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Showing 3 results for Khazaei
Impacts of Test Conditions, Soil Organic Matter, Clay and Calcium Carbonate Contents on Mean Weight Diameter and Tensile Strength of Aggregates of Some Hamedan Soils
A. Khazaei, M.r. Mosaddeghi, A.a. Mahboubi,
Volume 12, Issue 44 (summer 2008)
Abstract
Soil physical and chemical properties, and test conditions might affect soil structural stability. In this study, the effects of test conditions as well as intrinsic soil properties on structural stability were investigated for selected soils from Hamedan Province. Mean weight diameter (MWD) and tensile strength (Y) of aggregates were determined by wet sieving method and indirect Brazilian test, respectively. The soil samples were pre-wetted slowly to matric suction of 200 kPa before the wet sieving. The pre-wetted samples were wet-sieved for 5, 10 and 15 min in order to simulate different hydro-mechanical stresses imposed on soil structure. Tensile strength of soil aggregates were also measured at air-dry and 500 kPa matric suction conditions. Short duration shaking (i.e. 5 min) could effectively discriminate the Hamedan soils in terms of structural stability due to their fairly low aggregate stabilities. The soil organic matter content had the highest impact on MWD followed by both clay and CaCO3 content. The same was true for the Y values i.e. OM played the highest role in mechanical strength of soil aggregates. The highest coefficient of determination (R2) was obtained between Y and the intrinsic soil properties for matric suction of 500 kPa. The organic matter content had an important role in water and mechanically stable soil aggregates. The results indicated that short-duration wet sieving (i.e. 5 min) and measurements of tensile strength at matric suction of 500 kPa could be recommended for aggregate stability assessment in Hamedan soils
Study of Groundwater Table Variations and Drought Monitoring Using GRI Index in Shiraz Plain (Maharloo-Bakhtegan Watershed)
I. Saleh, S. Zandifar, M. Khazaei,
Volume 29, Issue 2 (Summer 2025)
Abstract
Groundwater resources are affected by long-term drought conditions and have received less attention than other issues. The current research was carried out to investigate and zone the quantitative fluctuations of groundwater as well as the temporal analysis of groundwater drought using GRI in the study area of Shiraz in the Maharloo-Bakhtegan watershed. The zoning of groundwater table variations was done in the ArcGIS environment, and a representative hydrograph of the aquifer was prepared using 15-year data (2003-2018) of groundwater resources divided into three five-year periods. Also, the drought of the groundwater resources of the studied plain was investigated using the GRI index. According to the results, the highest level of the groundwater table is related to the northwestern area of the plain by 1810.1 m in October 2007, and the lowest water table was observed in the southern study area with the amount of 1423.6 m in October 2017. Also, the results showed that the groundwater table faced a drop of 6 m and an average annual drop of 0.5 m during the studied 15 years. The volume changes of the reservoir also indicated that, in addition to consuming the entire renewable reserve, a large part of the fixed reserve has also been exploited in the past years. The descending trend of GRI and its intensification in the last years of the studied period is one of the most important results of this research, which occurred due to population growth and increasing cultivated area, a decrease in precipitation, and climate change.

Estimation of Soil Loss Volume Caused by Gully Erosion Using Machine Learning Models in Abgendi Watershed
I. Saleh, S. M. Soleimanpour, M. Khazaei, O. Rahmati, S. Shadfar,
Volume 29, Issue 4 (Winter 2025)
Abstract
Soil loss and extensive degradation caused by gully erosion have always caused serious damage. Because direct field measurement and monitoring of gully erosion are costly and time-consuming, it is very difficult to determine the amount of soil loss caused by gully erosion. The present research was conducted to calculate the volume of soil loss due to gully erosion using machine learning models in the Abgendi watershed of Kohgiluyeh and Boyar Ahmad province based on field studies. Machine learning models include random forest, support vector machine, artificial neural network, and adaptive neural fuzzy inference system. The location of 68 gullies in the area was recorded. Hence, initially, digital layers of factors affecting the expansion of gullies, including topography, pedology, lithology, and hydrology, were prepared as independent variables to model soil loss caused by gullies. Then, representative gullies were selected in the studied watershed, and the volume of soil loss due to gully erosion was directly measured in the field as a dependent variable. The measured gullies were randomly divided into two training and validation groups. The results of the models were evaluated using root mean square error (RMSE) and R2, and the models were compared. According to the results, gully erosion in the Abgendi watershed of Kohgiluyeh and Boyar Ahmad province is increasing every year. Also, the amount of erosion and soil loss will increase when the amount of rainfall and the frequency of intense rainfall (≥5mm) are high. Among the machine learning models used in the present research, the random forest (RF) model was selected as the best model to predict soil loss generated by gully erosion.

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