Search Results (191)

Rainwater harvesting for sustainable agriculture (RWHSA) offers a viable and eco-friendly strategy to alleviate water scarcity in semi-arid regions, particularly for agricultural use. This study aims to identify optimal sites for implementing RWH systems in northern Iraq to enhance water availability and promote sustainable farming practices. An integrated geospatial approach was adopted, combining Remote Sensing (RS), Geographic Information Systems (GIS), and Multi-Criteria Decision Analysis (MCDA). Key thematic layers, including soil type, land use/land cover, slope, and drainage density were processed in a GIS environment to model runoff potential. The Soil Conservation Service Curve Number (SCS-CN) method was used to estimate surface runoff. Criteria were weighted using the Analytical Hierarchy Process (AHP), enabling a structured and consistent evaluation of site suitability. The resulting suitability map classifies the region into four categories: very high suitability (10.2%), high (26.6%), moderate (40.4%), and low (22.8%). The integration of RS, GIS, AHP, and MCDA proved effective for strategic RWH site selection, supporting cost-efficient, sustainable, and data-driven agricultural planning in water-stressed environments. Full article

Landslide susceptibility modelling represents an efficient approach to enhance disaster management and mitigation strategies. The focus of this paper lies in the development of a landslide susceptibility evaluation in northwestern Tunisia using the Analytical Hierarchy Process (AHP) and Artificial Neural Network (ANN) approaches. The used database covers 286 landslides, including ten landslide factor maps: rainfall, slope, aspect, topographic roughness index, lithology, land use and land cover, distance from streams, drainage density, lineament density, and distance from roads. The AHP and ANN approaches were applied to classify the factors by analyzing the correlation relationship between landslide distribution and the significance of associated factors. The Landslide Susceptibility Index result reveals five susceptible zones organized from very low to very high risk, where the zones with the highest risks are associated with the combination of extreme amounts of rainfall and steep slope. The performance of the models was confirmed utilizing the area under the Relative Operating Characteristic (ROC) curves. The computed ROC curve (AUC) values (0.720 for ANN and 0.651 for AHP) convey the advantage of the ANN method compared to the AHP method. The overlay of the landslide inventory data locations of historical landslides and susceptibility maps shows the concordance of the results, which is in favor of the established model reliability. Full article

Xi’an, China, has a complex geological environment, with geological hazards seriously hindering urban development and safety. This study analyzed the conditions leading to disaster formation and screened 12 evaluation factors (e.g., slope and slope direction) using Spearman’s correlation. Furthermore, it also introduced an innovative combined weighting method, integrating subjective weights from the hierarchical analysis method and objective weights from the entropy method, as well as an information value model for susceptibility assessment. The main results are as follows: (1) There are 787 hazard points—landslides/collapses are concentrated in loess areas and Qinling foothills, while subsidence/fissures are concentrated in plains. (2) The combined weighting method effectively overcame the limitations of single methods. (3) Validation using hazard density and ROC curves showed that the combined weighting information value model achieved the highest accuracy (AUC = 0.872). (4) The model was applied to classify the disaster susceptibility of Xi’an into high (12.31%), medium (18.68%), low (7.88%), and non-susceptible (61.14%) zones. The results are consistent with the actual distribution of disasters, thus providing a scientific basis for disaster prevention. Full article

Alluvial fan scree deposits (AFSDs) in arid/semi-arid regions are highly susceptible to rainfall-induced erosion, posing significant risks to infrastructure like oil pipelines. This study evaluates the efficacy of SH polymer materials in enhancing AFSD erosion resistance through three experimental approaches: film characterization, rainfall erosion simulation, and environmental compatibility assessment. Tensile tests demonstrated that SH polymer films (0.16–0.56 mm thick) retained >80% mass after prolonged immersion, exhibiting prolonged ductility (250 mm elongation) and stable post-immersion softening, ideal for enduring cyclic erosion. Rainfall simulations (200 mm/h intensity) revealed that SH application rates ≥ 1.5 kg/m² reduced soil loss by >90%, with 2.0 kg/m² ensuring near-complete slope integrity across planar/curved morphologies. Ecological tests confirmed SH’s environmental friendliness, as treated soils supported robust tall fescue growth without permeability inhibition. The findings advocate SH polymers as a sustainable solution for AFSD stabilization, combining mechanical resilience, terrain adaptability, and eco-compatibility. Full article

