Search Results (20)

This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (USLE) and its revised version (RUSLE) often fall short in capturing complex environmental interactions, leading to inaccurate soil loss predictions. This research introduces a novel approach using Convolutional Neural Networks (CNNs) combined with Geographic Information Systems (GISs) to improve the precision and spatial resolution of soil loss risk assessments. High-resolution satellite imagery, soil maps, and climatic data were processed to extract critical factors, such as slope, land cover, and rainfall erosivity, which were then fed into the CNN model. The findings revealed that the CNN model outperformed traditional methods, achieving a low Root Mean Square Error (RMSE) of 2.3 and an R-squared value of 0.92, significantly surpassing the USLE and RUSLE models. The resulting high-resolution soil loss maps identified high-risk erosion areas, particularly in the central and eastern regions of the watershed, with soil loss rates exceeding 40 tons/ha/year. These findings demonstrate the superior predictive capabilities of deep learning, offering valuable insights for targeted soil conservation strategies and highlighting the potential of advanced computational techniques to revolutionize environmental modeling. Full article

Soil erosion is a critical factor impacting soil health and agricultural productivity, with soil erodibility often quantified using the K-factor in erosion models such as the universal soil loss equation (USLE). Traditional K-factor estimation lacks spatiotemporal precision, particularly under varying soil moisture and land cover conditions. This study introduces modified K-factor pedotransfer functions (Kmlr) integrating dynamic remotely sensed data on land use land cover to enhance K-factor accuracy for diverse soil health management applications. The Kmlr functions from multiple approaches, including dynamic crop and cover management factor (Cdynamic), high resolution satellite data, and downscaled remotely sensed data, were evaluated across spatial and temporal scales within the Fish River watershed in Alabama, a coastal watershed with significant soil–water interactions. The results highlighted that the Kmlr model provided more accurate sediment yield (SY) predictions, particularly in agricultural areas, where traditional models overestimated erosion by upto 59.23 ton/ha. SY analysis across the 36 hydrological response units (HRUs) in the watershed showed that the Kmlr model captured more accurate soil loss estimates, especially in regions with varying land use. The modified K-factor model (Kmlr-c) using Cdynamic and high-resolution soil surface moisture data outperformed the traditional USLE K-factors in predicting SY, with a strong correlation to observed SY data (R² = 0.980 versus R² = 0.911). The total sediment yield predicted by Kmlr-c (525.11 ton/ha) was notably lower than that of USLE-based estimates (828.62 ton/ha), highlighting the overestimation in conventional models. The identification of erosive hotspots revealed that 6003 ha of land was at high erosion risk (K-factor > 0.25), with an average soil loss of 24.2 ton/ha. The categorization of erosive hotspots highlighted critical areas at high risk for erosion, underscoring the need for targeted soil conservation practices. This research underscores the improvement of remotely sensed data-based models and perfects them for the application of soil erodibility assessments thus promoting the development of such models. Full article

Background/Objectives: This study investigated patient pain descriptors for transvaginal ultrasound (TVS) diagnostic evaluation of endometriosis for uterosacral ligaments (USLs), including correlation between USL thickness and site-specific tenderness (SST). It further investigated if SST could positively assist diagnosing endometriosis on TVS. Methods: TVS images and SST pain descriptors were collected from 42 patients. SST was evaluated by applying sonopalpation during TVS. The images were presented to six observers for diagnosis based on established USL criteria. Following this, they were given the SST pain scores and asked to reevaluate their diagnosis to assess if the pain scores impacted their decision. Results: An independent t-test showed that the patients with an endometriosis history had higher pain scores overall (7.2 ± 0.59) compared to the patients with no history (0.34 ± 0.12), t (40) = 8.8673. Spearman’s correlation showed a strong correlation to the pain scale score for clinical symptoms (r = 0.74), endometriosis diagnosis (r = 0.78), USL thickness (r = 0.74), and when USL nodules were identified (r = 0.70). Paired t-tests showed that the observers demonstrated a higher ability to correctly identify endometriosis with the pain scale information (33 ± 8.83) as opposed to not having this information (29.67 ± 6.31), which was a statistically significant change of 3.33, t (5) = 2.7735. Conclusions: Patients with an endometriosis history have significantly higher pain scores on TVS compared to patients with no endometriosis history. A strong correlation was shown between SST pain scores and patient symptoms, USL thickness, and USL nodules. Inclusion of SST alongside TVS imaging shows promise, with these results demonstrating a higher ability to diagnose endometriosis with additional SST pain scale information. Full article

