AUTHOR=Lin Huilong , Zhao Yuting TITLE=Soil Erosion Assessment of Alpine Grassland in the Source Park of the Yellow River on the Qinghai-Tibetan Plateau, China JOURNAL=Frontiers in Ecology and Evolution VOLUME=9 YEAR=2022 URL=https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2021.771439 DOI=10.3389/fevo.2021.771439 ISSN=2296-701X ABSTRACT=
The source park of the Yellow River (SPYR), as a vital ecological shelter on the Qinghai-Tibetan Plateau, is suffering different degrees of degradation and desertification, resulting in soil erosion in recent decades. Therefore, studying the mechanism, influencing factors and current situation of soil erosion in the alpine grassland ecosystems of the SPYR are significant for protecting the ecological and productive functions. Based on the 137Cs element tracing technique and machine learning algorithms, five strategic variable selection algorithms based on machine learning algorithms are used to identify the minimal optimal set and analyze the main factors that influence soil erosion in the SPYR. The optimal model for estimating soil erosion in the SPYR is obtained by comparisons model outputs between the RUSLE and machine learning algorithms combined with variable selection models. We identify the spatial distribution pattern of soil erosion in the study area by the optimal model. The results indicated that: (1) A comprehensive set of variables is more objective than the RUSLE model. In terms of verification accuracy, the simulated annealing -Cubist model (