Top PDF Gully erosion assessment and prediction on non-agricultural lands using logistic regression

Gully erosion assessment and prediction on non-agricultural lands using logistic regression

Gully erosion assessment and prediction on non-agricultural lands using logistic regression

Military training areas experience significant amounts of soil erosion which leads to nonpoint source pollution. Military activities lead to changes in the surrounding natural environment. The activities can cause significant land degradation, which lead to unfavorable environmental impacts, especially soil erosion. Training activities such as field maneuvers, mortar and artillery fire, small arms fire, and combat vehicle operations have led to soil disturbance. Most of the mechanized maneuvers take place on the northern 75% of Fort Riley (Abel et al. 2009). Wildfires resulting from training and management activities such as mowing, prescribed burning, chemical weed control, and small scale timber harvest have also caused soil disturbances on Fort Riley (Althoff et al. 2006). Since the National Environmental Policy Act of 1969 (NEPA) was passed and the U.S. Army Regulation 200-2 (Department of Defense 2002) was published, the military has been required to minimize these environmental impacts. It requires the Department of Defense to consider the environmental consequences of their actions and to document these considerations (Department of Defense 2002). The Army has developed the Integrated Training and Management (ITAM) program to optimize the sustainability of training areas. This program supports the management and maintenance of training areas while it also encourages military preparedness.
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Gully erosion assessment and growth prediction on military training lands

Gully erosion assessment and growth prediction on military training lands

As this concentrated flow develops, rill erosion is formed (U.S. Environmental Protection Agency, 2003). Rill erosion is the first type in which small channels or streamlets are created. With regards to agricultural settings, rill erosion can usually be removed via tillage practices and will not necessarily reform in the same location (Foster, 1986). These channels are smaller than ephemeral gullies and can visually be identified as multiple small, parallel streams that are disconnected from each other (Foster, 1986). In order to numerically separate ephemeral gullies from rill erosion, scientists use a threshold definition of 929 cm 2 for the cross-sectional area (Poesen et al., 2003). Therefore, if this area is less than 929 cm 2 , the erosion is classified as rill, while cross-section areas greater than this value are classified as ephemeral gullies. Having a minimum depth near 0.5-0.6 meters and a minimum width of 0.3 meters can also help categorize the type of erosion present in the landscape (Imeson & Kwaad, 1980), but does not seem to be an absolute threshold. Visually, each rill channel is typically the same size and spaced
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Modelling of Gully Erosion Site Data in Southeastern Nigeria, Using Poisson and Negative Binomial Regression Models

Modelling of Gully Erosion Site Data in Southeastern Nigeria, Using Poisson and Negative Binomial Regression Models

Abstract: The development of gully and other forms of erosion have become the greatest environmental problem facing the people of Southeastern Nigeria. The availability of farm land for agricultural production and construction activities have, been greatly reduced due to soil erosion. This study is set to apply Poisson and negative binomial regression models to identify the major factors that contribute to gully erosion development in Southeastern Nigeria and to ascertain better model suitable for prediction of gully erosion, using secondary data. Maximum likelihood estimation procedure was used to estimate the parameter of the selected model with the number of gully erosion sites as the response variable (Y) and 5-explanatory variable (X’s). Also applying the forward selection criteria to the 5-explanatory variables, model 5 is best suitable for forecasting the subject under study. The result of the Poisson regression model showed that there was over dispersion in gully erosion site data since the dispersion parameter (3.677) was greater than 1 hence underestimating the standard error and over estimating the coefficient of the explanatory variable, consequently giving misleading inference. The result of the assessment criteria for Poisson regression model and Negative binomial regression model revealed that the Negative binomial regression model predicts gully erosion soil data better in southeastern Nigeria as considered in this study. Heavy Rainfall (HRF), Extractive Industries (EXI), Excess Farm activities (EFX) are the major contributors to gully erosion site development in southeastern Nigeria, with Heavy Rainfall ranking first. A model suitable for prediction of gully erosion sites in southeastern Nigeria has been developed.
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Gully erosion susceptibility mapping using multivariate adaptive regression splines-replications and sample size scenarios