Microscopic pore–throat structures, known for their complexity and heterogeneity, significantly influence the characteristics of tight sandstone reservoirs. Despite the advances in geological research, studies leveraging fractal theory to elucidate differences across pore scales are limited, and conventional testing methods often fail to effectively characterize these complex structures. This gap poses substantial challenges for the exploration and evaluation of tight oil reservoirs, highlighting the need for refined analytical approaches. This study addresses these challenges by applying fractal analysis to the pore–throat structures of the Triassic Yanchang Formation tight sandstones in the Wuqi Area of the Ordos Basin. Employing a combination of experimental techniques—including pore-casted thin sections, scanning electron microscopy, high-pressure mercury intrusion, constant-rate mercury intrusion, and nuclear magnetic resonance (NMR)—this study analyzes the fractal dimensions of pore–throats. Findings reveal that tight sandstone reservoirs are predominantly composed of micron-scale pore–throats, displaying complex configurations and pronounced heterogeneity. Fractal curves feature distinct inflection points, effectively categorizing the pore–throats into large and small scales based on their mercury intrusion pressures. By linearly fitting slopes of fractal curves, we calculate variable fractal dimensions across these scales. Notably, NMR-derived fractal dimensions exhibit a two-segment distribution; smaller-scale pore–throats show less heterogeneity and spatial deformation, resulting in lower fractal dimensions, while larger-scale pore–throats, associated with extensive storage capacity and significant deformation, display higher fractal dimensions. Full article

The present study examines the application of four machine learning models—Multi-Layer Perceptron, Naive Bayes, Credal Decision Trees, and Random Forests—to assess landslide susceptibility using Mei County, China, as a case study. Aerial photographs and field survey data were integrated into a GIS system to develop a landslide inventory map. Additionally, 16 landslide conditioning factors were collected and processed, including elevation, Normalized Difference Vegetation Index, precipitation, terrain, land use, lithology, slope, aspect, stream power index, topographic wetness index, sediment transport index, plan curvature, profile curvature, and distance to roads. From the landslide inventory, 87 landslides were identified, along with an equal number of randomly selected non-landslide locations. These data points, combined with the conditioning factors, formed a spatial dataset for our landslide analysis. To implement the proposed methodological approach, the dataset was divided into two subsets: 70% formed the training subset and 30% formed the testing subset. A correlation analysis was conducted to examine the relationship between the conditioning factors and landslide occurrence, and the certainty factor method was applied to assess their influence. Beyond model comparison, the central focus of this research is the optimization of machine learning parameters to enhance prediction reliability and spatial accuracy. The results show that the Random Forests and Multi-Layer Perceptron models provided superior predictive capability, offering detailed and actionable landslide susceptibility maps. Specifically, the area under the receiver operating characteristic curve and other statistical indicators were calculated to assess the models’ predictive accuracy. By producing high-resolution susceptibility maps tailored to local geomorphological conditions, this work supports more informed land-use planning, infrastructure development, and early warning systems in landslide-prone areas. The findings also contribute to the growing body of research on artificial intelligence-driven natural hazard assessment, offering a replicable framework for integrating machine learning in geospatial risk analysis and environmental decision-making. Full article

