Slope Degree Research Articles

Flood susceptibility assessment using extreme gradient boosting (EGB), Iran

Flood occurs as a result of high intensity and long-term rainfalls accompanied by snowmelt which flow out of the main river channel onto the flood prone areas and damage the buildings, roads, and facilities and cause life losses. This study aims to implement extreme gradient boosting (EGB) method for the first time in flood susceptibility modelling and compare its performance with three advanced benchmark models including Frequency Ratio (FR), Random Forest (RF), and Generalized Additive Model (GAM). Flood susceptibility map is an efficient tool to make decision for flood control. To do this, the altitude, slope degree, profile curvature, topographic wetness index (TWI), distance from rivers, normalized difference vegetation index, plan curvature, rainfall, land use, stream power index, and lithology were fed to the models. To run the models, 243 flood locations were detected by field surveys and national reports. The same number of locations were randomly created in the study regions and considered as non-flood locations. The flood and non-flood locations were split in 70% ratio for the training dataset and 30% ratio for the testing dataset. Both flood and non-flood locations were fed into the models and output flood susceptibility maps were produced. In order to evaluate the performance of the algorithms, receiver operating characteristics (ROC) curve was implemented. The results of the current research show that the RF model and EGB have the best performances with the area under ROC curve (AUC) of 0.985, and 0.980, followed by the GAM and FR algorithms with AUC values of 0.97, and 0.953, respectively. The results of variable importance by the RF model show that distance from rivers has an important influence on flood susceptibility mapping (FSM), followed by profile curvature, slope, TWI, and altitude. Considering the high performances of the RF and EGB models in flood susceptibility modelling, application of these models is recommended for such studies.

Investigating the capability of estimating soil thermal conductivity using topographical attributes for the Southern Great Plains, USA

Soil thermal conductivity is one of the main factors influencing the soil temperature regime. Over the recent years, by developing remote sensing technology, topographic attributes such as elevation, slope and aspect have become accessible from digital elevation models (DEMs). Based on the interactions between soil properties and topographically heterogeneous landscapes, this research investigates the application of topographical attributes (geographical and satellite information), along with soil physical properties, in order to create pedotransfer functions (PTFs) and estimate soil thermal conductivity, not reported so far. The hydrology experiment database of the Southern Great Plains 1997 (SGP97) available online at (http://www.cei.psu.edu/nasa_lsh/), was used to obtain these functions through applying linear regression, artificial neural networks (ANNs) and support vector machine methods. Twelve PTFs (PTF1-12) were developed to predict the thermal conductivity using different combinations of soil physical properties and topographical attributes. Improvement was attained via a combination of soil physical properties and topographical attributes (PTF5-11) rather than soil physical properties alone (PTF2-4). Thermal conductivity was better estimated by use of east-west slope degree, north-south slope degree and saturated water content along with texture fractions and bulk density as predictors in PTF7. The root mean square error decreased from 0.266 and 0.217 in PTF1 to 0.217 and 0.157 in PTF7 in the training and testing steps, respectively. ANNs were the best performing method with the relative improvement ranging from 3.52 to 19.35% and from 14.00 to 28.00% in the training and testing steps, respectively. The results displayed the successful estimation of the thermal conductivity in the training and testing steps, applying a combination of soil physical and topographical attributes.

Logging and topographic effects on tree community structure and habitat associations in a tropical upland evergreen forest, Ghana

We determined the response of tree community structure to logging disturbance and topography, and the patterns of tree-habitat associations in Tano Offin Forest Reserve, Ghana. We sampled trees in 27 20 m × 20 m plots randomly and equally distributed in three topographic habitats (slope, valley, and hilltop) in each of two forests: logged and unlogged. Two topographic features, altitude and degree of slope, were measured and related with species composition. Overall, there were significant effects of logging and topographic habitat and their interaction on species diversity and composition, with the unlogged forest and valley habitat supporting higher diversity. Tree diversity varied among the topographic habitats in the logged but not in the unlogged forest. There were topographic effects on abundance of individual species but not on tree community abundance and basal area. Logging and its interaction with topographic habitat showed significant effects on tree abundance and basal area. Some species were associated with specific topographic habitats or a combination in the logged and unlogged forests. However, the patterns of habitat associations of the species differed between the logged and unlogged forests.