Gully erosion susceptibility mapping using multivariate adaptive regression splines-replications and sample size scenarios

by Gómez-Gutiérrez et al. [27], who achieved a mean AUC of 0.826 and 0.859 in Spain and Sicily respectively. Accordingly, Conoscenti et al. [17] pointed out that even for the worst validation AUC value inferred from five datasets, MARS is much more than the best calibration AUC value calculated compared with the logistic regression (LR) model. In addition, Conoscenti et al. [102] evaluated gully erosion sensitivity in both surrounding farmed basins of Sicily (Italy) by using multivariable adaptive regression splines. Model assessment on the whole basins indicates the outstanding predictive performance of models. This finding supports our results. Based on cut-off values, the probability of gully erosion occurrence for each cell was adapted into a binary (positive/negative) prediction to achieve the spatial distribution of cases correctly categorized (true positives and negatives (TP, TN)) and incorrectly classified (false positives and negatives (FP, FN)) for the MARS susceptibility model (Table 6). A larger true positive prediction is produced by the model, indicating that the conditions for gully erosion are widespread. As Rahmati et al. [96] pointed out, random ordination of datasets is the main origin of uncertainty in spatial modelling. From the validation result, it is clear that the MARS model provided acceptable to excellent performance in predicting the probability of gully erosion occurrence based on independent and dependent assessment measures. In light of abovementioned results, it is obvious that the MARS model can be used as an efficient statistical model for the prediction gully erosion susceptibility map. This is in line with the other studies that applied this model to landslides and gully erosion susceptibility mapping [9,25–27,102]. This is a relevant issue to achieve sustainable land management where gully erosions must be restored when they have developed as a result of human mismanagement, and for this it is necessary to use nature-based solutions [6]. To achieve success in gully erosion control, the strategies must find a way to reduce the connectivity of the flows [7].
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Ensembling Coalesce of Logistic Regression Classifier for Heart Disease Prediction using Machine Learning

Ensembling Coalesce of Logistic Regression Classifier for Heart Disease Prediction using Machine Learning

Abstract: In today’s modern world, the world population is affected with some kind of heart diseases. With the vast knowledge and advancement in applications, the analysis and the identification of the heart disease still remain as a challenging issue. Due to the lack of awareness in the availability of patient symptoms, the prediction of heart disease is a questionable task. The World Health Organization has released that 33% of population were died due to the attack of heart diseases. With this background, we have used Heart Disease Prediction dataset extracted from UCI Machine Learning Repository for analyzing and the prediction of heart disease by integrating the ensembling methods. The prediction of heart disease classes are achieved in four ways. Firstly, The important features are extracted for the various ensembling methods like Extra Trees Regressor, Ada boost regressor, Gradient booster regress, Random forest regressor and Ada boost classifier. Secondly, the highly importance features of each of the ensembling methods is filtered from the dataset and it is fitted to logistic regression classifier to analyze the performance. Thirdly, the same extracted important features of each of the ensembling methods are subjected to feature scaling and then fitted with logistic regression to analyze the performance. Fourth, the Performance analysis is done with the performance metric such as Mean Squared error (MSE), Mean Absolute error (MAE), R2 Score, Explained Variance Score (EVS) and Mean Squared Log Error (MSLE). The implementation is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that before applying feature scaling, the feature importance extracted from the Ada boost classifier is found to be effective with the MSE of 0.04, MAE of 0.07, R2 Score of 92%, EVS of 0.86 and MSLE of 0.16 as compared to other ensembling methods. Experimental results shows that after applying feature scaling, the feature importance extracted from the Ada boost classifier is found to be effective with the MSE of 0.09, MAE of 0.13, R2 Score of 91%, EVS of 0.93 and MSLE of 0.18 as compared to other ensembling methods.
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Exploring the relations between river bank erosion and geomorphological controls using geographically weighted logistic regression

Exploring the relations between river bank erosion and geomorphological controls using geographically weighted logistic regression

