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.

India's passenger traffic primarily relies on the road network for commuting. As a result, the demand for transport infrastructure has led to rapid growth in road construction across the country. California Bearing Ratio (CBR) tests measure strength of subgrade soil, which is essential for pavement design. In practice, the CBR value is often estimated through index and strength properties of soil, since it is easier as compared to the conventional time-consuming laboratory CBR testing. Over the years, a lot of efforts has been taken for developing CBR from index and strength properties correlation equations, most of which are based on regression analysis. Moreover, most of the correlation equations developed are based on a wide dataset compiled from different regions, which makes them incapable of accounting for the spatial variability of soil. This study presents a quick approach to estimate onsite CBR values using sensor acceleration data, avoiding time-consuming laboratory tests. An Arduino Uno sensor collected data for 19 locations in Dhule district, Maharashtra was used in present study. The developed CBR equations using sensor data showed a strong correlation with conventional regression equations and experimental results.

Source identification of a contaminant has always been challenging for accurately modeling groundwater transport. Source identification problems are classified into several parts, such as identifying the location of contamination, the strength of contamination, the time the contaminant is introduced into the groundwater, and the duration of its activity. Identifying the sources considering all the parts as variables increases the computational complexity. Reducing the number of variables in source identification problems is necessary for a swift solution through optimization approaches. The most challenging variable in source identification modeling is the location of contamination, as it is a discrete variable for almost all the numerical solutions of groundwater models. In this research study, we have created a methodology to narrow the location of contamination from a random distribution throughout the aquifer to a reasonable number of probable locations. Although methods to identify the location of contamination were devised earlier, we have attempted an approach of combining a particle tracking approach with Bayesian method of updating the probabilities as a novel approach, where the observation data is limited. We have considered the aquifer parameters and observation well data and devised a method with a Lagrangian approach to particle movement to identify the potential source locations. We have refined the source locations to a narrower probability distribution using the Bayesian method of updating the probability through new information of refined grid space. We have tested the models to identify the potential sources with different hypothetical problems and identified the sources in advective dominant transport with an average probability of 0.53, diffusion dominant transport with an average probability of 0.62, heterogenous soils with an average probability of 0.99, anisotropic aquifer with an average probability of 0.91, and aquifer with irregular boundary with an average probability of 0.96 to identify the location nearest to the actual contaminant source. The results are satisfactory in identifying the number of potential sources with an accuracy of 88 % (15 identified out of 17 sources with a probability greater than 0.4) and their locations in the aquifer with a probability of 0.223 for exact location identification. The probability of finding a source nearest to the actual location is 0.745 at an average distance of 11.6 m from the actual source location.

African mahogany (Khaya grandifoliola) is a tree species that has gained space in the forestry market, presenting utility in a wide range of uses, especially in Brazilian territory, where it is the main substitute for Brazilian mahogany wood. The objective of this work was to perform a path analysis between the response of nutrient solution to cadmium treatment and the other variables and attributes studied: amino acid, nitrate, protein, ammonium, reductase, IDM, cadmium. The experiment was carried out in a greenhouse. At first, seedlings were habituated to be later taken to the treatment, where the applicability was given by cadmium chloride monohydrate (CdCl 2.H2O) according to the nutrient solution. The experimental design was completely randomized (DIC), mixed in five concentrations (0; 10; 20; 30 and 40 mg L-1) with seven replications, totaling 35 experimental units. To perform the comparative examination, the data were exposed to the analysis of variance, followed by regression analysis. The path analysis allowed to directly verify that increasing doses of treatment with Cadmium (CAR) reflected in a negative correlation with the amino acid content (AAR) in the roots of mahogany plants. Abiotic stress, which was exposure to a toxic chemical (cadmium), decreased, in this case, the ability of plants to synthesize amino acids. Increasing doses of cadmium treatment (CAL) reflected in negative correlation with the protein content in the leaf (PRL) of mahogany plants. Evidently, the higher the cadmium concentration, the greater the damage to mahogany's metabolic systems. With this study, we showed that excess cadmium in the soil affects the development of seedlings of forest species such as mahogany.

