Modeling soil loss due to gully erosion using random forest and support vector machine models and evaluating their efficiency

Soleimanpour, Seyed Masoud; Rahmati, Omid; Shadfar, Samad; Enayati, Maryam

doi:10.22059/jrwm.2025.390477.1804

Document Type : Research Paper

Authors

¹ Soil Conservation and Watershed Management Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Iran

² Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Sanandaj, Iran

³ Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran

10.22059/jrwm.2025.390477.1804

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 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.

Keywords

References

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Journal of Range and Watershed Managment

Modeling soil loss due to gully erosion using random forest and support vector machine models and evaluating their efficiency

References

References

Volume 78, Issue 3
September 2025
Pages 363-383

Modeling soil loss due to gully erosion using random forest and support vector machine models and evaluating their efficiency

References

References

Volume 78, Issue 3September 2025Pages 363-383

Volume 78, Issue 3
September 2025
Pages 363-383