, geospatial platforms, and Python in environmental monitoring and predictive modeling. For classification tasks, logistic regression and decision trees were examined. The study results demonstrate that the application of modern geostatistical methods allows for the identification of spatial distribution patterns of environmental data and improves prediction accuracy. In particular, it was found that the spatial autocorrelation index effectively determines areas with high levels of similarity in environmental parameters, while local indicators of spatial association (LISA) help identify regional clusters with high pollution intensity or other anomalous characteristics. It was demonstrated that the use of spatial modelling platforms, such as Geographic Information System (GIS) software like ArcGIS and Quantum GIS (QGIS), along with the Python programming language and spatial data analysis libraries such as GeoPandas and the Python Spatial Analysis Library (PySAL), significantly enhances the effectiveness of environmental phenomenon analysis. The integration of satellite image data with geostatistical methods was found to contribute to the creation of more accurate maps for forecasting environmental risks. The proposed approaches demonstrate significant potential for environmental monitoring and natural resource management, enhancing the understanding of spatial patterns and serving as a basis for further research in this field.
Groundwater is critical in countries with arid to semi-arid climates and limited surface water availability. Groundwater use is strongly related to its quality. The most important elements influencing groundwater quality are the type of underlying rock, the amount of rainfall, and the type of soil through which surface water seeps into subsurface layers. The study area extends south of Sinjar Mountain and toward the town of Baaj. It is around 60 kilometers long and 30 km wide. The water catchment region on Sinjar Mountain's southern flank replenishes groundwater. Residents in this area rely on well water to support their civil and agricultural needs due to a lack of surface water and poor rainfall, which can fall below 350 mmlyear on average. Chemical analyses (Ca2+, Mg2+, Nat, Kt, HCO3-, SO42-, Cl-, NO3-) and physical tests (electrical conductivity (E.c.) and total dissolved salts (TDS)) were used to estimate the drinking water quality index (WQI) and irrigation water classification parameters (percentage of sodium adsorption, SAR; percentage of sodium, SSP; percentage of magnesium, MAR; Permeability Index, PI, and Kelly's Ratio, KR). The upper part of the investigated area represents an underground water reservoir in limestone strata, which are designated as good for drinking purposes, encouraging the development of many residential complexes in the region. The lower section depicts groundwater reservoirs in the evaporite strata, as well as the influence of infiltrated water containing the dissolving products of gypsum and carbonate rock fragments, which are classed as poor to unsuitable for drinking. Most wells indicated that their water was appropriate for irrigation. This serves to revitalize agricultural operations in the region, whether through supplementary irrigation or irrigation of farms distributed around the region.
This study applies the Analytic Hierarchy Process (AHP) and Geographic Information Systems (GIS) to assess flood vulnerability in Tebessa, Algeria, considering social, physical, and resource-related factors. Between 2008 and 2023, rainfall events of 45-70 mm caused extensive flooding, impacting most urban areas, especially rapidly growing neighborhoods, and resulting in significant damage to buildings and infrastructure. Social factors were found to be the main contributors to risk exposure. High hazard zones cover 32.06% of the city, mainly in central areas and older neighborhoods, while medium and low hazard levels account for 18% and 28%, mostly in peripheral areas. The uncontrolled expansion along river corridors (Zaarour, Naqis, Rafanah, and Saqi) has increased flood risk. The combined AHP-GIS approach identifies critical zones, evaluates potential impacts, and supports management strategies, including contingency planning and land-use regulation. The resulting thematic vulnerability maps provide essential guidance for prioritizing risk areas, improving urban resilience, and implementing sustainable planning and prevention measures. By synthesizing complex spatial data into a comprehensive vulnerability index, this methodology facilitates informed decision-making, protects people and infrastructure, and strengthens flood risk management in Tebessa.
The aim of this paper is to present a multi-level approach to risk identification and monitoring strategies for the lakeshore archaeological sites. Within a range of the cultural heritage typologies that are addressed by the TRIQUETRA project, the Late BronzeAge/Early Iron Age fortified settlement at Smuszewo (Poland) occupies a transitional position between mainland and water environment. Archaeological excavations and other surveys conducted between the 1950s and 2010s revealed wellpreserved wooden structures on land and on the east shore of Czeszewo Lake. Crucial to their preservation is the waterlogged environment which is directly related to the condition of the lake, water balance and particularly the water level. The problem of deteriorating water conditions (e.g. decreasing water level) in neighboring areas-resulting in recurrent droughts-has already been identified. However, its impact on the fragile wooden relics of the fortified settlement has not yet been assessed.
