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.
Seyed Masoud Soleimanpour; Bahram Hedayati; Majid Soufi; Mohammad Javad Rousta; Samad Shadfar
Abstract
One of the important relations in the erosion of gullies is to study the threshold of erosion creation and expansion. In recent decade, creation of new knowledge in determination of relation between variables was led to develop prediction methods in different science and therefore, investigating the ...
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One of the important relations in the erosion of gullies is to study the threshold of erosion creation and expansion. In recent decade, creation of new knowledge in determination of relation between variables was led to develop prediction methods in different science and therefore, investigating the ability to use these methods in erosion and soil conservation is essential. Also, in order to control the erosion of the gully, the mechanism of gullies growth and its dimension expansion, especially increasing in gullies length, has to be carefully determine; for this purpose, the present study aimed to determine the threshold of the most effective factors on increasing the length of the gully, using the K-Means data mining algorithms and the CART decision tree in the Ghazian watershed in the north of Fars province. The results of this study, which include measuring various variables of gullies under field condition and in laboratory, and using data mining techniques, showed that increasing the length of gully in this area depended on the factors of the area above headcut, saturated extract electrical conductivity, forehead slope, canopy cover percentage, and sodium adsorption ratio. It is recommended control of erosion in the foreheads is highly important in reducing the increase in gullies length and sediment production. Also, improving the soils of this area with soil amendments and the restoration of compatible vegetation and the increase in soil organic matter should be considered as the priority of effective actions to control the increasing length of gullies.
