Landslides pose significant risks to human life and infrastructure, particularly in mountainous regions like Inje, South Korea. This study aims to develop detailed landslide susceptibility maps (LSMs) using statistical (i.e., Frequency Ratio (FR), Logistic Regression (LR)) models and a hybrid integrated approach. These models incorporated various factors influencing landslides, including aspect, elevation, rainfall, slope, soil depth, slope length, and landform, derived from comprehensive geospatial datasets. The FR method assesses the likelihood of landslides based on historical occurrences relative to specific factor classes, while the LR method predicts landslide susceptibility through the statistical modeling of multiple predictor variables. The results from the FR, LR, and hybrid methods showed that the cumulative area covered by high and very high landslide susceptibility zones was 13.8%, 13.0%, and 14.28%, respectively. The results were validated using Receiver Operating Characteristic (ROC) curves and the Area Under the Curve (AUC), revealing AUC values of 0.83 for FR, 0.86 for LR, and 0.864 for the hybrid method, indicating high predictive accuracy. Subsequently, we used K-mean clustering algorithms on the hybrid LSI to identify the higher LSI cluster of the region. Furthermore, sensitivity analysis based on landslide density confirmed that all methods accurately identified high-risk areas. The resulting LSMs provide critical insights for land-use planning, infrastructure development, and disaster risk management, enhancing predictive accuracy and aiding in the prevention of future landslide damage.

Assessing environmental impacts and prioritizing projects that minimize ecological harm is essential, especially in regions characterized by diverse climates and geographical features. This study presents a two-phase methodology aimed at optimizing environmental parameter coefficients for asphalt paving projects undertaken by municipalities in Iran. In the first phase, the Genetic Optimization Algorithm is employed to identify, categorize, and cluster coefficients associated with key environmental parameters. The second phase involves the development of a comprehensive environmental index that ranks proposed projects based on the derived coefficients, providing a systematic approach to environmentally conscious decision-making. The results indicate that water resource pollution is the most critical concern prior to project implementation, with a coefficient of 3.59. During the implementation phase, noise pollution emerges as the most significant factor (coefficient 5.89), while ecosystem damage is most pronounced during land use changes (coefficient 5.25). Soil pollution (coefficient 5.81) and local climate damage (coefficient 5.67) are dominant during the maintenance and operational phases, respectively. These findings provide practical insights for prioritizing road infrastructure projects, benefiting both urban and rural planning efforts.

Moisture-driven landslides (MDL) are typically associated with elevated soil moisture content and sub-surface pore-water pressure due to temporal clustering of moderate to extreme precipitation over steep terrain leading to mass wasting phenomena such as rock, soil, and debris flows downward along the slope. With their quick response times and short recovery periods, these cascading hazard events are widespread in tectonically active regions, such as the Himalayas, damaging the natural and built environment systems. Due to climate and land use changes, the number of MDLs, including the mountainous Himalayas, is increasing globally. This study first uses Ripley's L-function to compare the spatial clustering of MDLs between two non-overlapping time windows 2007-2015 versus 2016-2022, assuming spatial point process information follows Poisson distribution across the Uttarakhand state (latitude: 28 degrees 42' N - 31 degrees 28' N; longitude: 77 degrees 35'E - 81 degrees 05' E), one of the most landslide-prone areas in the western Himalayas. Then, we investigate the potential physical controls of landslides by considering ranges of conditioning drivers, such as extreme rainfall indices, catchment and soil attributes. While we find evidence of marked spatial clustering of MDLs up to 80 km radial distance, which is more pronounced during the first half (2007-2015) of the time window compared to the latter half (2016-2022), we show that topographic factors contribute significantly to such events with a median contribution of 55% (range 33-60%), followed by the soil properties, and meteorological indices with median contributions lies in the tune of 20-22%. Among topographic factors, slope, form factor, stream power index, and drainage density significantly trigger MDLs. Whereas, soil factors such as cation exchange capacity and soil organic carbon content were identified as the significant factors to mediate landslides. Among meteorological drivers, the number of days with rainfall over 20 mm shows the highest confidence in triggering landslides, followed by the accumulated rainfall of more than 99th percentiles emerging as key conditioning drivers for MDLs. Understanding the spatial dynamics of landslides and their potential drivers enables stakeholders to develop early warning systems, adaptation, and planning, enhancing climate resilience in landslide-prone areas.