Les Argilliez, part of the UNESCO World Heritage Site "Prehistoric Pile Dwellings around the Alps," is in Lake Neuch & acirc;tel (Switzerland) and dates to the Classical Cortaillod (3841-3817 BC) and Late Cortaillod (around 3500 BC) cultures. Among other artifacts, it consists of 4,834 wooden piles found over a 7000 m2 area ranging from 2 m to 3 m depth below the water surface. Two dangers threaten the preservation of the site: erosion and the proliferation of invasive mussel species: Dreissena rostriformis bugensis (quagga mussel) and Dreissena polymorpha (zebra mussel), which pose the specific threat risk of degrading the wooden piles. This study presents two methods developed for the monitoring of the erosion and mussel populations at Les Argilliez: a new flash lidar based 3D imaging platform and satellite image data analysis. Together, the two data collection schemes allow for efficient identification, quantification and tracking of environmental risks threatening underwater archeological sites, such as this one. The flash lidar is optimized for underwater applications and designed to collect 3D point cloud data from a medium-sized unmanned surface vehicle (USV). The lidar system is battery-powered and features a 128x128 pixel focal plane array for high resolution point cloud capture. When mounted to the USV, the lidar enables regular and efficient lake surveys, allowing consistent comparison of the same locations over time. The point cloud data enables measuring the height and orientation of the wooden piles and the lakebed profile. The first demonstrative lidar measurement results are presented in this study. In addition, spectral data from satellite images taken by Sentinel-2 was compared with simulated reflectance spectra for sand, macrophytes and mussels. The processed satellite data successfully identifies spectral anomalies correlated to the proliferation of quagga mussels in the area over a period of four years, and is confirmed by underwater surveys done by divers.
In the light of threats including climate change, geological degradation and extreme weather conditions, the geometric documentation of cultural heritage sites plays a crucial role in their preservation. Photogrammetric techniques enable the production of highly accurate 3D models, orthoimages, and digital surface models (DSMs), which facilitate both site monitoring and conservation planning. This article presents the photogrammetric documentation of three archaeological sites in Greece, namely, the archaeological site of Aegina Kolonna, the Sunken City and the coastal cultural heritage of Ancient Epidaurus, as well as the sanctuary of Kalapodi. The geometric documentation of all three sites was conducted within the framework of the TRIQUETRA EU-funded project, through ground surveys and UAV-based photogrammetric techniques, either independently or in combination with underwater photogrammetry workflows, in order to capture the geometry of the cultural heritage sites and their surrounding environments. The produced results include 3D dense point clouds, 3D textured mesh models, DSMs and high-resolution orthomosaics. The generated datasets support detailed structural assessments, vulnerability analyses and risk assessment studies, providing a fundamental basis for protection efforts of the archaeological sites of interest.
Rose Island (Germany) is part of the UNESCO World Heritage site "Prehistoric Pile Dwellings around the Alps" and a pilot site of the EU funded project TRIQUETRA, which targets the risks of climate change on cultural heritage. With the lack of a detailed bathymetric map of the waters around Rose Island and in search for an efficient approach for documenting the wooden relics from Iron Age at the lake bottom, both a sonar and a photogrammetric campaign were conducted by the GermanAerospace Center(DLR). From the sonar measurements, the first reliable bathymetric map of the area was generated and provided to TRIQUETRA's decision support system and WebGIS. During the photogrammetric survey, 15.000 high resolution images of the lake floor were taken by an unmanned surface vehicle (USV) and processed to high-resolution 3D models by using the structure-from-motion method (SfM). The models provide an unprecedented level of detail for the documentation and examination of the archaeologic remains at Rose Island and a fascinating insight to the prehistoric settlement remains for the general public.
Cultural heritage sites are increasingly at risk due to climate change and environmental hazards, which can include floods, erosion, and ground instabilities. Climate forcing may also favour the impact of more severe anthropogenic hazards, as in the case of wildfires. In this framework, remote sensing may provide hazard quantifications and risk assessment, supporting the definition of conservation planning and protective measures. This study explores the analysis of optical satellite images to monitor and assess natural and anthropogenic threats to Ventotene and Santo Stefano islands, Italy. Specifically, we quantify damage by wildfires nearby cultural heritage sites from 2017 to 2024, while multispectral images allow a first assessment of bathymetry around the islands and deriving water constituents parameters. This enables a more comprehensive hazard analysis with a lookout on the process understanding and definition of the risks these sites face.
The TRIQUETRAKBP, a core component ofthe TRIQUETRA DSS, comprises two key elements: a comprehensive database housing all outputs from the project's literature review and a WebGIS platform integrating data from pilot CH sites such as Aegina, Choirokoitia, Epidaurus, Kalapodi, Les Argilliez, Roseninsel, Smuszewo, and Ventotene. As a dynamic electronic repository, the KBP offers extensive data and advanced search tools for efficient information retrieval. The TRIQUETRA project aims to develop a robust, evidence-based DSS to mitigate the impacts of climate change on CH monuments. The DSS toolbox includes the Risk Severity Quantification module and the Mitigation Measure Selection and Optimisation module. The latter provides tailored mitigation measures for each pilot site, offering tangible recommendations for risk mitigation through a multicriteria search feature that allows filtering based on cost, timeframe, and topological effects (IoANNIDIs et alii, 2024). Designed for adaptability and scalability, the module leverages the KBP database to propose solutions by assessing site compatibility with others facing similar hazards. Supporting advanced research, it facilitates informed decision-making, enhanced monitoring, and tailored preservation strategies, contributing to the long-term protection of CH sites.