The Hulan River Basin is located in the black soil region of northeast China. This region is an important food-producing area and the susceptibility of black soil to erosion increases the risk of soil erosion, which is a serious environmental problem that affects agricultural productivity, water supply, and other important aspects of the region. In this paper, the changes in LULC (land use and land cover) in the basin between 2001 and 2020 were thoroughly analysed using GIS (geographic information system) and USLE (universal soil loss equation) models. The soil erosion risk in the Hulan River Basin between 2001 and 2020 was also studied and soil erosion hot spots were identified to target those that remained significant even under the implementation of soil conservation measures. Precipitation data were used to obtain the R factor distribution, LULC classification was adopted to assess the C factor distribution, soil data were employed to estimate the K factor distribution, DEM (Digital Elevation Model) data were used to generate an LS factor map, and slope and LULC data were considered to produce a P factor distribution map. These factors were based on the model parameters of the USLE. The findings of LULC change analysis over the last 20 years indicated that, while there have been nonobvious changes, agricultural land has continued to occupy the bulk of the area in the Hulan River Basin. The increase in areas used for human activities was the most notable trend. In 2001, the model-predicted soil erosion rate varied between 0 and 120 t/ha/yr, with an average of 4.63 t/ha/yr. By 2020, the estimated soil erosion rate varied between 0 and 193 t/ha/yr, with an average of 7.34 t/ha/yr. The Hulan River Basin was classified into five soil erosion risk categories. Most categories encompassed extremely low-risk levels and, over the past 20 years, the northeastern hilly regions of the basin have experienced the highest concentration of risk change areas. The northeastern hilly and mountainous regions comprised the risk change area and the regions that are most susceptible to erosion exhibited a high concentration of human production activities. In fact, the combined use of GIS and USLE modelling yielded erosion risk areas for mapping risk classes; these results could further assist local governments in improving soil conservation efforts. Full article

The calculation of the R-factor (rainfall erosivity) for implementation in soil erosion models such as USLE (Universal Soil Loss Equation) and RUSLE (Revised Universal Soil Loss Equation) encounters substantial difficulties due to the scarcity of spatial databases with adequate resolution for territorial planning actions at the local level. Otherwise, there is a spatial database available with a coarse resolution of themes that can be used to calculate the R-factor. We apply the spatial downscaling—based on the following regression models: linear (LN), general additive model (GAM), random forest (RF), cubist (CU)—to erosivity data (target variable) prepared for the State of São Paulo, Brazil, with a spatial resolution of 2500 m. We used DEM and slope data with 30 m fine resolution from the Atibaia watershed, located between the metropolitan regions of São Paulo (RMSP) and Campinas (RMC), to apply the downscaling. This framework improved the spatial resolution of the R-factor, which is necessary to calculate soil loss in the USLE and RUSLE equations in a territory where data with a fine resolution are still limited to the development of territorial planning projects at the local level. The RF model was better with R² 0.94. Full article

Soil erosion often leads to land degradation, agricultural production reduction, and environmental deterioration, which seriously restricts the sustainable development of regions. Clarifying the driving factors of soil erosion is the premise of preventing soil erosion. Given the lack of current research on the driving factors/force changes of soil erosion in different regions or under different erosion intensity grades, this paper pioneered to use machine learning methods to address this problem. Firstly, the widely used (Revised) Universal Soil Loss Equation ((R)USLE) framework was applied to simulate the spatial distribution of soil erosion. Then, the K-fold algorithm was used to evaluate the accuracy and stability of five machine learning algorithms for fitting soil erosion. The random forest (RF) method performed best, with average accuracy reaching 86.35%. Then, the Permutation Importance (PI) and the Partial Dependence Plot (PDP) methods based on RF were introduced to quantitatively analyze the main driving factors under different geological conditions and the driving force changes of each factor under different erosion intensity grades, respectively. Results showed that the main drivers of soil erosion in Chongqing and Guizhou were cover management factors (PI: 0.4672, 0.4788), while that in Sichuan was slope length and slope factor (PI: 0.6165). Under different erosion intensity grades, the driving force of each factor shows nonlinear and complex inhibitory or promoting effects with factor value changing. These findings can provide scientific guidance for the refined management of soil erosion, which is significant for halting or reversing land degradation and achieving sustainable use of land resources. Full article