Soil salinity is one of the most challenging environmental factors affecting rice productivity, particularly in regions with high saline soils such as Egypt. The ability of rice to maintain high yield and quality under saline stress is often limited, leading to significant reductions in productivity. With the increasing salinization of agricultural lands, finding effective agronomic practices and treatments to mitigate salt-induced damage in rice crops is critical for ensuring food security. This study investigates the potential of exogenous glycine betaine (GB) and proline (Pro) applications to mitigate the adverse effects of salt stress on rice (cv. Sakha 108) over two consecutive growing seasons (2021-2022). Treatments of 30 mM GB and 30 mM Pro significantly enhanced dry weight (162.2 and 169.7 g in 2021 and 2022, respectively), plant height (88.94 and 99.00 cm), tiller number (10.58 and 10.33), and grain yield (4.22 and 4.30 t/ha) compared to control groups. Combined treatments of 30 mM GB and 30 mM Pro exhibited the greatest improvements across both years, with maximum dry weight (193.44 and 186.56 g), plant height (112.00 and 112.33 cm), tiller number (15.33 and 16.28), spikelet number per meter (264.00 and 264.05), thousand-kernel weight (70.00 and 73.2 g), and grain yield (6.17 and 6.64 t/ha). Additionally, the combined treatments resulted in the highest harvest index (53.22% in 2021 and 48.94% in 2022), amylose content (24.24% and 20.09%), and protein content (12.33% and 12.00%). Correlation analysis highlighted strong positive relationships among traits, such as plant height with grain yield (r = 0.94), biomass yield (r = 0.92), and harvest index (r = 0.90). Path analysis further demonstrated that thousand-kernel weight and biomass yield had the most significant direct effects on grain yield, with values of 0.43 and 0.42, respectively. Heatmap clustering and principal component analysis (PCA) confirmed the synergistic effects of combined GB and Pro treatments, with the 30P_30GB treatment consistently clustering with high-yield traits, enhancing nitrogen use efficiency and stress resilience. In conclusion, the combined application of glycine betaine and proline significantly enhances the agronomic and chemical traits of rice under salt stress. This study demonstrates that these osmoprotectants improve vegetative growth, grain yield, and quality, with synergistic effects observed at optimal concentrations. The findings highlight the potential of glycine betaine and proline as effective tools for improving salt tolerance in rice, offering practical solutions to address challenges in saline-affected agricultural regions.

In this paper, we investigate the evolution characteristics of floor failure during pressured mining in extra-thick coal seams. A mechanical expression relating floor failure depth to seam thickness is established based on soil mechanics and mine pressure theory. The findings reveal a linear relationship between seam thickness and floor failure depth; specifically, as the coal seam thickens, the depth of floor failure increases. To simulate the mining process of extra-thick coal seams, FLAC3D numerical simulation software is utilized. We analyze the failure process, failure depth, and the behavior of water barriers at the coal seam floor under the influence of extra-thick coal seam mining from three perspectives: rock displacement evolution in the floor, stress evolution in the floor, and plastic deformation. Based on geological characteristics observed in the Longwanggou mine field, we establish a main control index system for assessing floor water-inrush risk. This system comprises 11 primary control factors: water abundance, permeability, water pressure, complexity of geological structure, structural inter points, thickness of both actual and equivalent water barriers, thickness ratio of brittle-plastic rocks to coal seams, as well as depths related to both coal seams and instances of floor failure. Furthermore, drawing upon grey system theory and fuzzy mathematics within uncertainty mathematics frameworks leads us to propose an innovative approach-the interval grey optimal clustering model-designed specifically for risk assessment concerning potential floor water inrush during pressured mining operations involving extra-thick coal seams. This method of mine water inrush risk assessment is applicable for popularization and implementation in mines with analogous conditions, and it holds practical significance for the prevention of mine water damage.

Nowadays, more and more attention is being paid to environmental issues due to the development of road transportation, particularly the construction of arterial roads. Despite the existence of diverse methods to determine convenient criteria for their assessment, determining the projects with the least harmful effects on the environment and ranking them for purposes of budget allocation and prioritization are remarkably important. The case is more highlighted in regions where roads go through diverse areas with different climatic and geographical distributions. In the present study, a new method consisting of two phases was proposed to determine the optimal coefficient of environmental parameters in road construction parameters. In the first phase, the Genetic Optimization Algorithm was implemented to determine convenient coefficients for the relevant parameters. During this stage, similar coefficients were clustered together. In the second phase, an environmental index for various projects was determined based on the obtained results, and the proposed projects were ranked based on that. According to the results obtained concerning environmental parameters during the pre-implementation stage, polluting water resources was the most influential parameter, with a coefficient determined at 3.59. Moreover, the most significant parameter during the implementation was noise pollution, with a coefficient of 5.89, while damaging the ecosystem was the most significant one during the stage of land use change (5.25). Furthermore, soil pollution was the most remarkable parameter during the stage of maintenance (5.81), while damaging the local climate pollution was the most important one during the stage of road implementation (5.67). The above findings can be helpful for researchers in road construction projects.