Integrating environmental, entomological, animal, and human data to model the Leishmania infantum transmission risk in a newly endemic area in Northern Italy

IntroductionHistorically, leishmaniasis in Italy was constrained to areas with Mediterranean climate. In the last 20 years, sand fly vectors (Phlebotomus perniciosus), cases of canine leishmaniasis (CanL) and cases of human visceral leishmaniasis (VL) have been observed in Northern Italian regions, traditionally classified as cold areas unsuitable for sand fly survival. AimWe aim to evaluate through a One-Health approach the risk of endemic transmission of Leishmania infantum in the Piedmont Region, Northern Italy. MethodsWe collected environmental, entomological, animal, and human data. We applied a geostatistical binomial model to map the probability of P. perniciosus presence in the study area, using selected environmental parameters as predictors. We evaluated the spatial relationship between the probability of P. perniciosus presence and the geographical distribution of CanL and VL cases observed between 1999 and 2013. ResultsBetween 1999 and 2003, 142 sampling sets (17%) out of 839 resulted positive for P. perniciosus. Elevation, degree of slope, normalized difference vegetation index (NDVI) and summer temperatures were associated with positive sampling sets. During the study period, 164 (13.6%) of Piedmont municipalities reported at least one autochthonous case of CanL, while 89 VL cases were observed in 54 municipalities (4.5%). We observed an association between municipalities affected by autochthonous CanL cases and the estimated probability of P. perniciosus presence (Odds Ratio for 10% increase of probability: 2.66; 95% confidence intervals (CI): 2.16–3.37). We found that human VL incident cases were positively associated with the probability of the municipality of residence of being endemic for CanL (Incidence Rate Ratio for 10% increase of probability: 1.49; 95% CI 1.02–2.16). ConclusionsUsing a One-Health approach, we quantified the spatial association between the distribution of P. perniciosus, municipalities endemic for CanL and incident cases of human VL, suggesting that the disease has become endemic in the Piedmont region.

Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data

Groundwater (GW) is being uncontrollably exploited in various parts of the world resulting from huge needs for water supply as an outcome of population growth and industrialization. Bearing in mind the importance of GW potential assessment in reaching sustainability, this study seeks to use remote sensing (RS)-derived driving factors as an input of the advanced machine learning algorithms (MLAs), comprising deep boosting and logistic model trees to evaluate their efficiency. To do so, their results are compared with three benchmark MLAs such as boosted regression trees, k-nearest neighbors, and random forest. For this purpose, we firstly assembled different topographical, hydrological, RS-based, and lithological driving factors such as altitude, slope degree, aspect, slope length, plan curvature, profile curvature, relative slope position, distance from rivers, river density, topographic wetness index, land use/land cover (LULC), normalized difference vegetation index (NDVI), distance from lineament, lineament density, and lithology. The GW spring indicator was divided into two classes for training (434 springs) and validation (186 springs) with a proportion of 70:30. The training dataset of the springs accompanied by the driving factors were incorporated into the MLAs and the outputs were validated by different indices such as accuracy, kappa, receiver operating characteristics (ROC) curve, specificity, and sensitivity. Based upon the area under the ROC curve, the logistic model tree (87.813%) generated similar performance to deep boosting (87.807%), followed by boosted regression trees (87.397%), random forest (86.466%), and k-nearest neighbors (76.708%) MLAs. The findings confirm the great performance of the logistic model tree and deep boosting algorithms in modelling GW potential. Thus, their application can be suggested for other areas to obtain an insight about GW-related barriers toward sustainability. Further, the outcome based on the logistic model tree algorithm depicts the high impact of the RS-based factor, such as NDVI with 100 relative influence, as well as high influence of the distance from river, altitude, and RSP variables with 46.07, 43.47, and 37.20 relative influence, respectively, on GW potential.