The USLE and the RUSLE are two common erosion prediction models that are used worldwide, and soil erodibility (K-factor) is one parameter used to calculate them. The objectives of this study were to investigate the variability of soil-erodibility factors under different soil-texture classes and evaluate the efficiency of diffuse reflectance spectroscopy (DRS) in the near-infrared range at predicting the USLE and RUSLE K-factors using a partial least squares regression analysis. The study was conducted in Fluvisols in dry tropical forest (the Caatinga). Sampling was undertaken in the first 20 cm of soil at 80 sites distributed 15 m apart on a 70 m × 320 m spatial grid. Results show that the clay fraction is represented mainly by 2:1 phyllosilicates. Soil organic matter content is low (<0.2%), which is typical of tropical dry forests, and this is reflected in the high values of the calculated USLE and RUSLE K-factors. An empirical semivariogram was used to investigate the spatial dependence of both K-factors. Pedometric modeling showed that DRS can be used to predict both USLE (R²_adj = 0.53; RMSE = 8.37 10⁻³ t h MJ⁻¹ mm⁻¹) and RUSLE (R²_adj = 0.58; RMSE = 6.78 10⁻³ t h MJ⁻¹ mm⁻¹) K-factors. Full article

A devastating forest fire in August 2021 burned about 517 km² of the northern part of Evia Island, affecting vegetation, soil properties, sediment delivery and the hydrological response of the catchments. This study focuses on the estimation of the annual soil loss in the study area under natural (pre-fire) and post-fire conditions. The assessment of the soil loss potential was conducted with the application of the Universal Soil Loss Equation (USLE), which is an empirical equation and an efficient way to predict soil loss. The USLE factors include rainfall erosivity (R), soil erodibility (K), the slope and slope length factor (LS), the cover management factor (C) and the erosion control practice factor (P). The USLE quantified the annual soil erosion (in t/ha/year) for both pre- and post-wildfire conditions, and the study area has been classified into various soil loss categories and soil erosion intensity types. The results showed that the annual soil loss before the forest fires ranged from 0 to 1747 t/ha, with a mean value of 253 t/ha, while after the fire the soil loss significantly increased (the highest annual soil loss was estimated at 3255 t/ha and the mean value was 543 t/ha). These values demonstrate a significant post-fire change in mean annual soil loss that corresponds to an increase of 114% compared to the pre-fire natural condition. The area that is undergoing high erosion rates after the extreme wildfire event increased by approximately 7%, while the area of moderate rates increased by 2%. The calculated maximum potential of soil erosion, before and after the 2021 extreme wildfire event, has been visualized on spatial distribution maps of the average annual soil loss for the study area. The present study underlines the significant post-fire increase in soil loss as part of the identification of the more vulnerable to erosion areas that demand higher priority regarding the protective/control measures. Full article

Many new agricultural activities resulted in severe soil erosion across the Cameron Highlands’ land surface. Therefore, this study determines the cover (C) and land management (P) factors of the USLE for predicting soil loss risk in Cameron Highlands using a Geographic Information System (GIS). For this study, data from the Department of Agriculture Malaysia (DOAM) and the Department of Town and Country Planning Malaysia (PLANMalaysia) were used to generate several C&P factors in the Cameron Highlands. Data from both agencies have resulted in C factors with 0.01 to 1.00 and P factors with 0.30 to 0.49. Due to the cover and land management factor varies depending on the data collected by the various agencies, this study used the two data sets to come up with a C&P factor that accurately reflected both agricultural and urban growth effects. RKLS factors of USLE were obtained from the DOAM with values R (2375–2875), K (0.005), LS (2.5–25), respectively. The Cameron Highlands’ soil loss risk with these new C&P values resulted in a soil loss of 6.72 per cent (4547.22 hectares) from high to critical, with a percentage difference range of −0.77 to +3.37 under both agencies, respectively. Full article

Soil erosion caused by intense rainfall events is one of the major problems affecting agricultural and forest ecosystems. The Universal Soil Loss Equation (USLE) is probably the most adopted approach for rainfall erosivity estimation, but in order to be properly employed it needs high resolution rainfall data which are often unavailable. In this case, empirical formulas, employing aggregated rainfall data, are commonly used. In this work, we select 12 empirical formulas for the estimation of the USLE rainfall erosivity in order to assess their reliability. Moreover, we used a Stochastic Rainfall Generator (SRG) to simulate a long and high-resolution rainfall time series with the aim of assessing its application to rainfall erosivity estimations. From the analysis, performed in the Rieti province of Central Italy, we identified three equations which seem to provide better results. Moreover, the use of the selected SRG seems promising and it could help in solving the problem of hydrological data scarcity and consequently guarantee major accuracy in soil erosion estimation. Full article

