Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method DOI Creative Commons

Jinxi Su,

Rong Tang,

Huilong Lin

et al.

Land, Journal Year: 2024, Volume and Issue: 13(9), P. 1456 - 1456

Published: Sept. 7, 2024

The source region of the Yellow River (SRYR), known as “Chinese Water Tower”, is currently grappling with severe soil erosion, which jeopardizes sustainability its alpine grasslands. Large-scale erosion monitoring poses a significant challenge, complicating global efforts to study and land cover changes. Moreover, conventional methods for assessing do not adequately address variety types present in SRYR. Given these challenges, objectives this were develop suitable assessment prediction model tailored SRYR’s needs. By leveraging data measured by 137Cs from 521 locations employing random forest (RF) algorithm, new was formulated. Key findings include that: (1) RF significantly outperformed revised universal loss equation (RUSLE) wind (RWEQ) model, achieving an R2 0.52 RMSE 5.88. (2) indicated that 2001 2020, SRYR experienced average annual modulus (SEM) 19.32 t·ha−1·y−1 total 225.18 × 106 t·y−1. Spatial analysis revealed 78.64% suffered low intensity declining northwest southeast. (3) SEM demonstrated downward trend 83.43% area showing improvement. Based on findings, measures prevention control proposed. Future studies should refine temporal better understand influence extreme climate events while high-resolution enhance accuracy. Insights into drivers will support more effective policy development.

Language: Английский

Gully transforms the loss pattern of runoff, sediment, nitrogen, and phosphorus in agricultural catchment of Northeast China DOI
Zhuoxin Chen, Mingming Guo, Xin Liu

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133503 - 133503

Published: May 1, 2025

Language: Английский

Citations

0
Simulation and Spatio-Temporal Analysis of Soil Erosion in the Source Region of the Yellow River Using Machine Learning Method DOI Creative Commons

Jinxi Su,

Rong Tang,

Huilong Lin

et al.

Land, Journal Year: 2024, Volume and Issue: 13(9), P. 1456 - 1456

Published: Sept. 7, 2024

The source region of the Yellow River (SRYR), known as “Chinese Water Tower”, is currently grappling with severe soil erosion, which jeopardizes sustainability its alpine grasslands. Large-scale erosion monitoring poses a significant challenge, complicating global efforts to study and land cover changes. Moreover, conventional methods for assessing do not adequately address variety types present in SRYR. Given these challenges, objectives this were develop suitable assessment prediction model tailored SRYR’s needs. By leveraging data measured by 137Cs from 521 locations employing random forest (RF) algorithm, new was formulated. Key findings include that: (1) RF significantly outperformed revised universal loss equation (RUSLE) wind (RWEQ) model, achieving an R2 0.52 RMSE 5.88. (2) indicated that 2001 2020, SRYR experienced average annual modulus (SEM) 19.32 t·ha−1·y−1 total 225.18 × 106 t·y−1. Spatial analysis revealed 78.64% suffered low intensity declining northwest southeast. (3) SEM demonstrated downward trend 83.43% area showing improvement. Based on findings, measures prevention control proposed. Future studies should refine temporal better understand influence extreme climate events while high-resolution enhance accuracy. Insights into drivers will support more effective policy development.

Language: Английский

Citations

1