The attraction of GWLR analysis to geomorphology lies in the ability to explore spatial variation in the relations between driver variables and process—in this case, bank erosion. Such analysis permits the identification of zones of process control, which might be expected to vary between reaches of a river, but also between catch- ments. Using the Afon Dyfi as a case study, it becomes possible to comment on the widely held theoretical view that a specific sequence of process domains exist along a watercourse (Lawler 1992; Abernathy and Rutherford 1998). The GWLR analysis of the Dyfi data set demonstrates that the scale and sequencing of process domains may be much finer and more varied than existing models suggest. Rather, controls on bank erosion processes vary at a fine spatial frequency, primarily according to the available energy to drive erosion (stream power) and the resistance to that erosion provided by bank materials and vegetation. For the Dyfi at least, channel planform and the histor- ical activity of the channel do not appear to influence the process or location of con- temporary bank erosion directly, although both may control the location and nature of bank materials and vegetation.
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Storm wise sediment yield prediction using hillslope erosion model in semi arid abundant lands

Storm wise sediment yield prediction using hillslope erosion model in semi arid abundant lands

Evaluation of soil erosion by existing models is needed as an important tool for managerial purposes in desig- nation of proper water and soil conservation techniques. The present study aimed to assess the applicability of hillslope erosion model (HEM) as one of the newest erosion models for prediction of storm-wise sediment yield in Khosbijan rangeland with 20% slope steepness by using soil erosion standard plots. In order to run the model, runoff depth, land surface cover, soil texture, slope steepness and length were determined for 16 storm events. The results showed that the uncalibrated HEM did not simulate the observed sediment yields properly. Calibration of soil erodibility parameter and developing regression between observed and estimated data indi- cated that the model was capable of predicting sediment yield in plots by applying soil erodibility parameter of 0.15 with determination coefficient of 0.64 and estimate error of 40%.
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A Heart Disease Prediction Model using SVM Decision Trees Logistic Regression (SDL)

A Heart Disease Prediction Model using SVM Decision Trees Logistic Regression (SDL)

A support vector machine is a type of model used to analyze data and discover patters in classification and regression analysis. Support vector machine (SVM) is used when your data has exactly two classes. An SVM classifies data by finding the best hyper plane that separates all data points of one class from those of the other class. The larger margin between the two classes, the better the model is. A margin must have no points in its interior region. The support vectors are the data points that on the boundary of the margin. SVM is based on mathematical functions and used to model complex, and real world problems. SVM performs well on data sets that have many attributes, such as the CHDD.
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A Bayesian Logistic Regression approach in Asthma Persistence Prediction

A Bayesian Logistic Regression approach in Asthma Persistence Prediction

It is important to mention that the data table has to be preprocessed before the analysis. When the variables are measured in different units, (as in the case of the asthma dataset) it is usual to standardize each variable. This is obtained by subtracting the mean from each variable and then dividing each variable by its standard deviation [35]. Another important issue in Bayesian Logistic regression is which parameters are going to be kept in the model. In this study our approach leads to a model in which none of the estimated coefficients contains zero in the 95% credible interval (Figures A1-A4 are given in Supplementary files ) of the coefficient posterior distribution, i.e. from the full model, we eliminate those variables whose 95% credible interval contains zero. Using the Metropolis – Hastings algorithm mentioned in the previous section with 100000 MCMC samples with a burn - in period of 25000 samples and centering or standardizing the data matrix before the use of the Principal Component analysis, the results are shown in the models of Tables 2 (Models A and Model B) and 3 (Models C and D). Also it must be mentioned that a thinning interval equal to 10 was used to remove dependencies between successive simulations. It can be observed (from Tables 2-3) that the four variables are included in all models, but the models of Table 3 have a reduction in the standard errors of the coefficients (posterior means) as well as in the odds ratio. This change is due to the covariance matrix being different in case of centering or standardizing before PCA.
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Environmental conditions of gully erosion in Hungary

Environmental conditions of gully erosion in Hungary

85 Investigations in Lake Balaton catchment by various authors in- cluded also some aspects of gully development. The Department of Physical Geography of the Hungarian Academy of Sciences carried out several research projects on soil erosion forms and processes in the catchment. Tóth A. (2004) analysed the ratio of sheet and gully erosion in the Tetves catchment. Jakab, G. et al. (2005) made a very detailed morphometrical survey of gullies in the same catchment. Kertész, Á. (2004a) studied geomorphic processes on collapsible and dispersive soils. Rill initiation and development was part of various rain- fall simulation experiments (Csepinszky B. et al. 1998; Csepinszky B.–Jakab G. 1999; Sisák, I. et al. 2002; Centeri, Cs. 2002; Centeri, Cs.–Pataki, R. 2003, 2005; 2005, Szűcs, P. et al. 2006; Jakab, G.–Szalai, Z. 2005; Balogh J. et al. 2008).
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Prediction of Antibiotics Residues in Raw Milk by Using Binary Logistic Regression Model