Soil erosion modeling is becoming more significant in the development and implementation of soil management and conservation policies. For a better understanding of the geographical distribution of soil erosion, spatial-based models of soil erosion are required. The current study proposed a spatial-based model that integrated geographic information systems (GIS) techniques with both the universal soil loss equation (USLE) model and the Index of Land Susceptibility to Wind Erosion (ILSWE). The proposed Spatial Soil Loss Model (SSLM) was designed to generate the potential soil erosion maps based on water erosion and wind erosion by integrating factors of the USLE and ILSWE models into the GIS environment. Hence, the main objective of this study is to predict, quantify, and assess the soil erosion hazards using the SSLM in the Dakhla Oasis as a case study. The water soil loss values were computed by overlaying the values of five factors: the rainfall factor (R-Factor), soil erodibility (K-Factor), topography (LS-Factor), crop types (C-Factor), and conservation practice (P-Factor). The severity of wind-driven soil loss was calculated by overlaying the values of five factors: climatic erosivity (CE-Factor), soil erodibility (E-Factor), soil crust (SC-Factor), vegetation cover (VC-Factor), and surface roughness (SR-Factor). The proposed model was statistically validated by comparing its outputs to the results of USLE and ILSWE models. Soil loss values based on USLE and SSLM varied from 0.26 to 3.51 t ha⁻¹ yr⁻¹ with an average of 1.30 t ha⁻¹ yr⁻¹ and from 0.26 to 3.09 t ha⁻¹ yr⁻¹ with a mean of 1.33 t ha⁻¹ yr⁻¹, respectively. As a result, and according to the assessment of both the USLE and the SSLM, one soil erosion class, the very low class (<6.7 t ha⁻¹ yr⁻¹), has been reported to be the prevalent erosion class in the study area. These findings indicate that the Dakhla Oasis is slightly eroded and more tolerable against water erosion factors under current management conditions. Furthermore, the study area was classified into four classes of wind erosion severity: very slight, slight, moderate, and high, representing 1.0%, 25.2%, 41.5%, and 32.3% of the total study area, respectively, based on the ILSWE model and 0.9%, 25.4%, 43.9%, and 29.9%, respectively, according to the SSLM. Consequently, the Dakhla Oasis is qualified as a promising area for sustainable agriculture when appropriate management is applied. The USLE and ILSWE model rates had a strong positive correlation (r = 0.97 and 0.98, respectively), with the SSLM rates, as well as a strong relationship based on the average linear regression (R² = 0.94 and 0.97, respectively). The present study is an attempt to adopt a spatial-based model to compute and map the potential soil erosion. It also pointed out that designing soil erosion spatial models using available data sources and the integration of USLE and ILSWE with GIS techniques is a viable option for calculating soil loss rates. Therefore, the proposed soil erosion spatial model is fit for calculating and assessing soil loss rates under this study and is valid for use in other studies under arid regions with the same conditions. Full article

The fretting fatigue performance of laminated, unidirectional (UD), pin-loaded, carbon fibre-reinforced polymer (CFRP) straps that can be used as bridge hanger cables was investigated at a sustained service temperature of 60 °C. The aim of this paper is to elucidate the influence of the slightly elevated service temperature on the tensile fatigue performance of CFRP straps. First, steady state thermal tests at ambient temperature and at 60 °C are presented, in order to establish the behaviour of the straps at these temperatures. These results indicated that the static tensile performance of the straps is not affected by the increase in temperature. Subsequently, nine upper stress levels (USLs) between 650 and 1400 MPa were chosen in order to establish the S–N curve at 60 °C (frequency 10 Hz; R = 0.1) and a comparison with an existing S–N curve at ambient temperature was made. In general, the straps fatigue limit was slightly decreased by temperature, up to 750 MPa USL, while, for the higher USLs, the straps performed slightly better as compared with the S–N curve at ambient temperature. Full article

