Related Experiment Video
Updated: Jan 25, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
Published on: November 1, 2019
Evaluation of factors affecting gully headcut location using summary statistics and the maximum entropy model:
Narges Kariminejad1, Mohsen Hosseinalizadeh1, Hamid Reza Pourghasemi2
1Department of Watershed and Arid Zone Management, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan 49189-434, Iran.
Abstract:
Gully erosion is an important soil degradation process, which under climate changes is projected to increase. Therefore, better understating of factors controlling gully erosion and prediction of gully headcuts' (GHs) location is still highly relevant. This study aimed to examine the spatial distribution of GHs and to assess the importance of pedological (i.e. aggregate stability, organic matter, bulk density, silt, clay, and sand content) and topographical factors (i.e. altitude, slope length, gradient, and aspect) using summary statistics and the maximum entropy (MaxEnt) model. The study was conducted in the loess-covered region of NE Iran. The highly precise data of 287 GHs locations were obtained by extensive fieldwork and the interpretation of UAV images. The spatial distribution of GHs was evaluated by univariate pair correlation function and O-ring statistics. The spatial effect of GHs density controlling factors was assessed by the cumulative density correlation function Cm,K(r). Variable importance was analyzed using the MaxEnt model, which was also for the susceptibility modelling of GHs. The results of univariate tests showed the aggregated distribution of GHs. The Cm,K(r) analyses indicated that the areas characterized by higher values of bulk density, aggregate stability, and organic matter content have lower GHs density, whereas the areas with high silt content and higher slope gradient have higher GHs density. According to the MaxEnt, there is no one single factor responsible for GHs location, but rather the combination of topographical and pedological factors with the predominance of slope gradient (0.86) and silt content (0.57). The MaxEnt modelling of GHs susceptibility has revealed that the best accuracy (0.958) is given when all pedological and topographical factors are used in the model. The susceptibility maps prepared in the study can be used for soil conversation and land use planning and, consequently, for sustainable development in the region.
More Related Videos
10:24Evaluation of Intracellular Location of Reactive Oxygen Species in Solea Senegalensis Spermatozoa
Published on: March 11, 2018
09:12Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method
Published on: May 19, 2023
Related Concept Videos
Entropy
Entropy
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
Standard Entropy Change for a Reaction
5-Number Summary
In a box plot, the minimum and maximum data values represent the lower and upper whiskers in the graph, and the median is designated as the center of the box in the chart. The first quartile and third...
Discharge Summary Forms
Here's a detailed look at the key components and guidelines for preparing a discharge summary:
Statistical Significance