Prediction of Antibiotics Residues in Raw Milk by Using Binary Logistic Regression Model

After accomplishing of tests and collecting data in summer and autumn, binary logistic regression model has been chosen to describe and predict the presence or absence of antibiotic residues in raw milk and some tests have been done for achieving the best equation based on the dependent variable (antibiotic residues) and independent variables (somatic cell count, total count of mesophilic microorganisms, protein, fat, lactose, acidity, PH and electrical conductivity). In analysis by this regression, the code one means presence and the code zero means the absence of antibiotic residues in raw milk. One of the greatest methods to achieve the best logistic regression model is to use Backward Conditional Technique. The basis of this method is that with the help of software SPSS Statistics ver.22.0 once all the independent variables and their interactions with the dependent variable are considered as models. Then the software changes the variables from model based on probability ratio and this process goes on as far as the variables are not deleted because of the importance (Table 2). By using the mentioned technique, the following equations were obtained based on logistic regression, respectively, for summer and autumn:
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Logistic Regression Model for Prediction of Airway Reversibility Using Peak Expiratory Flow

Logistic Regression Model for Prediction of Airway Reversibility Using Peak Expiratory Flow

Thus, the question is whether the PEF is a reliable alternative for spirometry and the important challenge is using PEF for predicting BDR, especially in airflow limitation. Aggarwal and colleagues showed that ΔPEF and ΔPEF% had poor discrimination power in identifying BDR (24). The highest specificity of 88% was found with ΔPEF increase of 80 l/min versus a 12% improvement over baseline in either FEV1 or FVC, along with an absolute volume increase of 200ml. Dekker et al. could detect BDR (>9% increase in FEV1) in patients with asthma and COPD based on PEF (60 l/min increase in PEF) with a sensitivity, specificity and PPV of 68%, 93%, and 87%, respectively (27). In another study on asthmatic patients, a >18% increase in the PEF predicted FEV1 >15% with a sensitivity, specificity, PPV and NPV of 85%, 79%, 77%, and 86%, respectively (28). Since the patients and the definition of BDR were different in previous studies, it is difficult to compare their results with our findings. However, our results confirmed previous investigations regarding the fact that PEF was a poor predictor of airway reversibility. In this research, BDR (>12% increase in FEV1 or FVC) was detected using a >20% increase in ΔPEF% with a sensitivity, specificity, PPV and NPV of 60%, 68.57%, 75%, and 52.17%, respectively.
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Mathematical modeling of ephemeral gully erosion

Mathematical modeling of ephemeral gully erosion

technic to produce DEM for three cite areas and to study gully development. forestation and shadows were the problems encountered during this research. Besides, the average accuracy of the method was within 1 m. Acquired DEMs for different times were then used to estimate the gully growth. Meyer & Martínez-Casasnovas (1999) studied the probability of ephemeral gully development. The probability was calculated using the statistical model based on the GIS data of topography, soil, and management. Their model produced an 85% overall accuracy for their study area of Alt Penede-Anoia region (Catalonia, NE Spain). J. Nachtergaele & Poesen (1999) studied ephemeral gully erosion on on May 28, 1998, after the intensive rainfall event happened in Belgian Brussels and Leaven. They used high altitude aerial (stereo) photographs and field measurements of the gullies to compare the accuracy of each method. After comparing the results, it was shown that each method missed part of the gullies. A correction factor was proposed for the data acquired from aerial photographs. These data were used to estimate ephemeral gullies erosion rates. Furthermore, researchers suggested using this technic to collect inputted data of gullies geometry and the length for physically based models. Vandekerckhove et al. (2000) collected data from the literature and provided their data from the experimental study fields (in the Mediterranean region) to determine the critical relation of the local slope and catchment area for gully initiations. Researchers also mentioned the importance of the current vegetation, soil type and moisture condition of the field, stressing that they were among the factors affecting gully erosion. Daba, Rieger, & Strauss (2003) studied gully erosion in Ethiopia where they used topographic maps and photogrammetry to assess the volumes of nutrients lost during gully erosion. In that area, this group of scholars also tried to produce topographic
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Detection of Propaganda Using Logistic Regression