The island of Ventotene, part of the Pontine Archipelago, is home to the remains of an imperial Roman villa at Punta Eolo promontory, a site threatened by severe hydro-geological risks. Since 2023, this site has been a key focus of the EU-H2020 TRIQUETRA Project, which aims to analyze and mitigate environmental threats to cultural heritage. The research integrates geological and archaeological investigations, combining high-resolution photogrammetry, geophysical surveys, and material analysis to assess the site's conditions. Geological studies reveal a complex stratigraphy of lava and tuff formations, influencing coastal erosion and landslides, which endanger both the site and its historical structures. Archaeological surveys reassess the villa's architectural evolution, identifying multiple construction phases and previously undocumented features. The project also evaluates material degradation, particularly in wall plasters, frescoes, and pavements, correlating deterioration patterns with environmental stressors. Through GIS-based mapping and laboratory testing of building materials, the study aims to develop tailored conservation strategies, ensuring the long-term safeguarding of this invaluable site. The TRIQUETRA Project represents a multidisciplinary effort to establish conservation models applicable to other heritage sites facing similar geological and climatic challenges.
In central Greece, in today's Fthiotis, where in antiquity Phokis bordered eastern Lokris and Boeotia, there is a sanctuary that is one of the most important of ancient Phokis. Systematic excavations were carried out in the sanctuary in the second half of the 20th century, under the direction of the German Archaeological Institute and they continue to this day. As part of the TRIQUETRA programme an integrated methodological model to protect archaeological remains at Kalapodi from frost is proposed. The TRIQUETRA project (EU HE research and innovation programme under GA No. 101094818) aims at creating an evidence-based assessment platform that allows precise risk stratification, and also creates a database of available mitigation measures and strategies, acting as a Decision Support Tool towards efficient risk mitigation and site remediation (IoANNIDIS et alii, 2024). This paper will present climatic data of Kalapodi together with materials analysis of the building materials of the sanctuary highlighting the frost problem.
Cultural heritage sites constitute irreplaceable records of human history, illustrating the progression of our social, architectural, and cultural practices. Increasing threats from climate-related hazards, such as shifting rainfall patterns, escalating temperatures, and intensified extreme weather, combined with geological and physical risks like landslides, earthquakes, and erosion, render these sites increasingly vulnerable. Earth observation technology is pivotal in preserving cultural heritage by improving documentation, enabling more effective monitoring, and supporting proactive conservation strategies. Recently, with advances in technology, advanced 3D scanning and imaging techniques, such as laser scanning and photogrammetry, have captured precise digital records of cultural heritage sites, documenting and helping conservators measure changes over time and swiftly identify structural vulnerabilities. Remote sensing technologies, including satellite imagery, aerial photography and UAV-based surveys, allow for extensive site evaluations, reducing risks and costs associated with onsite inspections, especially in remote or hazardous locations. Methodological frameworks and technological developments, encompassing remote sensing, satellite and aerial imaging, digital modeling with laser scanners, photogrammetry, and participatory data collection, are creating fresh opportunities for proactive, evidence-based conservation. Data-driven tools such as sensor arrays and digital twin models enable continuous monitoring, where real-time structural and environmental information is integrated into predictive models to anticipate emerging threats. This paper provides a comprehensive review of innovative remote sensing methods for safeguarding and monitoring cultural heritage under these compounded vulnerabilities. It focuses on integrating techniques employing remote sensing, geodetic methodologies, synthetic aperture radar, unmanned aerial vehicles (UAVs), digital twin platforms, and participatory data collection initiatives with sensors and crowdsourcing. A key emphasis of this study is the integration of state-of-the-art techniques for monitoring cultural heritage assets. Examples of various studies conducted in Cyprus, more specifically the case study of the Neolithic UNESCO World Heritage Site of Choirokoitia, demonstrate the practical application of these frameworks, highlighting the TRIQUETRA project (funded by the EU Horizon Europe research and innovation programme) with an innovative integration of conventional and novel methodologies for risk quantification, site monitoring, and stakeholder participation. The findings underscore the critical necessity of interdisciplinary collaboration, sustained funding mechanisms, and robust policy support to ensure the long-term preservation of cultural heritage for future generations.
