Seyed Masoud Soleimanpour; Omid Rahmati; Samad Shadfar; Maryam Enayati
Abstract
Field measurements of soil loss due to gully erosion are very time-consuming and costly, so direct measurement of gully erosion at large scales is a time-consuming, costly, and labor-intensive process. For this purpose, the present study attempted to accomplish this by modeling soil loss due to gully ...
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Field measurements of soil loss due to gully erosion are very time-consuming and costly, so direct measurement of gully erosion at large scales is a time-consuming, costly, and labor-intensive process. For this purpose, the present study attempted to accomplish this by modeling soil loss due to gully erosion using random forest and support vector machine learning models and evaluating their efficiency in the Mahurmilati watershed located in the southwest of Fars province. Field measurements of dimensional parameters of 70 gullies were conducted over four years (2021 to 2024). In the modeling process, 15 environmental factors were considered as independent variables and the rate of soil loss in ditches as the dependent variable, and modeling was performed with a cross-validation approach. The accuracy of the models was evaluated using quantitative criteria such as root mean square error (RMSE), coefficient of determination (R2), root mean square error (RSR), and correlation coefficient (d). The rate of soil loss in gullies during the study period was 15300.94 tons. The results of the model prediction accuracy evaluation showed that the random forest model has better performance than the support vector machine model in terms of evaluation criteria and was introduced as the superior model for predicting the rate of soil loss due to gully erosion. The findings showed that "modeling" can provide valuable services to water and soil conservation management in saving time and money. For this purpose, it is suggested that the use of artificial intelligence-based models and machine learning structures be given more attention in future research.
Omid Kavoosi; Khaled Ahmadaali; Aliakbar Nazari Samani
Abstract
Soil erosion and its consequences, such as soil destruction at the source, silting of rivers and filling of reservoirs of dams, are one of the most important natural hazards in watersheds, which reduce ecosystem durability. To be one of the most important practical solutions to control sedimentation ...
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Soil erosion and its consequences, such as soil destruction at the source, silting of rivers and filling of reservoirs of dams, are one of the most important natural hazards in watersheds, which reduce ecosystem durability. To be one of the most important practical solutions to control sedimentation and reduce peak flow is to build a check dam. Therefore, determining the quantitative variables affecting the volume of the structure is an important factor in determining the construction costs and their effectiveness. The present study was conducted to model checkdam volumes at the level of 100 sub-basins in different provinces of Iran (Alborz, East Azerbaijan, Ilam, Isfahan, Bushehr, Tehran, Qazvin, Fars, Mazandaran, and Hamadan). The database used for modeling includes 27 environmental features extracted in each of 100 sub-basins and the modeling was done using Genetic Expression Algorithm (GEP). The results of modeling showed that the most important characteristics in estimating the volume of checkdam among the 27 characteristics are: precipitation, temperature, TWI index, shape factor, height difference, concentration time, slope, drainage density and NDVI index. The results of estimating the volume of the structures using the nine selected variables showed that the R2, RRMSE and NSE values for the training phase are .088, .035 and 0.92, respectively, and for the test phase, they are 0.91, 0.29 and 0.91, respectively. Also, based on the results, the characteristics of environmental precipitation can be used with great accuracy to estimate the volume of sediment control structures in a short time, and therefore, before their implementation, the related costs were known in order to prioritize the areas.
Mohammad Tahmoures; davud nikkami
Abstract
Erosion and sedimentation phenomena are two inevitable phenomena of watersheds that are subject to complex factors. Identifying these factors and recognizing their effect on erosion and sediment will help in better planning to reduce the damage caused by erosion and sediment in a basin. In this study, ...
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Erosion and sedimentation phenomena are two inevitable phenomena of watersheds that are subject to complex factors. Identifying these factors and recognizing their effect on erosion and sediment will help in better planning to reduce the damage caused by erosion and sediment in a basin. In this study, to determine the factors affecting sedimentation, the Urmia Lake watershed was selected as the study basin. After identifying 30 characteristics affecting the sedimentation of sub-basins of the study area, including hydrological, physiographic, geomorphological, geological and soil characteristics, climate, land use and vegetation as independent variables, the amount of sediment produced in each sub-basin. Was identified as a dependent variable. Using factor analysis, principal component analysis (PCA), cluster analysis and stepwise multivariate regression between selected independent variables and dependent variable using SPSS software Statistical relationship was obtained between sedimentation of sub-basins and watershed characteristics. According to the selected regression model, it is determined that the amount of sediment in the watershed of Lake Urmia to five factors of agricultural land area (rainfed, irrigated and orchards), the area of sub-basins, the total area of erosion and Quaternary structures, average discharge The annual and basin form factor depends on the fact that these five factors control 89% of the sediment production changes in the selected sub-basins, which is significant at the 5% confidence level. In general, the factors affecting erosion and sedimentation of the Urmia Lake watershed can be divided into three groups: human factors and land use change, geology and physiography.
Suma Mohamadpur; Hamed Rouhani; Hojat Ghorbani Vaghei; Seyed Morteza Seyedian; Abulhasan Fath Abadi
Abstract
In many semi-arid regions of Iran, soil erosion has turned into a serious environmental problem affecting land productivity, nutrient loss, water quality, and fresh water ecosystems. Rates of soil loss differ according to erosion type and land degradation processes. Rill erosion is commonly observed ...
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In many semi-arid regions of Iran, soil erosion has turned into a serious environmental problem affecting land productivity, nutrient loss, water quality, and fresh water ecosystems. Rates of soil loss differ according to erosion type and land degradation processes. Rill erosion is commonly observed when rainstorms occur on steep slopes and sediment transport in rill flows exhibits the characteristics of non-equilibrium transport. In this paper, sediment concentration of rill flow is estimated by adaptive neuro-fuzzy inference system (ANFIS). A series of mathematical equations and parameters affecting rill hydrodynamics and soil detachment were used for well-defined rill sediment concentration. A series of filed experiments were performed to evaluate the model. The stepwise method was used to select the most important and effective input variables from measured input parameters of soil properties, topographic and vegetation attributes affecting sediment concentration of rill flow. Based on the stepwise procedure, the most significant parameters in the model predications were steep slope, vegetation percentage, clay percentage, and shear stress parameters. The values of sediment concentration simulated by the model were in agreement with observed values with Coefficient of Correlation (R2), Root Mean Square Error (RMSE) and Mean Bias Error (MBE) of 0.697, 30.5 and 1.0, respectively. The results of the investigation shows that the data-driven ANFIS modeling approach can be a powerful alternative technique for correctly estimating rill sediment concentration.