Detection of Propaganda Using Logistic Regression

Table 1 shows the ablation study results for the SLC task. We used the Logistic Regression with sentence length(the number of characters) fea- ture to be the baseline. To test the importance of each individual feature in the classification, we applied them to Logistic Regression one at a time, including readability grade level, sen- tence length, LIWC, TF-IDF, emotion and BERT. Among these features, readability and sentence length increased 3.13% and 5.34% of F1 score, while LIWC, TF-IDF and emotion features in- creased 7.28%, 12.76% and 12.92% of F1 score respectively. These results suggest that the length and the complexity of a sentence is effective to dif- ferentiate propagandistic sentences from the non- propagandistic ones, but not as effective as LIWC, TF-IDF and emotion do. The implication is that while propaganda techniques are likely to ap- pear in a complex and longer sentences, there are also long non-propagandistic sentences con- taining complex words. In addition, some pro- paganda techniques like slogan are not necessar- ily expressed in long sentences. The difference of language use, reflected by the words, punctu- ations (LIWC), term frequency inverse document frequency (TF-IDF) and the emotional expression (emotion) shapes a more fit boundary between
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Prediction Of Mortality Using Logistic Regression Analysis In Female Patients Suffering From Myocardial Infarction

Prediction Of Mortality Using Logistic Regression Analysis In Female Patients Suffering From Myocardial Infarction

In order to improve the accuracy of the logistic model, the researchers transformed some information from the original dataset downloaded from WHO (see Table 2). Since logistic regression deals with binary as dependent variable, the outcome variable is categorized as 0 or 1. If the patient is identified as 0, it signifies the algorithm that the patient is dead and if the patient is identified as 1, it means that the patient was able to survive previous myocardial infarction. There are seven categorical attributes that contribute to the disease, and in order to produce a mathematical formula, categorical attributes were transformed into numerical distribution and put a scale for each attribute.
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Predicting ephemeral gully erosion with RUSLER and EphGEE

Predicting ephemeral gully erosion with RUSLER and EphGEE

deposition occurred most often at confluences of channels (Vieira et al., 2014), and the main thalweg shows incision to the non-erodible layer (Fig. 4(a)) and a width greater than 1 m (Fig. 3(a)) until within 30 m of the watershed outlet where the topography leveled. When the grassed waterway was simulated, the main thalweg had deposition throughout its length (Fig. 4(b)). The simulated depths of deposition in the waterway thalweg are somewhat exagerated since no sediment deposition was simulated in the RUSLER simulation because the waterway raster cells where simulated as cropland. If a grass waterway had been simulated in RUSLER, increased deposition in the waterway field areas would have decreased sediment load to EphGEE and, thus, reduced sediment depth predicted by EphGEE. Deposition depths would also have been lower if the watershed cross-section had started out as a trapezoid rather than a triangle. However, Spomer et al. (1985) showed triangular initial grassed waterway cross-sections and reported that waterway sediment deposition more than 0.5 m deep and extending 40 m wide occurred between 1963 and 1980 in an adjacent field.
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Rehabilitation of Alluvial Gully Erosion in the Normanby Catchment

Rehabilitation of Alluvial Gully Erosion in the Normanby Catchment

~ $6,000 to bulldoze, gypsum, hydromulch a 0.2 ha (50x40m) gully slope ~ $60 million to treat 2000 ha of mapped alluvial gullies in the Normanby. Would this actually reduce erosio[r]

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THE DEVELOPMENT OF MODELS FOR PREDICTION OF GULLY GROWTH AND HEAD ADVANCEMENT(A Case Study: Queen Ede Gully Erosion Site, Benin City, Edo State, Nigeria)

THE DEVELOPMENT OF MODELS FOR PREDICTION OF GULLY GROWTH AND HEAD ADVANCEMENT(A Case Study: Queen Ede Gully Erosion Site, Benin City, Edo State, Nigeria)

