Chapter 18 - Prediction of soil erosion using machine learning

Abstract

Soil erosion carries significant economic, social, and political ramifications, particularly in arid and semiarid regions. Recognizing the gravity of this issue, the primary objective of this study is to utilize machine learning methods for predicting soil erosion. To achieve this goal, the topographic position index (TPI) method was initially employed to identify landforms and assess their relationship with erosion rates. Subsequently, the study employed the linear regression method to ascertain the correlation between erosion rate and influential factors such as topography, soil, and vegetation characteristics. Furthermore, support vector machines (SVMs) and random forest methods were utilized to predict soil erosion patterns, utilizing a comprehensive dataset encompassing drainage density (Dd), elevation, slope length (LS), topographic wetness index (TWI), slope, soil type, stream power index (SPI), and normalized difference vegetation index (NDVI). TPI findings revealed that small plains exhibited the highest erosion within the study area. Regression analysis demonstrated that parameters such as digital elevation model (DEM) (R2=−0.92), NDVI (R2=−0.94), TWI (R2=−0.83), and SPI (R2=−0.92) displayed the strongest correlation with soil erosion. Additionally, SVM method exhibited higher accuracy (R2=0.89) compared to the random forest method in predicting soil erosion. These findings underscore the efficacy of the SVM method in studying soil erosion.