نوع مقاله : پژوهشی
نویسندگان
1 دانشیار بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان فارس، سازمان تحقیقات، آموزش و ترویج کشاورزی، شیراز، ایران
2 استادیار بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان کردستان، سازمان تحقیقات، آموزش و ترویج کشاورزی، سنندج، ایران
3 دانشیار پژوهشکده حفاظت خاک و آبخیزداری، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران ایران
4 کارشناس ارشد بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان فارس، سازمان تحقیقات، آموزش و ترویج کشاورزی، شیراز، ایران
چکیده
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Introduction and Goal
Water erosion is also one of the most important factors in land degradation. Among the different types of water erosion, gully erosion is a very obvious and prominent type of soil erosion and is one of the most important challenges threatening food production, human health, and the ecosystem. Since the amount of soil loss due to gully erosion is directly related to environmental factors, it is possible to model the amount of soil loss due to gullies based on environmental conditions. Field measurement of the amount of soil loss due to gully erosion is very time-consuming and costly, and direct measurement of gully erosion on a large scale is not possible. For this purpose, these study, evaluated the efficiency of support vector machine (SVM) and artificial neural network (ANN) models in modeling soil loss due to gully erosion in the Mahurmilati watershed located in the southwest of Fars province.
Materials and Methods
In field visits, the geographical location of all gullies located in this watershed was recorded using a GPS device, and after matching with Google Earth satellite images, these points were transferred to the GIS and a gully distribution map was drawn. Field measurements of the dimensional parameters of 70 gullies were carried out over four years (2021-2024), including: gully length, upper width, lower width, and gully depth, and the volume and weight of soil lost due to gully erosion were also calculated. 15 environmental factors were selected as independent variables (predictor variables) for the modeling process. In the modeling process, environmental factors were considered as independent variables, and the rate of soil loss in gullies was considered as a dependent variable. The gullies were randomly divided into two groups: training (70%) and validation (30%) and modeling was performed using two models: support vector machine and artificial neural network, with a cross-validation approach. The accuracy of the models was evaluated using quantitative criteria such as root mean square error (RMSE), determination coefficient (R2), RSR error index, and compliance index (D).
Results and Discussion
The results of the prediction accuracy evaluation of the models showed that in terms of the root mean square error (RMSE) criterion, the support vector machine model had the lowest error, followed by the artificial neural network model in second place. Based on the evaluation criterion of the coefficient of determination (R2), the support vector machine model (R2=0.41-0.59) was in first place and the artificial neural network model (R2=0.21-0.34) was in second place. In terms of the evaluation criterion of the RSR error index, the artificial neural network model was in first place and the support vector machine model was in second place. In terms of the conformity evaluation index (d), the support vector machine model had the highest conformity (0.81) between the observational and forecast data and had the highest value of this index, and the artificial neural network model was in second place with a conformity index value of 0.63. Therefore, in this study, since the support vector machine model had better performance than the artificial neural network model in terms of evaluation criteria of root mean square error (RMSE), explanation index, and agreement index, it was introduced as the superior model for predicting the amount of soil loss due to gully erosion in the Mahurmilati watershed of Fars province.
Conclusion and Suggestions
When the performance of a model is evaluated to simulate a phenomenon, several factors play a role in this issue that must be considered. In this study, 15 environmental variables were used to predict soil loss due to gully erosion; these factors were also able to provide very important information in terms of spatial and temporal changes and watershed characteristics for the model. In this study, the monitoring period was not long; but the two models used were able to provide appropriate performance for predicting soil loss due to gully erosion. Also, one of the important advantages of this study is that the two models used were selected from among the most efficient artificial intelligence models; so that by considering factors that are variable in "time", they provide the conditions for prediction in the future and there is no need for field re-measurements in the coming years. In fact, the model can predict the amount of soil loss caused by gullies in subsequent years based on the learnings gained in the training phase, taking into account new rainfall and vegetation information. This is the most important distinguishing feature of this research and shows that modeling can provide valuable services to the country's water and soil conservation management by saving time and money. For this purpose, it is suggested that the use of models based on artificial intelligence and machine learning structures be given more attention in future research.
کلیدواژه‌ها [English]
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