However, it was observed that in the study area, land use pressure is caused by astronomical increases in population migration from rural to urban areas. One of the main causes of gully erosion in Edo State is road construction work with inappropriately ter- minated drains [10]. The road is said to induce a concentration of surface run o, with a diversion of con- centrated run o to other catchments, and an increase in catchment size, which enhances gully development after road construction [11]. Changes in drainage pattern associated with urbanization result in gully erosion, particularly where illegal settlements without urban infrastructures exist. Kalu and Goodwill [12] observed that while all the various climatic factors are important in the assessment of erosion potential, rainfall is the most signicant in the determination of causes of erosion in the study area. Byran [13] noted that physical properties such as the size (tex- ture), hydrologic (permeability) and chemical (organic content) of soil are generally the main parameter that aects soil erodibility. On the other hand, Zingg [14] observed that the shape of the catchment aects the velocity and tangential stress developed by the run o within a catchment. Conrming this observation, Rascal and Francis [15] stated that runo under a 25 mm/hr rainfall intensity increased from 69% of rainfall at a 5% slope to 86% of rainfall at a slope of 20%. Jean Poosen et al. [16], in their study on gully erosion, emphasized the importance of modelling as an essential tool in the modelling of gully erosion studies. Also, Ehiorobo and Izinyon [17], in their study on gully erosion, described the importance of GPS application with total station surveys, in combination with GIS and remote sensing, in the monitoring of gully erosion. Hum et al. [18] observed that gully erosion is the main source of sedimentation in river basins. Research carried out by various interest groups have shown that gully erosion represents one of the most important soil degradation processes in Nigeria, as it causes consider- able soil loss, and produces large volumes of sediment. Gullies are also catalysts for transferring surface run o and sediment load from upland areas to valley oors; thereby, creating channels that aggravate the problem of ooding and water pollution. Many cases of damage to water courses and property by runo from agricultural land are related to the occurrence of gully erosion [19]. Bourdman [20] observed that
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Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers

Graphical assessment of internal and external calibration of logistic regression models by using loess smoothers

Several statistical tests have been proposed to assess model calibration or goodness-of-fit. Hosmer and Lemeshow proposed a goodness-of-fit test on the basis of dividing the sample into strata on the basis of the predicted probability of the outcome [9, 10]. In practice, subjects are often divided into ten, approxi- mately equally-sized, groups on the basis of the deciles of risk. A chi-squared test is then used to compare the observed versus predicted probability of the outcome across the strata. While the Hosmer–Lemeshow test is based on grouping subjects on the basis of the predicted probability of the outcome, Tsiatis pro- posed a test on the basis of grouping subjects on the basis of the predictor variables [11]. Le Cessie and van Houwelingen proposed tests of goodness-of-fit on the basis of smoothed residuals [12], while Royston proposed tests to detect nonlinearity that used partial sums of residuals [13]. Stukel proposed a generalized logistic model that permits testing of the adequacy of a fitted logistic regression model [14]. While the tests described previously allow one to formally test the goodness-of-fit of the fitted logistic regression model, other authors have proposed methods to qualitatively assess model calibration. Cox proposed a recalibration framework, in which the observed outcome is compared with the linear pre- dictor [15]. An intercept and slope are then estimated, which are related to calibration-in-the-large and the calibration slope [8]. The former compares the mean predicted probability of the occurrence of the outcome with the mean outcome, while the latter, when used for internal validation, reflects the amount of shrinkage that is necessary to make the model well calibrated for predicting outside the derivation sample [8]. A two-degree of freedom test can be performed to test for miscalibration [16]. Harrell and Lee extended Cox’s recalibration framework to allow one to derive indices denoting the lack of cali- bration, discrimination, and overall quality of prediction [17, 18]. Similarly, Dalton recently extended Cox’s recalibration framework to provide for a flexible recalibration of binary prediction models [19]. Furthermore, the use of this method permits the derivation of a relative measure of miscalibration for comparing two competing prediction models.
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Gully erosion in Hungary, review and case study

Gully erosion in Hungary, review and case study

The initiation and development of gullies is in some cases promoted by subsurface erosion, i.e. by piping (called also suffusion in Hungarian literature) [12]. Physical and chemical properties of loess and loess-like sediments offer favourable conditions for the development of pipes. Collapsibility is first of all connected with calcium carbonate content (including lime concretions in older loess deposits), with the very high porosity (volume of pores is 40-60 %). The most important processes on collapsible/dispersive rocks and soils include sheet erosion, rill erosion, gully erosion, piping (tunnel erosion, suberosion), wind erosion and mass movements.
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