Novel hybrid intelligence models for flood-susceptibility prediction: Meta optimization of the GMDH and SVR models with the genetic algorithm and harmony search

Floods are among the deadliest natural hazards for humans and the environment. Identifying the most flood-susceptible areas is a fundamental step in the development of flood mitigation strategies and for reducing flood damage. There is an ongoing global debate regarding the most suitable model for flood-susceptibility modeling and predictions. There is also a growing interest in the development of parsimonious and precise models for flood-susceptibility prediction. This study proposed several novel hybrid intelligence models based on the meta-optimization of the support vector regression (SVR) and group method of data handling (GMDH) using different meta-heuristic algorithms, i.e., the genetic algorithm (GA) and harmony search (HS). In contrast to the traditional models, in the SVR model computational complexity does not depend on the dimensionality of the input space. GMDH model has also advantage of being appropriate to analyze multi-parametric data sets. The methodology was developed for the Haraz-Neka watershed, one of the most flood-prone areas in the coastal margins of the Caspian Sea. A total of nine geospatial parameters (slope degree, aspect, elevation, plan curvature, profile curvature, distance to the river, land use, lithology, and rainfall) were identified as the main flood-conditioning factors using information gain ratio (IGR) analyses. Based on existing reports, 132 flood locations were identified in the study area, 92 points (70%) were used together with geospatial data for flood-susceptibility modeling, and the remaining 40 points (30%) were used to validate the models. An initial flood-susceptibility model was constructed based on the SVR and GMDH models. The model parameters were optimized using the GA and HS to reproduce the flood-susceptibility maps. The prediction accuracy of the resultant maps was evaluated in terms of various statistical measures, i.e., mean square error (MSE), root mean square error (RMSE), receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC). The results showed that the SVR model had superior performance (AUC = 0.70–0.75, RMSE = 0.29–0.36, MSE = 0.08–0.13) compared to the GMDH model (AUC = 0.67–0.74, RMSE = 0.32–0.39, MSE = 0.1–0.15). Both the GA and HS remarkably improved the SVR and GMDH performance, with the SVR-GA model performing the best (AUC = 0.75, RMSE = 0.29, MSE = 0.08) followed by the SVR-HS model (AUC = 0.75, RMSE = 0.33, MSE = 0.11). Our results verify the efficiency of the proposed hybrid models for spatial flood-susceptibility prediction. The proposed models can be adopted for use in other regions with similar hydro-environment characteristics.

Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses. In this study, we applied two novel deep learning algorithms, the recurrent neural network (RNN) and convolutional neural network (CNN), for national-scale landslide susceptibility mapping of Iran. We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors (altitude, slope degree, profile curvature, distance to river, aspect, plan curvature, distance to road, distance to fault, rainfall, geology and land-sue) to construct a geospatial database and divided the data into the training and the testing dataset. We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset. We calculated the receiver operating characteristic (ROC) curve and used the area under the curve (AUC) for the quantitative evaluation of the landslide susceptibility maps using the testing dataset. Better performance in both the training and testing phases was provided by the RNN algorithm (AUC ​= ​0.88) than by the CNN algorithm (AUC ​= ​0.85). Finally, we calculated areas of susceptibility for each province and found that 6% and 14% of the land area of Iran is very highly and highly susceptible to future landslide events, respectively, with the highest susceptibility in Chaharmahal and Bakhtiari Province (33.8%). About 31% of cities of Iran are located in areas with high and very high landslide susceptibility. The results of the present study will be useful for the development of landslide hazard mitigation strategies.

Gyroscope and Accelerometer Sensor on the Lanange Jagad Dance Robot Balance System

In performing dance moves, humanoid robots are expected to move flexibly and not easily fall during dance moves. To reduce the risk of robots falling while performing dance moves, a balance control system using a gyroscope sensor and accelerometer from the MPU6050 is controlled through the Arduino MEGA 2560 PRO. Robots that have balance control, are able to maintain stability in track conditions that have a certain degree of slope. This balance control system uses the Kalman filter method for processing data from the gyroscope sensor and accelerometer in order to reduce the noise that occurs during the robot's balance process. From the results of the test, the percentage of the success rate of robots in rest was 88.8%, the percentage of success when the robot was running was 86.6%, and the percentage of success when the robot was walking with dancing was 75%. From the results of all tests, humanoid robot has a percentage of 83.4% after adding a balance control system and when the humanoid robot does not use balance control will only produce a percentage of success rate of 48.4%.