Objective: This study aimed to assess the diagnostic value of contrast-enhanced ultrasound (CEUS) time–intensity curve (TIC) parameters in detecting non-ossified thyroid cartilage invasion in patients with laryngeal squamous cell carcinoma (SCC). Methods: A CEUS TIC analysis was performed on 32 cases from 27 patients with histologically confirmed laryngeal SCC. The diagnostic performance of time to peak (TTP), peak intensity (PI), wash-in slope (WIS), area under the curve (AUC), and their quantitative differences (∆TTP, ∆PI, ∆WIS, and ∆AUC) to discriminate between the invaded and the non-invaded non-ossified thyroid cartilage was determined using ROC analysis. A logistic regression analysis was employed to identify significant predictors. Results: In an ROC analysis, of all TIC parameters analyzed separately, ∆TTP showed the greatest diagnostic performance (AUC: 0.85). A ∆TTP cut-off of ≤ 8.9 s differentiated between the invaded and the non-invaded non-ossified thyroid cartilage with a sensitivity of 100%, specificity of 76.9%, and accuracy of 81.3%. A combination of ∆TTP and PI increased the AUC to 0.93, specificity to 100%, and accuracy to 96.8%, but reduced the sensitivity to 83.3%. Meanwhile, the visual assessment of enhancement on CEUS to detect cartilage invasion had 83.3% sensitivity and 84.6% specificity. In a univariate logistic regression, only ∆TTP was a significant predictor of non-ossified thyroid cartilage invasion (OR: 0.80; 95% CI: 0.64–1.00). For every second increase in ∆TTP, the probability of thyroid cartilage invasion decreased by 20%. Conclusions: CEUS TIC parameters, particularly a combination of ∆TTP and PI, showed high diagnostic performance in the detection of non-ossified thyroid cartilage invasion in laryngeal SCC. Full article

Flash floods rank among the most devastating natural hazards, causing widespread socio-economic, environmental, and infrastructural damage globally. Hence, innovative management approaches are required to mitigate their increasing frequency and intensity, driven by factors such as climate change and urbanization. Accordingly, this study introduced an integrated flood assessment approach (IFAA) for sustainable management of flood risks by integrating the analytical hierarchy process-weighted linear combination (AHP-WLC) and fuzzy-ordered weighted averaging (FOWA) methods. The IFAA was applied in South Khorasan Province, Iran, an arid and flood-prone region. Fifteen controlling factors, including rainfall (RF), slope (SL), land use/land cover (LU/LC), and distance to rivers (DTR), were processed using the collected data. The AHP-WLC method classified the region into flood susceptibility zones: very low (10.23%), low (23.14%), moderate (29.61%), high (17.54%), and very high (19.48%). The FOWA technique ensured these findings by introducing optimistic and pessimistic fuzzy scenarios of flood risk. The most extreme scenario indicated that 98.79% of the area was highly sensitive to flooding, while less than 5% was deemed low-risk under conservative scenarios. Validation of the IFAA approach demonstrated its reliability, with the AHP-WLC method achieving an area under curve (AUC) of 0.83 and an average accuracy of ~75% across all fuzzy scenarios. Findings revealed elevated flood dangers in densely populated and industrialized areas, particularly in the northern and southern regions, which were influenced by proximity to rivers. Therefore, the study also addressed challenges linked to sustainable development goals (SDGs), particularly SDG 13 (climate action), proposing adaptive strategies to meet 60% of its targets. This research can offer a scalable framework for flood risk management, providing actionable insights for hydrologically vulnerable regions worldwide. Full article

The rapid development of urban transportation renovation and transportation networks in China has driven the construction of an increasing number of large-span, large cross-section tunnels under sensitive environments, such as airport runways, critical infrastructure, and high-speed railways. These projects often require strict settlement control within a millimeter-level tolerance range, thus theoretical methods and key technologies for micro-settlement control have been developed. This study first derives a calculation formula for surface settlement associated with large cross-section tunnels and elucidates its correlations with factors such as pipe-roof stiffness, support system stiffness, pipe-roof construction procedures, and groundwater level changes. Theoretical approaches for controlling micro-settlement are introduced, including increasing pipe-roof stiffness, reinforcing the support system, mitigating group pipe effects, maintaining pressure and reducing resistance around the pipe, and controlling groundwater levels. A method is proposed for determining the appropriate stiffness of the pipe roof and support system. The stiffness should be selected from the transition segment between the steep decline and the gentle slope on the stiffness-settlement curves of the pipe roof and the support system. If the stiffness of the pipe roof and primary support combined with temporary support fails to meet the micro-settlement control requirements, an integrated support system with greater stiffness can be adopted. A reasonable pressure-regulating grouting technique for maintaining pressure and reducing resistance around the pipe is proposed. It is recommended that the spacing for simultaneous jacking of pipes be greater than half the width of the settlement trough. For over-consolidation-sensitive strata such as medium or coarse sands, water-blocking measures, including freezing, grouting, or a combination of both, are recommended. For over-consolidation-insensitive strata like gravels and cobbles with strong permeability, water-blocking treatments are generally unnecessary. The proposed theoretical approaches have been successfully implemented in projects such as the tunnel beneath Beijing Capital Airport runways and Taiyuan Railway Station, demonstrating their reliability. The research findings provide valuable insights into surface micro-settlement control for similar projects. Full article