The erosive capacity of precipitation depends on its intensity, volume, and duration. The rainfall erosivity factor (R) of the Universal Soil Loss Equation (USLE) requires high frequency (subhourly) data. When these are not available, R can be estimated from simplified indices such as the Modified Fournier Index (MFI), the Precipitation Concentration Index (PCI), and the Seasonality Index (SI), which are computed from monthly precipitation. We calculated these indices for 34 stations in the complex terrain Abruzzo region (central Italy) during 1980–2018, based on both gauge (point) and grid datasets. Using 30-min rainfall data of 14 stations, we verified that MFI and PCI are reliable predictors of R (R² = 0.91, RMSE = 163.6 MJ mm ha⁻¹ h⁻¹ year⁻¹). For MFI, grid data do not capture the peaks in high-altitude stations and the low values in some inland areas, detected by the point dataset. Grid data show significant MFI positive trends in 74% of the stations, while the point data display significant positive trends in only 26% of stations and significant negative trends in four stations in the inland areas. The grid data complex orography requires preliminary validation work. Full article

Rainfall erosivity factor (R-factor) is one of the Universal Soil Loss Equation (USLE) input parameters that account for impacts of rainfall intensity in estimating soil loss. Although many studies have calculated the R-factor using various empirical methods or the USLE method, these methods are time-consuming and require specialized knowledge for the user. The purpose of this study is to develop machine learning models to predict the R-factor faster and more accurately than the previous methods. For this, this study calculated R-factor using 1-min interval rainfall data for improved accuracy of the target value. First, the monthly R-factors were calculated using the USLE calculation method to identify the characteristics of monthly rainfall-runoff induced erosion. In turn, machine learning models were developed to predict the R-factor using the monthly R-factors calculated at 50 sites in Korea as target values. The machine learning algorithms used for this study were Decision Tree, K-Nearest Neighbors, Multilayer Perceptron, Random Forest, Gradient Boosting, eXtreme Gradient Boost, and Deep Neural Network. As a result of the validation with 20% randomly selected data, the Deep Neural Network (DNN), among seven models, showed the greatest prediction accuracy results. The DNN developed in this study was tested for six sites in Korea to demonstrate trained model performance with Nash–Sutcliffe Efficiency (NSE) and the coefficient of determination (R²) of 0.87. This means that our findings show that DNN can be efficiently used to estimate monthly R-factor at the desired site with much less effort and time with total monthly precipitation, maximum daily precipitation, and maximum hourly precipitation data. It will be used not only to calculate soil erosion risk but also to establish soil conservation plans and identify areas at risk of soil disasters by calculating rainfall erosivity factors. Full article

The universal soil loss equation (USLE) is a widely used empirical model for estimating soil loss. Among the USLE model factors, the cover management factor (C-factor) is a critical factor that substantially impacts the estimation result. Assigning C-factor values according to a land-use/land-cover (LULC) map from field surveys is a typical traditional approach. However, this approach may have limitations caused by the difficulty and cost in conducting field surveys and updating the LULC map regularly, thus significantly affecting the feasibility of multi-temporal analysis of soil erosion. To address this issue, this study uses data mining to build a random forest (RF) model between eight geospatial factors and the C-factor for the Shihmen Reservoir watershed in northern Taiwan for multi-temporal estimation of soil loss. The eight geospatial factors were collected or derived from remotely sensed images taken in 2004, a digital elevation model, and related digital maps. Due to the memory size limitation of the R software, only 4% of the total data points (population dataset) in each C-factor class were selected as the sample dataset (input dataset) for analysis using the stratified random sampling method. Seventy percent of the input dataset was used to train the RF model, and the other 30% was used to test the model. The results show that the RF model could capture the trend of vegetation recovery and soil loss reduction after the destructive event of Typhoon Aere in 2004 for multi-temporal analysis. Although the RF model was biased by the majority class’s large sample size (C = 0.01 class), the estimated soil erosion rate was close to the measurement obtained by the erosion pins installed in the watershed (90.6 t/ha-year). After the model’s completion, we furthered our aim to address the input dataset’s imbalanced data problem to improve the model’s classification performance. An ad-hoc down-sampling of the majority class technique was used to reduce the majority class’s sampling rate to 2%, 1%, and 0.5% while keeping the other minority classes at a 4% sample rate. The results show an improvement of the Kappa coefficient from 0.574 to 0.732, the AUC from 0.780 to 0.891, and the true positive rate of all minority classes combined from 0.43 to 0.70. However, the overall accuracy decreases from 0.952 to 0.846, and the true positive rate of the majority class declines from 0.99 to 0.94. The best average C-factor was achieved when the sampling rate of the majority class was 1%. On the other hand, the best soil erosion estimate was obtained when the sampling rate was 2%. Full article