Effects of vegetation restoration and environmental factors on understory vascular plants in a typical karst ecosystem in southern China

Understory vegetation is an important component in most forest ecosystems. It is very important for soil and water conservation in karst region, study on understory will provide valuable information for understanding the interaction mechanism between understory flora and karst environment. Thirty-two plots were sampled in three vegetation types along with a restoration gradient (shrubland, forest–shrub transition, and mixed-species broadleaf forest) in typical karst mountains in Southwest Guangxi, China. Overstory trees, understory vascular plants, soil nutrients, and topographic factors were recorded in each 400-m2 plot. Multivariate statistics were used, including the multi-response permutation procedure (MRPP), indicator species analysis, and canonical correlation analysis (CCA). MRPP showed understory species composition significantly differed among the three vegetation types, with the greatest difference between the shrubland and the mixed forest. Twenty-one understory species were identified as significant indicator species, with 13 species being identified as indicators of the shrubland, two of forest–shrub transition, and six of the mixed forest. Light-demanding herbaceous seed plants were common in shrubland, while shade-tolerant calcicole assembled under the mixed forest. Forward selection of CCA ordination revealed that understory plant distribution was most strongly influenced by elevation, followed by soil pH, the concentration of total potassium and exchangeable calcium, slope aspect, slope degree, and the concentration of available potassium. The result reveals that vegetation types affect understory species composition by modifying understory environments. Elevation affects the spatial distribution of vegetation and soil factors, and then the understory plants. Meanwhile, soil Ca content also plays a key role in the understory species distribution. Understory diversity increased with increasing canopy structure complexity from shrubland to mixed-species forest. Thus, it is necessary to take measures to promote natural vegetation restoration and to protect the mixed forests in degraded karst areas.

Comparison of strength and stiffness parameters of purlins with different cross-sections of profiles

Abstract The article presents comparative analyzes aimed at determining the optimal cross-section used in cold-formed steel purlins. The geometrical characteristics, bending resistance and self-weight of channel, zeta and hat cross-sections were compared. The calculations were made using Dlubal SHAPE-THIN software for the bending by the main axis y-y and z-z. The characteristics for the gross and effective cross-section were determined. Based on the first stage analyzes, a significant decrease in cross-sectional bending resistant was observed as a result of a local buckling. The solution to this problem may be the use of intermediate stiffeners. In the second stage, analysis of the impact of the intermediate stiffeners’ locations on the characteristics of hat sections were conducted. Additional intermediate stiffeners on the webs, on the upper chord, and on the webs and upper chord (together) were considered. A significant effect on the bending resistant with a small increase in the element’s self-weight has been demonstrated. In the third stage, the characteristics of a channel, zeta and hat profile with intermediate longitudinal stiffener in the middle of the web were compared. The performed analyzes demonstrated that the hat cross-section shows a significant advantage in bending by the main axis z-z. This advantage can be used in case of lack of protection against lateral torsional buckling and a larger degree of roof slope.

Spatial distribution and influencing factors of settlements in the farming–pastoral ecotone of Inner Mongolia, China

ABSTRACT Rural settlements serve as an important part of the rural landscape, and environmental conditions influence their spatial distribution. We hypothesize that there are variations in the spatial patterns of settlements across different farming–pastoral ecotones that exist in accordance with different human activities and environmental conditions. We used remote sensing imagery, digital elevation model, and GIS technology to analyze and compare the spatial patterns of settlements and factors influencing these patterns across pastoral, agricultural, and ecotone areas in Inner Mongolia, China. A Voronoi model, adjacency analysis, landscape metrics, and quantitative analysis were conducted. The results indicated that (1) the settlements in the three areas tended to have a random spatial distribution, and although there were no significant differences between the settlement types, agricultural settlements tended to be clustered and pastoral settlements tended to be evenly distributed; (2) the pastoral area had only a few large and sparsely fragmented patches; (3) the factors influencing settlement distribution mainly differed with respect to the NDVI, distance to roads and rivers, and elevation across three areas, but the slope degree was similar. These results indicated there are no significant differences between the spatial patterns of settlements along the agricultural to pastoral gradient, although environmental conditions differ. Consequently, we can optimize the spatial pattern of settlements and achieve sustainable development within farming–pastoral ecotones.

Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction

Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with ~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility maps provide a basis for developing more efficient fire-fighting strategies and reorganizing policies in favor of sustainable management of forest resources.

