Modeling soil loss under rainfall events using machine learning algorithms

Yulan Chen; Jianjun Li; Ziqi Zhang; Juying Jiao; Nan Wang; Leichao Bai; Yue Liang; Qian Xu; Shijie Zhang

doi:10.1016/j.jenvman.2023.120004

Modeling soil loss under rainfall events using machine learning algorithms

J Environ Manage. 2024 Feb 14:352:120004. doi: 10.1016/j.jenvman.2023.120004. Epub 2024 Jan 12.

Authors

Yulan Chen¹, Jianjun Li², Ziqi Zhang², Juying Jiao³, Nan Wang⁴, Leichao Bai⁵, Yue Liang⁶, Qian Xu², Shijie Zhang⁷

Affiliations

¹ The Research Center of Soil and Water Conservation and Ecological Environment, Chinese Academy of Sciences and Ministry of Education, Yangling, Shaanxi, 712100, China; Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi, 712100, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
² Institute of Soil and Water Conservation, Northwest A＆ F University, Yangling, Shaanxi, 712100, China.
³ The Research Center of Soil and Water Conservation and Ecological Environment, Chinese Academy of Sciences and Ministry of Education, Yangling, Shaanxi, 712100, China; Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi, 712100, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Institute of Soil and Water Conservation, Northwest A＆ F University, Yangling, Shaanxi, 712100, China. Electronic address: jyjiao@ms.iswc.ac.cn.
⁴ The Research Center of Soil and Water Conservation and Ecological Environment, Chinese Academy of Sciences and Ministry of Education, Yangling, Shaanxi, 712100, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
⁵ Institute of Soil and Water Conservation, Northwest A＆ F University, Yangling, Shaanxi, 712100, China; School of Geographical Sciences, China West Normal University, Nanchong, 637009, China.
⁶ University of Chinese Academy of Sciences, Beijing, 100049, China; State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 100048, China.
⁷ Institute of Soil and Water Conservation, Northwest A＆ F University, Yangling, Shaanxi, 712100, China; Anhui and Huaihe River Institute of Hydraulic Research, Hefei, 230088, China.

PMID: 38218170
DOI: 10.1016/j.jenvman.2023.120004

Abstract

Soil loss is an environmental concern of global importance. Accurate simulation of soil loss in small watersheds is crucial for protecting the environment and implementing soil and water conservation measures. However, predicting soil loss while meeting the criteria of high precision, efficiency, and generalizability remains a challenge. Therefore, this study first used three machine learning (ML) algorithms, namely, random forest (RF), support vector machine (SVM), and artificial neural network (ANN) to develop soil loss models and predict soil loss rates (SLRs). These soil loss models were constructed using field observation data with an average SLR of 1756.48 t/km² from rainfall events and small watersheds in the hilly-gully region of the Loess Plateau, China. During training, testing and generalizability stages, the average coefficients of determination from the RF, SVM, and ANN models were 0.903, 0.860, and 0.836, respectively. Similarly, the average Nash-Sutcliffe coefficients of efficiency from the RF, SVM and ANN models were 0.893, 0.791 and 0.814, respectively. These results indicated that MLs have superior predictive performance and generalizability, and broad prospects for predicting SLRs. This study also demonstrated that the RF model outperformed better than the SVM and ANN models. Therefore, the RF model was used to simulate the SLR of each small watershed in the Chabagou watershed. Our results showed the four-year (2017-2020) average annual SLR of the small watersheds ranged from 0.73 to 1.63 × 10⁴ t/(km²∙a) in the Chabagou watershed. Additionally, the results also indicated the SLR of small watersheds under the rainstorm event with a 100-year recurrence interval was 4.4-51.3 times that of other rainfall events.Furthermore, this study confirmed that bare land was the predominant source of soil loss in the Chabagou watershed, followed by cropland land and grassland. This study helps to provide the theoretical basis for deploying soil and water conservation measures to realize the sustainable utilization of soil resources in the future.

Keywords: Artificial neural network; Loess plateau; Random forest; Soil loss model; Support vector machine.

MeSH terms

Algorithms
China
Conservation of Water Resources*
Machine Learning
Soil*

Substances

Soil