The Longnan mountainous area, characterized by its complex geological structure and fragile geological environment, is one of the four major regions in China prone to geological disasters. Previous studies have employed traditional evaluation methods to assess landslide susceptibility in the Longnan mountainous area. However, these traditional methods are often subjective, and their accuracy and efficiency are difficult to guarantee. This study, supported by GIS technology, focuses on Wen County in Longnan City, a region frequently affected by landslide disasters. Based on 260 collected landslide disaster points, the study combines the ReliefF model to evaluate and zone landslide susceptibility in Wen County, Longnan City, based on feature contribution values. The lithology and rainfall factors have significant impacts on geological disasters, respectively. Areas along rivers and roads, with loose soil, heavy rainfall, steep slopes, and dense vegetation, are more prone to landslide disasters due to the combined effects of natural factors and human activities. This study also uses the receiver operating characteristic (ROC) curve to validate the accuracy of the evaluation results. The area under the curve (AUC) for the ReliefF feature fusion method is 0.853, which is higher than the 0.838 obtained from the information value method. The ReliefF method demonstrates excellent performance in landslide susceptibility evaluation, offering better predictive capability at a lower computational cost, thus achieving a balance between accuracy and efficiency. This approach can provide valuable references for rapid decision-making by relevant geological disaster prevention and management departments. Full article

Acoustic temperature measurement (ATM) and sound source localization (SSL) are two important applications of acoustic sensors. The development of novel acoustic sensors capable of both ATM and SSL is an innovative research topic with great interest. In this work, an acoustic Fabry-Perot resonance detector (AFPRD) and its cross-shaped array were designed and fabricated, and the passive ATM function of the AFPRD and the SSL capability of the AFPRD array were simulated and experimentally verified. The AFPRD consists of an acoustic waveguide and a microphone with its head inserted into the waveguide, which can significantly enhance the microphone’s sensitivity via the FP resonance effect. As a result, the frequency response curve of AFPRD can be easily measured using weak ambient white noise. Based on the measured frequency response curve, the linear relationship between the resonant frequency and the resonant mode order of the AFPRD can be determined, the slope of which can be used to calculate the ambient sound velocity and air temperature. The AFPRD array was prepared by using four bent acoustic waveguides to expand the array aperture, which combined with the multiple signal classification (MUSIC) algorithm can be used for distant multi-target localization. The SSL accuracy can be improved by substituting the sound speed measured in real time into the MUSIC algorithm. The AFPRD’s passive ATM function was verified in an anechoic room with white noise as low as 17 dB, and the ATM accuracy reached 0.4 °C. The SSL function of the AFPRD array was demonstrated in the outdoor environment, and the SSL error of the acoustic target with a sound pressure of 35 mPa was less than 1.2°. The findings open up a new avenue for the development of multifunctional acoustic detection devices and systems. Full article

Prunus microcarpa is an endemic species prevalent throughout the highlands of the Kurdistan Region of Iraq. Conservation, introduction, and restoration efforts require an in-depth understanding of the species’ current and future habitat distributions under different climate change scenarios. This study utilized field observations, species distribution modeling, geospatial techniques, and environmental predictors to analyze the distribution and forecast potential habitats for P. microcarpa in the highlands of Iraq. Findings indicate that, according to the global climate models (i.e., BCC-CSM2-MR and MRI-ESM2.0), the reduction in habitat for the species is projected to be more than the potential expansion. Specifically, the area of habitat is expected to reduce by 2351.908 km² (4.6%) and 2216.957 km² (4.3%), while it could increase by 1306.384 km² (2.5%) and 1015.612 km² (2.0%) for the respective climate models. Topographic features such as elevation and slope, climatic conditions, precipitation seasonality, and annual mean temperature relatively shape the distribution of P. microcarpa. The modeling demonstrated good predictive capability (area under the curve (AUC) score = 0.933). The total study area is approximately 51,558.327 km², with around 20.5% (10,602 km²) identified as suitable habitat for P. microcarpa. These findings offer essential baseline information for conservation strategies and provide new insights into where the species currently resides and where it could be found in the future. This underscores how combining distribution modeling with geospatial techniques can be effective, particularly in data-deficient regions like Iraq. Full article

