干旱区生态环境知识资源中心(Arid): An artificial intelligence-based assessment of soil erosion probability indices and contributing factors in the Abha-Khamis watershed, Saudi Arabia

Arid

DOI	10.3389/fevo.2023.1189184
	An artificial intelligence-based assessment of soil erosion probability indices and contributing factors in the Abha-Khamis watershed, Saudi Arabia
	Alqadhi, Saeed; Mallick, Javed; Talukdar, Swapan; Alkahtani, Meshel
通讯作者	Mallick, J
来源期刊	FRONTIERS IN ECOLOGY AND EVOLUTION
ISSN	2296-701X
出版年	2023
卷号	11
英文摘要	Soil erosion is a major problem in arid regions, including the Abha-Khamis watershed in Saudi Arabia. This research aimed to identify the soil erosional probability using various soil erodibility indices, including clay ratio (CR), modified clay ratio (MCR), Critical Level of Soil Organic Matter (CLOM), and principle component analysis based soil erodibility index (SEI). To achieve these objectives, the study used t-tests and an artificial neural network (ANN) model to identify the best SEI model for soil erosion management. The performance of the models were then evaluated using R-2, Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE), with CLOM identified as the best model for predicting soil erodibility. Additionally, the study used Shapley additive explanations (SHAP) values to identify influential parameters for soil erosion, including sand, clay, silt, soil organic carbon (SOC), moisture, and void ratio. This information can help to develop management strategies oriented to these parameters, which will help prevent soil erosion. The research showed notable distinctions between CR and CLOM, where the 25-27% contribution explained over 89% of the overall diversity. The MCR indicated that 70% of the study area had low erodibility, while 20% had moderate and 10% had high erodibility. CLOM showed a range from low to high erodibility, with 40% of soil showing low CLOM, 40% moderate, and 20% high. Based on the T-test results, CR is significantly different from CLOM, MCR, and principal component analysis (PCA), while CLOM is significantly different from MCR and PCA, and MCR is significantly different from PCA. The ANN implementation demonstrated that the CLOM model had the highest accuracy (R-2 of 0.95 for training and 0.92 for testing) for predicting soil erodibility, with SOC, sand, moisture, and void ratio being the most important variables. The SHAP analysis confirmed the importance of these variables for each of the four ANN models. This research provides valuable information for soil erosion management in arid regions. The identification of soil erosional probability and influential parameters will help to develop effective management strategies to prevent soil erosion and promote agricultural production. This research can be used by policymakers and stakeholders to make informed decisions to manage and prevent soil erosion.
英文关键词	soil erodibility index (SEI) soil erosion principle component analysis (PCA) artificial neural network SHAP
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001008199400001
WOS关键词	BIOLOGICAL-ACTIVITY ; PRODUCTION SYSTEMS ; TILLAGE IMPACTS ; QUALITY ; DEGRADATION ; INDICATORS ; CARBON ; AGRICULTURE ; MANAGEMENT ; CONVERSION
WOS类目	Ecology
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396492
推荐引用方式 GB/T 7714	Alqadhi, Saeed,Mallick, Javed,Talukdar, Swapan,et al. An artificial intelligence-based assessment of soil erosion probability indices and contributing factors in the Abha-Khamis watershed, Saudi Arabia[J],2023,11.
APA	Alqadhi, Saeed,Mallick, Javed,Talukdar, Swapan,&Alkahtani, Meshel.(2023).An artificial intelligence-based assessment of soil erosion probability indices and contributing factors in the Abha-Khamis watershed, Saudi Arabia.FRONTIERS IN ECOLOGY AND EVOLUTION,11.
MLA	Alqadhi, Saeed,et al."An artificial intelligence-based assessment of soil erosion probability indices and contributing factors in the Abha-Khamis watershed, Saudi Arabia".FRONTIERS IN ECOLOGY AND EVOLUTION 11(2023).