A GIS-based assessment of the potential soil erosion and flood hazard zones in Ekiti State, Southwestern Nigeria using integrated RUSLE and HAND models

Soil loss estimation and flood hazard mapping cannot be overemphasized due to their environmental, economic and societal concern. Thus, the main objective of this study was to assess the potential soil erosion and flood hazards zones using Revised Universal Soil Loss Equation (RUSLE) and Hand Above Nearest Drainage (HAND) models, respectively for appropriate conservation and prevention measures in Ekiti State, Southwestern Nigeria. The result reveals annual soil erosion ranges from 0 to 889 t ha−1 year−1. The estimated total annual soil loss in the state was around 1.5 million tonnes, of this, 193223.3 tonnes which covers 7508 ha was lost at a rate much greater than the tolerable soil loss rate. Soil erosion vulnerability mapping was done using six (6) categories of soil loss severity from slight to very severe. Soil erosion rates varied from 0.21 t ha−1 year−1 in forests to 1.69 t ha−1 year−1 under bare soils. The very steep slope category had the highest soil erosion rate of 15.07 t ha−1 year−1 while the gentle slope regions had the least soil erosion rate of 0.39 t ha−1 year−1. About 23% of the area which covers 115847.58 ha was prone to high flood hazard whereas 15.69% (80932.30 ha) was moderately susceptible to flood risk. Approximately 15% of the area was susceptible to low flood hazard zone while 44.90% were vulnerable to very low flood occurrence. LULC has significant effect on the spatial pattern of soil loss in the study area. Potential soil loss risk zones were highly related to the degree and inclination of slope. The reported results can serve as preliminary information to determine erosion and flood hotspots in the study area and as input for policy decision for disasters prevention and conservation measures.

Tibial tray rotation and posterior slope increase risk for outliers in coronal alignment.

The extensive variation in axial rotation of tibial components can lead to coronal plane malalignment. We analyzed the change in coronal alignment induced by tray malrotation. We constructed a computer model of knee arthroplasty and used a virtual cutting guide to cut the tibia at 90° to the coronal plane. The virtual guide was rotated axially (15° medial to 15° lateral) and with posterior slopes (0° to 7°). To assess the effect of axial malrotation, we measured the coronal plane alignment of a tibial tray that was axially rotated (25° internal to 15° external), as viewed on a standard anteroposterior (AP) radiograph. Axial rotation of the cutting guide induced a varus-valgus malalignment up to 1.8° (for 15° of axial rotation combined with 7° of posterior slope). Axial malrotation of tibial tray induced a substantially higher risk of coronal plane malalignment ranging from 1.9° valgus with 15° external rotation, to over 3° varus with 25° of internal rotation. Coronal alignment of the tibial cut changed by 0.07° per degree of axial rotation and 0.22° per degree of posterior slope (linear regression, R2 > 0.99). While the effect of axial malalignment has been studied, the impact on coronal alignment is not known. Our results indicate that the direction of the cutting guide and malalignment in axial rotation alter coronal plane alignment and can increase the incidence of outliers. Cite this article: Bone Joint J 2020;102-B(6 Supple A):43-48.

Application of statistical probabilistic methods in landslide susceptibility assessment in Kurseong and its surrounding area of Darjeeling Himalayan, India: RS-GIS approach

Kurseong and its surrounding areas are frequently affected by the landslide which causes huge loss of properties and lives. The landslide susceptibility maps can play an important role in human development and sustainable environment management in Darjeeling Himalayan. The present research focused on preparing the effective landslide susceptible models using different statistical probabilistic methods, namely landslide nominal susceptibility factor (LNSF), information value (InfoVal) and certainty factor (CF) models. Experiments have been carried out in Kurseong region, a part of Darjeeling Himalaya as a field of this research. Landslide sites were identified from previous records through extensive field survey and Google pro satellite imagery. Totally, 273 landslide sites were compiled and prepared the inventory map. Out of 273 landslides, 70% were used for training and 30% were used for validating the models. Seventeen landslide conditioning factors were selected for modeling landslide susceptibility, i.e., elevation, aspect, slope degree, rainfall, geological structure, geomorphologic division, lineament, land use/land cover, distance to roads, earthquake zone, soil texture, soil depth, normalized difference vegetation index, drainage density, stream power index and topographic wetness index. The produced susceptibility maps were validated using the receiver???s operating characteristic (ROC) curves. The prediction accuracy of LNSF, InfoVal and CF models as per ROC are 80.78%, 82.91% and 86.13%, respectively. The CF achieved the highest accuracy (86.13%), while the LNSF produced the lowest ROC value (80.78%). However, the comparison of the produced landslide maps revealed that all the applied models have good precision for studying susceptibility of landslide in Kurseong and its surrounding of Darjeeling Himalaya, India. The findings of current study can be supportive for the mitigation of landslide risk in the Kurseong range as well as the surrounding comparable areas having same geoenvironmental conditions.