Early detection of drought stress is essential for preventing permanent plant damage and minimizing yield loss. This study utilized hyperspectral imaging at the leaf level to visualize drought stress in safflower plants ( Car- thamus tinctorius L.). Three safflower genotypes, Palenus, A82, and IL-111, were cultivated under three irrigation levels. Stress conditions were simulated by depleting 50%, 70%, and 90% of soil water content, representing unstressed (US), mild stress (MS), and severe stress (SS) conditions, respectively. Hyperspectral images of leaf samples were captured before any visible signs of water scarcity emerged. Classification analysis was performed using the full mean spectral data with partial least squares discriminant analysis, soft independent modeling of class analogy (SIMCA), support vector machines, and artificial neural network (ANN) classifiers. Feature selection methods were applied to extract the most informative wavebands, and ANN was used to build predictive models. Spatial analysis involved pixel-wise classification using both unsupervised (k-means clustering) and supervised (best classifiers) approaches. ANN outperformed other classifiers using the entire spectral data, effectively distinguishing US, MS, and SS classes in the Palenus, A82, and IL-111 genotypes, achieving F1-scores of 92.22%, 96.01%, and 96.47%, respectively. Among the feature selection methods, SIMCA-based features excelled in monitoring stress conditions in the Palenus and A82 genotypes. In supervised spatial analysis, ANN models clearly depicted the progression of stress in leaves across different genotypes. This study demonstrates the potential of hyperspectral imaging to differentiate various levels of drought stress in safflower, an important oilseed crop.

Sand porosity is an important compactness parameter that influences the mechanical properties of sand. In order to evaluate the temporal variation in sand porosity, a new method of sand porosity evaluation based on the statistics of target sand particles (which refers to particles within a specific particle size range) is presented. The relationship between sand porosity and the number of target sand particles at the soil surface considering observation depth is derived theoretically, and it is concluded that there is an inverse relationship between the two. Digital image processing and the k-means clustering method were used to distinguish particles in digital images where particles may mask each other, and a criterion for determining the number of particles was proposed, that is, the criterion of min(Dao). The execution process was implemented by self-written codes using Python (2021.3). An experiment on a simple case of Go pieces and sand samples of different porosities was conducted. The results show that the sum of the squared error (SSE) in the k-means method can converge with a small number of iterations. Furthermore, there is a minimum value between the parameter Dao and the set value of a single-particle pixel, and the pixel corresponding to this value is a reasonable value of a single-particle pixel, that is, the min(Dao) criterion is proposed. The k-means method combined with the min(Dao) criterion can analyze the number of particles in different particle size ranges with occlusion between particles. The test results of sand samples with different densities show that the method is reasonable.

Maharashtra stands out as a crucial state in India, demonstrating significant progress in infrastructural development and industrialization. Several prominent cities, including Mumbai, Pune, Nagpur, etc., are significantly contributing to the Indian economy. Considering the importance of the state, a deterministic seismic hazard analysis is executed to reduce the damages to critical and important structures and fatalities caused due to earthquakes. Past earthquakes data are collected within and around the state to prepare a homogenised earthquake catalogue. Seven seismic zones are prepared using K- mean cluster analysis. Independent earthquake events i.e., mainshocks are identified using four renowned declustering methods. Additionally, with the help of mainshocks from each zone, the maximum observed earthquake magnitude ( m(max)) and positive correction factor (Delta) are estimated. By superimposing all the m (max) mainshocks (after adding A) onto the fault map, the maximum observed possible earthquake magnitude of all faults (M-max) are assigned to each fault. M-max value is used to estimate surface rupture length (RLD) and consecutively, the maximum magnitude (M-Max) from fault sources are estimated. Three region-specific ground motion prediction equations (GMPEs) are adopted in the logic trees assigning a proper weightage to each GMPE. A seismic hazard contour maps are prepared at bedrock level, C, and D-type soil sites for Maharashtra. In the western part of the study area, the maximum PGA value is found to be 0.58 g, 0.70 g, and 0.33 g at bedrock level, C, and D-type sites, respectively.

Tree root systems are crucial for providing structural support and stability to trees. However, in urban environments, they can pose challenges due to potential conflicts with the foundations of roads and infrastructure, leading to significant damage. Therefore, there is a pressing need to investigate the subsurface tree root system architecture (RSA). Ground-penetrating radar (GPR) has emerged as a powerful tool for this purpose, offering high-resolution and nondestructive testing (NDT) capabilities. One of the primary challenges in enhancing GPR's ability to detect roots lies in accurately reconstructing the 3-D structure of complex RSAs. This challenge is exacerbated by subsurface heterogeneity and intricate interlacement of root branches, which can result in erroneous stacking of 2-D root points during 3-D reconstruction. This study introduces a novel approach using our developed wheel-based dual-polarized GPR system capable of capturing four polarimetric scattering parameters at each scan point through automated zigzag movements. A dedicated radar signal processing framework analyzes these dual-polarized signals to extract essential root parameters. These parameters are then used in an optimized slice relation clustering (OSRC) algorithm, specifically designed for improving the reconstruction of complex RSA. The efficacy of integrating root parameters derived from dual-polarized GPR signals into the OSRC algorithm is initially evaluated through simulations to assess its capability in RSA reconstruction. Subsequently, the GPR system and processing methodology are validated under real-world conditions using natural Angsana tree root systems. The findings demonstrate a promising methodology for enhancing the accurate reconstruction of intricate 3-D tree RSA structures.