Italian journal of engineering geology and environment

North-West Passage: the Battle for Natural Resources

Gabriele Scarascia Mugnozza — Mon, 29 Dec 2025 00:00:00 +0000

Development of a flood sensitivity model to improve land location and urban planning using the Analytic Hierarchy Process (AHP) method and Geographic Information System (GIS): the risk of floods in the city of Tebassa (ALGERIA) as a model

Sandra Boussetti — Mon, 29 Dec 2025 00:00:00 +0000

This study applies the Analytic Hierarchy Process (AHP) and Geographic Information Systems (GIS) to assess flood vulnerability in Tébessa, 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 Tébessa.

Optimizing ornamental stone quarrying and commercial exploitation through sedimentological and UAV-based 3D modelling

Francesco Rossi, Francesco Gentili, Sergio Madonna, Salvatore Milli — Mon, 29 Dec 2025 00:00:00 +0000

Quarrying ornamental stone involves extracting lithoid materials that are often variably fractured and heterogeneous, resulting in commercial products of different qualities. This variability poses challenges in planning and managing efficient quarrying processes. Traditionally, efforts have prioritized extracting the most commercially valuable materials, often neglecting less profitable portions of the deposit. Over time, such practices can lead to safety risks and environmental issues, including complications in restoring and integrating the landscape post-extraction. Sedimentological studies in quarries, particularly facies analysis, offer valuable tools for identifying high-value lithotypes during the initial stages of site development. This supports the creation of effective mining plans that optimize resource use, enhance occupational safety, and address environmental considerations, including post-quarry landscape integration. Adopting unmanned aerial vehicles (UAVs) combined with accurate Global Navigation Satellite Systems (GNSS) surveys facilitates the development of high-resolution 3D terrain models with centimeter-level accuracy. When integrated with detailed photographic data using specialized software, these models simplify the application of facies analysis, enabling precise estimation of the location and volume of lithofacies of commercial interest. This integrated methodology improves extraction efficiency, reduces operational costs, and promotes more sustainable quarrying practices. This study demonstrates the experimental application of this approach at the Poggio la Vecchia quarry in the municipality of Manciano (Grosseto Province). Here, the Manciano Sandstone, commercially known as “Pietra Santafiora,” is extracted, showcasing the potential for sustainable resource exploitation through advanced sedimentological and modeling techniques.

Vegetation filtering in photogrammetric 3D point cloud data by tree-based machine learning models: a comparative study

Mon, 29 Dec 2025 00:00:00 +0000

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.

Geomorphological evolution in a highly dynamic context as a key factor in cultural heritage: the case of Civita di Bagnoregio (Central Italy)

Giovanni Maria Di Buduo — Mon, 29 Dec 2025 00:00:00 +0000

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.

Geostatistical analysis of the spatial distribution of environmental data: a survey on methods and applications

Sander Kovaci, Alfred Lako — Mon, 29 Dec 2025 00:00:00 +0000

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 peerreviewed 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.

Reassessing climate variability through geological time: implications for environmental management and hazard mitigation

Paul P. A. Mazza — Mon, 29 Dec 2025 00:00:00 +0000

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 temperatewarm 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.

A geochemical study on the usability of wells water south Sinjar Mountain, Northern Iraq

Kotayba T. Al-Youzbakey, Basma A. M. Al-Jawadi — Mon, 29 Dec 2025 00:00:00 +0000

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 mm/year on average. Chemical analyses (Ca2+, Mg2+, Na+, K+, 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.

A hybrid machine learning model for disaster prediction using historical geological disaster data

Qin Guan, Bin Wang, Fengzuo Guo, Zhenlong Xue, Yuqi Wang — Mon, 29 Dec 2025 00:00:00 +0000

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.