Novel Ensemble Landslide Predictive Models Based on the Hyperpipes Algorithm: A Case Study in the Nam Dam Commune, Vietnam

Development of landslide predictive models with strong prediction power has become a major focus of many researchers. This study describes the first application of the Hyperpipes (HP) algorithm for the development of the five novel ensemble models that combine the HP algorithm and the AdaBoost (AB), Bagging (B), Dagging, Decorate, and Real AdaBoost (RAB) ensemble techniques for mapping the spatial variability of landslide susceptibility in the Nam Dan commune, Ha Giang province, Vietnam. Information on 76 historical landslides and ten geo-environmental factors (slope degree, slope aspect, elevation, topographic wetness index, curvature, weathering crust, geology, river density, fault density, and distance from roads) were used for the construction of the training and validation datasets that are the prerequisites for building and testing the proposed models. Using different performance metrics (i.e., the area under the receiver operating characteristic curve (AUC), negative predictive value, positive predictive value, accuracy, sensitivity, specificity, root mean square error, and Kappa), we verified the proficiency of all five ensemble learning techniques in increasing the fitness and predictive powers of the base HP model. Based on the AUC values derived from the models, the ensemble ABHP model that yielded an AUC value of 0.922 was identified as the most efficient model for mapping the landslide susceptibility in the Nam Dan commune, followed by RABHP (AUC = 0.919), BHP (AUC = 0.909), Dagging-HP (AUC = 0.897), Decorate-HP (AUC = 0.865), and the single HP model (AUC = 0.856), respectively. The novel ensemble models proposed for the Nam Dan commune and the resultant susceptibility maps can aid land-use planners in the development of efficient mitigation strategies in response to destructive landslides.

Comparing the efficiency of weight of evidence, support vector machine and their ensemble approaches in landslide susceptibility modelling: A study on Kurseong region of Darjeeling Himalaya, India

The main objectives of the current study is to generate and compare the landslide susceptibility maps (LSMs) using a probabilistic model i.e. weight of evidence (WoE), a machine learning technique i.e. support vector machine (SVM) and ensemble approach i.e. WoE-SVM. Experiments were conducted in the Kurseong region, a part of Darjeeling Himalaya as a study area. Previous landslide sites were identified through field survey and previous records. Total 273 landslide sites were compiled and considered for the landslide inventory map to calibrate as well as validate the models. Seventeen landslide conditioning factors (LCFs) were used for landslide susceptibility modelling, i.e. slope degree, elevation, aspect, curvature, rainfall, geological structure, lineament, distance to main roads, soil texture, soil depth, earthquake zone, land-use/land-cover, drainage density, stream power index, sediment transport index, and topographic wetness index. The generated landslide susceptibility maps were validated applying the receiver operating characteristic curves (ROC), Positive Predictive Value (PPV), Proportion Correctly Classified (PCC) and the seed cell area index (SCAI). The results revealed that prediction rates are 89.47%, 91.81%, and 94.45% with standard error 0.0359, 0.0289 and 0.0183 respectively for WoE, SVM and ensemble WoE-SVM models. The study shows that the fellow land, plantation area, roads side areas and denudation structural hills having elevation more than 1500 m and 24° to 69° slopes are extremely susceptible to landslides. The comparison of the produced landslide maps revealed that all the applicable models have committed accuracy for studying sensitivity in Kurseong region of Darjeeling Himalaya. The ensemble model has better capability than the other two models. The results may be helpful for landslide risk mitigation in the Kurseong and its surrounding region having similar terrain region and geological conditions.

