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.
Nayereh Ghazanfarpour; Sadat Feiznia; Hassan Ahmadi; Mohammad Jafari; Masoud Nasri
Abstract
Estimation of the amount of soil loss measurement of field indicators is a low-cost method, is easy to learn and can be simply applied. In order to measure and make assessment of soil loss amount by field indicators method in Shahrak Watershed, first map of the work units was prepared and then, measurements ...
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Estimation of the amount of soil loss measurement of field indicators is a low-cost method, is easy to learn and can be simply applied. In order to measure and make assessment of soil loss amount by field indicators method in Shahrak Watershed, first map of the work units was prepared and then, measurements of the relevant field indicators were carried out within these units. Then, EPM model was calibrated and verified. Then the measured data for the amount of the soil loss obtained by using field indicators were analysed and assessed using EPM experimental model. Mean of relative error and correlation coefficient between values from filed indicators method and EPM model were around 7.6 and 0.9, respectively which these results verify field indicators method for estimation of soil loss intensity. Estimation of the average of soil loss amounts relevant to each of the field indicators shows the following soil losses: Rock exposure indicator: 47.61 (ton/ha), Pedestal indicator: 22.61 (ton/ha), Rill indicator: 5.67 (ton/ha), Sediment in drains indicator: 2.21 (ton/ha), Gully indicator: 2.17 (ton/ha) and Build up against barriers indicator: 34.78 (ton/ha).