The evolution of the average freezing depth and maximum freezing depth of seasonal frozen soil and their correlations with the average winter half-year temperature in Heilongjiang Province in China are analyzed. Linear regression, the Mann-Kendall test, and kriging interpolation are applied to freezing depth data from 20 observation stations in Heilongjiang Province from 1972 to 2016 and daily average temperature data from 34 national meteorological stations collected in the winters of 1972-2020. The results show that the average freezing depth decreases at a rate of 4.8 cm (10 yr)(-1) and that the maximum freezing depth decreases at a rate of 10.1 cm (10 yr)(-1). The winter half-year average temperature generally shows a fluctuating upward trend in Heilongjiang Province, increasing at a rate of 0.3 degrees C (10 yr)(-1). The correlations between the average and maximum freezing depths and the winter half-year average temperature are -0.53 and -0.49, respectively. For every 1 degrees C increase in the average temperature during the winter half of the year, the average freezing depth decreases by 3.85 cm and the maximum freezing depth decreases by 7.84 cm. The average freezing depth sequence mutated in 1987, and the maximum freezing depth sequence mutated in 1988. The average temperature in the winter half-year displayed multiple abrupt changes from 1972 to 2020. The spatial variations in the average and maximum freezing depths are basically consistent with those in the average winter half-year temperature. These research results provide a theoretical basis for the design and site selection of hydraulic structures in cold areas and for regional development and agricultural planning. Significance StatementThe freeze-thaw balance in the frozen soil environment has been disrupted in recent years, and various degrees of degradation have occurred in the frozen soil. The degradation of frozen soil will further aggravate the greenhouse effect, which in turn will affect the accumulation of water in the soil and will have a significant impact on local agricultural production. This article uses Heilongjiang Province in China as an example. The results show that 1) the temperature in the winter half-year has exhibited an upward trend in recent years, 2) the temperature in the winter half-year has a considerable impact on the frozen soil environment, and 3) the response of the spatial distribution of frozen soil to temperature changes in the winter half-year is revealed.

The Arctic is experiencing an unprecedented rate of environmental and climate change. The active layer ( the uppermost layer of soil between the atmosphere and permafrost that freezes in winter and thaws in summer) is sensitive to both climatic and environmental changes, and plays an important role in the functioning, planning, and economic activities of Arctic human and natural ecosystems. This study develops a methodology for modeling and estimating spatial-temporal variations in active layer thickness ( ALT) using data from several sites of the Circumpolar Active Layer Monitoring network, and demonstrates its use in spatial-temporal interpolation. The simplest model's stochastic component exhibits no spatial or spatio-temporal dependency and is referred to as the naive model, against which we evaluate the performance of the other models, which assume that the stochastic component exhibits either spatial or spatio-temporal dependency. The methods used to fit the models are then discussed, along with point forecasting. We compare the predicted fit of the various models at key study sites located in the North Slope of Alaska and demonstrate the advantages of space-time models through a series of error statistics such as mean squared error, mean absolute and percent deviance from observed data. We find the difference in performance between the spatio-temporal and remaining models is significant for all three error statistics. The best stochastic spatio-temporal model increases predictive accuracy, compared to the naive model, of 33.3%, 36.2% and 32.5% on average across the three error metrics at the key sites for a one-year hold out period.

Alpine cold ecosystem with permafrost environment is quite sensitive to climatic changes and the changes in permafrost can significantly affect the alpine ecosystem. The vegetation coverage, grassland biomass and soil nutrient and texture are selected to indicate the regime of alpine cold ecosystems in the Qinghai-Tibet Plateau. The interactions between alpine ecosystem and permafrost were investigated with the depth of active layer, permafrost thickness and mean annual ground temperature (MAGTs). Based on the statistics model of GPTR for MAGTs and annual air temperatures, an analysis method was developed to analyze the impacts of permafrost changes on the alpine ecosystems. Under the climate change and human engineering activities, the permafrost change and its impacts on alpine ecosystems in the permafrost region between the Kunlun Mountains and the Tanggula Range of Qinghai-Tibet Plateau are studied in this paper. The results showed that the permafrost changes have a different influence on different alpine ecosystems. With the increase in the thickness of active layer, the vegetation cover and biomass of the alpine cold meadow exhibit a significant conic reduction, the soil organic matter content of the alpine cold meadow ecosystem shows an exponential decrease, and the surface soil materials become coarse and gravelly. The alpine cold steppe ecosystem, however, seems to have a relatively weak relation to the permafrost environment. Those relationships resulted in the fact that the distribution area of alpine cold meadow decreased by 7.98% and alpine cold swamp decreased by 28.11% under the permafrost environment degradation during recent 15 years. In the future 50 years the alpine cold meadow ecosystems in different geomorphologic units may have different responses to the changes of the permafrost under different climate warming conditions, among them the alpine cold meadow and swamp ecosystem located in the low mountain and plateau area will have a relatively serious degradation. Furthermore, from the angles of grassland coverage and biological production the variation characteristics of high-cold ecosystems in different representative regions and different geomorphologic units under different climatic conditions were quantitatively assessed. In the future, adopting effective measures to protect permafrost is of vital importance to maintaining the stability of permafrost engineering and alpine cold ecosystems in the plateau.