The challenge of obtaining landslide susceptibility zoning in Tibet is compounded by the high altitude, extensive range, and difficult exploration of the region. To address this issue, a novel evaluation approach based on Stacking ensemble machine learning is proposed. This study focuses on Jiacha County, adopts the slope unit as the evaluation unit, and picks up 14 evaluation factors that symbolize the topography and geomorphology, environmental and hydrological features, and basic geological features. These landslide conditioning factors were integrated into a total of 4660 Stacking ensemble learning models, randomly combined by 10 base-algorithms, including AdaBoost, Decision Tree (DT), Gradient Boosting Decision Tree (GBDT), k-Nearest Neighbors (kNNs), LightGBM, Multilayer Perceptron (MLP), Random Forest (RF), Ridge Regression, Support Vector Machine (SVM), and XGBoost. All models were trained, using the natural discontinuity method to classify landslide susceptibility, and the AUC value, the area under the ROC curve, was taken to evaluate the model. The results show that the maximum AUC values in the 9 models performing better reach 0.78 and 0.99 over the test set and the train set. Most of the areas identified as high susceptibility and above show consistency with the interpretation of the existing geological field data. Thus, the Stacking ensemble method is applicable to the landslide susceptibility situation in Jiacha County, Tibet, and can provide theoretical support for disaster prevention and mitigation work in the Qinghai–Tibet Plateau area. Full article

Geological hazards, particularly in mountainous regions, represent significant threats to life, property, and the environment. In this study, we focus on Luoping County, Yunnan Province, China, employing SBAS-InSAR technology to monitor surface deformation between 8 October 2022 and 27 September 2024. By integrating InSAR deformation data with 10 static disaster-causing factors, including elevation, slope, aspect, curvature, distance to faults, distance to rivers, distance to roads, engineering geological rock groups, geomorphological types, and the NDVI, geological hazard susceptibility was assessed using the information value (IV) model and the information value–random forest (IV-RF) coupled model. Accuracy validation using ROC curves indicated that the IV-RF model, integrated with InSAR deformation data, achieved the highest accuracy, with an AUC value of 0.805. Based on the susceptibility evaluation, rainfall intensity was introduced as a triggering factor to assess geological hazard risks under four rainfall conditions: 10-year, 20-year, 50-year, and 100-year return periods. The results demonstrated that incorporating InSAR deformation data significantly improved disaster prediction accuracy, providing more reliable and sustainable risk assessment outcomes. This study underscores the critical role of InSAR technology, combined with rainfall conditions, in enhancing the precision of geological hazard risk assessments, offering a scientific basis for disaster prevention and mitigation strategies in Luoping County and similar regions. Full article

The Guangxi Zhuang Autonomous Region, a vital strategic geographic entity in southern China, is prone to frequent road collapse disasters due to its complex topography and high rainfall, severely affecting regional economic and social development. Existing risk assessments for these collapse disasters often lack comprehensive analysis of the combined influence of multiple factors, and their predictive accuracy requires enhancement. To address these deficiencies, this study utilized the ResNet18 model, a convolutional neural network (CNN)-based approach, integrating 10 critical factors—including slope gradient, lithology, and precipitation—to develop a risk assessment model for road collapse disasters. This model predicts and maps the spatial distribution of collapse risk across Guangxi. The results reveal that very high-risk areas span 49,218.94 km², constituting 20.38% of Guangxi’s total area, with a disaster point density of 8.67 per 100 km²; high-risk areas cover 56,543.87 km², representing 23.41%, with a density of 3.38 per 100 km²; and low-risk areas encompass 61,750.69 km², accounting for 25.57%, with a density of 0.29 per 100 km². The receiver operating characteristic (ROC) curve yields an area under the curve (AUC) value of 0.7879, confirming the model’s high reliability and predictive accuracy in assessing collapse risk. This study establishes a scientific foundation for the prevention and mitigation of road collapse disasters in Guangxi and offers valuable guidance for risk assessments in similar regions. Full article