Prediction of Wind Erosion Threshold Velocity Using Portable Wind Tunnel Combined with Machine Learning Algorithms

Document Type : Research Paper

Authors

1 Department of Soil Science and Engineering, College of Agriculture, Shiraz University, Shiraz, Iran

2 Department of Soil Science and Engineering. College of Agriculture. Shiraz University.Shiraz.Iran

3 Department of Computer Science and Information Technology, Faculty of Engineering, Hormozgan University, Bandar abbas, Iran

Abstract

Wind erosion is a key process in land degradation worldwide, especially in arid and semi-arid regions of Iran. This phenomenon is affected by many soil characteristics. The main objective of this study was to estimate the wind erosion threshold velocity using easily measurable soil characteristics along with data mining methods. For this purpose, wind erosion threshold velocity was measured in 100 areas in Fars province using a portable wind tunnel. Wind erosion threshold velocity was predicted by a support vector regression algorithm using easily measurable soil properties. In this regard, a genetic algorithm was used in order to obtain a set of parameters effective in estimating wind erosion threshold velocity. The results showed that the characteristics of soil moisture (r = 0.77), the size distribution of soil particles including the mean weight diameter of aggregate (r = 0.87) and the wind-erodible fraction of soils (r = -0.81), penetration resistance (r = 0.75), and organic matter (r = 0.33) have a high and significant correlation with wind erosion threshold velocity and play a key role in determining the threshold velocity of wind erosion in the region. According to the evaluation criteria, the combined support vector regression model with the genetic algorithm had the best performance and the most accurate estimate for wind erosion threshold velocity (RMSE = 0.53 and R2 = 0.92) and can be a promising method for estimation of wind erosion threshold velocity.

Keywords

Main Subjects

Prediction of Wind Erosion Threshold Velocity Using Portable Wind Tunnel Combined with Machine Learning Algorithms

EXTENDED ABSTRACT

 

Introduction

Wind erosion is a key process in land degradation worldwide, especially in arid and semi-arid regions. This phenomenon is affected by many soil characteristics. The main objective of the present study was to estimate the wind erosion threshold velocity using easily measurable soil characteristics along with data mining methods. Therefore, the present study aimed 1) to measure the wind erosion threshold velocity by extensive wind tunnel tests; 2) to predict the wind erosion threshold velocity using machine learning algorithms; 3) to determine features affecting the wind erosion threshold velocity using genetic algorithm coupled with machine learning algorithms in the prediction of wind erosion threshold velocity.

 

Materials and Methods

For this purpose, wind erosion threshold velocity was measured in 100 areas in Fars province using a portable wind tunnel. Fars province is located in the southwest region of Iran. Due to the climate conditions, wind erosion occurs in most parts of Fars province. Thirty critical sources of wind erosion with an area of 123,500 ha have been identified in Fars province. Therefore, investigation of the wind erosion threshold velocity in Fras province is vital for guiding the decision-makers to implement conservative practices against wind erosion. Wind erosion threshold velocity was predicted by a support vector regression algorithm using easily measurable soil properties. In this regard, a genetic algorithm was used in order to obtain a set of influential parameters in estimating wind erosion threshold velocity. In this study, the algorithms were provided in MATLAB 2019 programming software packages.

 

Results and Discussion

The results showed that with respect to sand, silt, and clay content, there are a variety of different soil textures according to USDA classification in the study area. The majority of soils had a medium texture class. The results showed that the characteristics of soil moisture (r = 0.77), the size distribution of soil particles including the mean weight diameter of aggregates (r = 0.87) and the wind-erodible fraction of soils (r = -0.81), penetration resistance (r = 0.75), and organic matter (r = 0.33) have a high and significant correlation with wind erosion threshold velocity and play a key role in determining the threshold velocity of wind erosion in the region. According to the evaluation criteria, the combined support vector regression model with the genetic algorithm had the best performance and the most accurate estimate for wind erosion threshold velocity (RMSE = 0.53 and R2 = 0.92) and can be a promising method for the estimation of wind erosion threshold velocity.

Conclusion

The threshold velocity of wind erosion is a very important parameter for studying soil wind erosion potential in arid and semi-arid regions. Therefore, this study was conducted to determine the threshold velocity of wind erosion using a portable wind tunnel and to predict the threshold velocity of wind erosion using easily measurable soil properties with support vector regression. The results of this study can be helpful in efficiently assessing vast areas prone to wind erosion and dust emission and can help policymakers to prioritize regions for soil conservation practices. The latter is very important for developing countries where only a limited budget is available for soil conservation programmers. In addition, portable wind tunnel is not always available for wind erosion studies, such fast and easy-to-apply methods introduced in this study can be a good alternative for wind erosion monitoring without disturbing the soil. We suggest that using other data mining approaches (e.g., random forest and artificial neural networks) for predicting the threshold velocity of wind erosion in future studies.

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Iranian Journal of Soil and Water Research
Volume 54, Issue 6
September 2023
Pages 933-947

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