Understanding the Role of Friction and Adhesion in the Display of Tapestries on Slanted Supports

ABSTRACT This paper reports on an investigation into slanted support, an increasingly common method of displaying tapestries in mainland Europe. Two factors contribute to the efficacy of slanted supports for tapestry: the angle of display and the degree of friction between the tapestry and the support. Initial analysis demonstrated that a small degree of slope, of 5°, has only marginal impact on reducing the load experienced by the tapestry. Experiments were carried out to compare the friction between small tapestry fragments and a range of fabrics commonly used to cover slanted supports. Lined and unlined tapestry fragments were placed on a fabric-covered board and tilted from horizontal to vertical. In most cases it was found that the board could be tilted beyond vertical before the tapestry began to slip; this meant it was not possible to calculate a coefficient of friction. It was clear that the simple Coulomb model of friction does not apply in this situation and that adhesive forces are even more significant than friction. Loading the tapestry samples demonstrated that the coefficient is dependent on load, confirming this observation. The paper also reports on the use of digital image correlation (DIC) to calculate strain data. A tapestry fragment was suspended on a vertical board half covered in cotton molton fabric. The strain values were very low on the half of the tapestry against the molton; they were higher on the half against the uncovered board. It was also apparent that the tapestry with board-covering fabric was less subject to expansion and contraction caused by fluctuating relative humidity. The research concluded that the friction/adhesion forces are so effective that a tapestry is well supported on a vertical fabric-covered board; there is no additional benefit in providing a small degree of slant.

Self-Reinforcing Processes Governing Urban Sprawl in Belgium: Evidence over Six Decades

Urban sprawl is widely acknowledged as an environmental and socio-economic challenge worldwide. This study examines urban sprawl in Belgium over six decades from 1950 to 2010. We assume that sprawl is a self-reinforcing process, i.e., sprawl is fueling further sprawl over time. The main objective of this study is to examine this assumption. We measure urban sprawl at four different levels in this study: country, regions, municipalities, and 1-km2 cells. Three sprawl indices are employed: the degree of urban dispersion, degree of urban permeation of the landscape, and built-up land uptake per capita. These three indices consider both the growth of built-up areas and population density to measure the magnitude of sprawl. The drivers of urban sprawl have been analyzed at a 1-km2 level. The examined drivers are previous urban dispersion patterns, distance to urban cores, elevation, and slope degree by means of linear regression. Urban sprawl significantly increased between 1950 and 1980, whereas its increase was more moderate between 1980 and 2010. Urban dispersion and permeation strongly affect the Brussels and Flanders regions. The results show that the increase in the degree of dispersion is locally driven by previous values of dispersion; i.e., it provides an adequate milieu for further dispersion. Therefore, our conclusion is that urban sprawl in Belgium tends to be a self-reinforcing process.

Application of predictor variables in spatial quantification of soil organic carbon and total nitrogen using regression kriging in the North Kashmir forest Himalayas

Soil organic carbon (SOC) and total soil nitrogen (TSN) are the critical indices of soil fertility and the biogeochemical cycle. Distribution and variation of SOC and TSN in space are central to climate change and sustainable soil management studies. Little research on spatial prediction of SOC and TSN based on geostatistical techniques employing secondary variables (sampling location) and auxiliary information (topographic factors and type of vegetation) has been conducted globally and under the Himalayas in particular. In this study, 96 soil samples of 0–20 cm depths were taken from the forest area of North Kashmir Himalayas. The effect of topographic factors-elevation, slope, compound topographic index (CTI), stream power index (SPI), sediment transport index (STI), and normalized difference vegetation index (NDVI) on SOC and TSN spatial distribution were studied using regression kriging. Results indicate that regression kriging is a better predictor of SOC and TSN spatial distribution by 97.3% and 96.4% respectively than ordinary kriging with residuals moderately auto-correlated. Semi-variogram test indicated that elevation, slope, and NDVI are major factors driving SOC and TSN spatial variation. The negative correlation of topographic elevation and slope with the spatial distribution of SOC and TSN reveals better stabilization of SOC and TSN at lower degrees of slope and lower altitudes. Our study suggests that regression kriging at greater scale can provide the best estimations in a relatively uniform ecosystem provided there is a significant correlation between auxiliary variables and the SOC and TSN contents with residuals spatially auto-correlated.