The archaeological site of Aegina Kolonna, a prominent cultural heritage landmark in Greece, is increasingly threatened by geological hazards, including coastal erosion, seismic activity, and slope instabilities. The progressive retreat of the calcarenite sea cliffs has already led to the loss of unexcavated historical remains, posing a severe risk to the site's longterm preservation. Within the framework of the TRIQUETRA (Toolbox for assessing and mitigating Climate Change risks and natural hazards threatening cultural heritage) European Project, an interdisciplinary approach that integrates engineering-geological, geophysical, and archaeological investigations has been adopted to assess site vulnerability and implement targeted mitigation strategies. A comprehensive geological survey identified the primary factors driving cliff instability, while ambient seismic noise measurements helped characterize the subsurface conditions and assess local seismic amplification effects. The structural stability of key archaeological elements, such as the last standing column of the Apollo Temple, was also evaluated, revealing resonance frequencies in the range 5-8 Hz, which may influence its seismic vulnerability. In addition to hazard assessment, the TRIQUETRA project focuses on heritage conservation of this site, particularly of the northeastern prehistoric settlement. Past restoration efforts relied on cement-based mortars, which have deteriorated over time, leading to structural instability. To address this, new interventions include detailed documentation, the replacement of degraded materials with lime-based mortars, partial backfilling to stabilize exposed foundations, and the implementation of long-term monitoring strategies. These measures aim to enhance structural resilience while adhering to international heritage conservation guidelines. This study underscores the necessity of a holistic approach to cultural heritage management, demonstrating how scientific research and restoration practices can be integrated to mitigate geological and environmental risks and ensure the sustainable preservation of archaeological sites.
The dominant geotechnical challenge in deltaic zones stems from subsidence because water-saturated soft soils lead to structural failures in buildings. ERT and Seismic Refraction methods were utilized to identify sub-surface conditions at Niger Delta University Amassoma after one of its buildings collapsed into the ground. The sub-surface contains three different layers of soil with resistivity measurements between 0.453 S2m and 145 S2m that represent different levels of moisture content and earth composition. Seismic refraction analysis shows shear wave velocities (Vs) between 128.23 m/s and 230.59 m/s in the upper 13.6 m, correlating with weak, highly compressible soils. N-value analysis demonstrates weak soil strength because N-values show an increase from 2.09 to 3.71 in the upper layers before reaching 9.97-11.43 at deeper levels which signifies improved stability. The results suggest that the collapsed/ inking building's foundation was most likely built on unstable, low-strength soils prone to settlement and failure. This study emphasizes the importance of deep foundation designs and soil improvement strategies for reducing subsidence hazards in similar conditions. The integration of ERT and seismic refraction provides a comprehensive assessment of subsurface instability, offering valuable insights for geotechnical engineering and safer construction in deltaic regions.
This study investigated the stability and sustainability of the Lowari tunnel constructed in the geologically challenging Himalayan terrain by analyzing geological parameters, deformation monitoring, and support system performance. Detailed face mapping across the 8.509 km tunnel revealed highly variable rock types, weathering conditions, and discontinuities-ranging from stable, compact granite to fractured and sheared zones requiring enhanced stabilization. Groundwater ingress in critical sections further exacerbated stability challenges. A network of 269 monitoring stations provided comprehensive deformation data, with 93% of stations recording inward displacements (0.000 m to-0.0364 m), confirming the effectiveness of the new Austrian tunneling method (NATM) in managing excavation-induced stresses. Support systems, including shotcrete (28-day compressive strength: 28.6-30.8 MPa), rock bolts (pull-out load >165 kN), wire mesh, and lattice girders, demonstrated reliable performance, ensuring structural integrity under varying geological conditions. Over-breaks-predominantly in jointed and sheared zones-emphasized the need for refined excavation techniques and real-time monitoring to mitigate avoidable instabilities. The findings underscore the adaptability of NATM, the importance of accurate geological mapping, and the effectiveness of robust support systems in ensuring tunnel stability.
Effective evaluation of regional landslide geologic disaster susceptibility can improve the timeliness and accuracy of monitoring and early warning. However, existing studies have problems such as indicator selection without considering regional variability and strong subjectivity in evaluation. In this paper, a new evaluation method of landslide disaster susceptibility is proposed by combining geodetector and hierarchical analysis method; taking the eastern part of Qinling Mountain System in Henan Province as the study area, spatial analysis, reclassification, autocorrelation analysis and statistical methods are used for quantitative analysis and evaluation. The results show that: road density and water system density are the main influencing factors, with q-values of 0.361 and 0.242, respectively; the mean value of Landslide Susceptibility Index (LSI) in the study area is 0.402, which belongs to the moderate susceptibility class as a whole, with the high-value zones mainly located in the valley and rivers and along the roads, the LSI values show significant positive spatial correlation. Therefore, this method can more accurately quantitatively evaluate the regional landslide disaster susceptibility.
