干旱区生态环境知识资源中心(Arid): Spatial variability of clay minerals in a semi-arid region of Turkiye

Arid

DOI	10.1016/j.geodrs.2024.e00820
	Spatial variability of clay minerals in a semi-arid region of Turkiye
	Gunal, Hikmet; Acir, Nurullah
通讯作者	Günal, H
来源期刊	GEODERMA REGIONAL
ISSN	2352-0094
出版年	2024
卷号	38
英文摘要	Clay minerals are the source of many chemical and physical properties that influence the provision of soil-based ecosystem services. This study aimed to identify the most significant soil characteristics contributing to the spatial variability of clay minerals in a semi-arid region of Turkiye. Additionally, the study assessed the predictive capabilities of Classification and Regression Tree (CART), Random Forest Regression (RF) and eXtreme Gradient Boosting Regression (XGBoost) in estimating soil clay mineral content. Smectite +vermiculite (SMVR) was the most abundant clay mineral in the study area, followed by illite and kaolinite. Hyperparameter tuning significantly improved model accuracy, with root mean square error (RMSE) reductions ranging from 2.53% to 97.3%. The machine learning algorithms demonstrated varying performances in spatial prediction accuracy. The RF model achieved the lowest RMSE (8.587%) and the highest R 2 values (0.796) for predicting SMVR. The XGBoost outperformed other models for kaolinite (RMSE: 4.814%, R 2 :0.713) and illite (RMSE:7.368%, R 2 :0.613). Exchangeable cations, particularly magnesium (Mg) and calcium (Ca), were identified as crucial factors influencing the spatial distribution of clay minerals. Among these, M concentration had the strongest influence on predicting both SMVR (38.1%) and illite (26.3%). Conversely, for kaolinite prediction, Ca concentration played the most significant role (38.7%), followed by Mg (19.93%). In conclusion, this study demonstrates the effectiveness of machine learning models, particularly XGBoost which achieved the lowest RMSE for all clay minerals investigated. These models offer a valuable tool for predicting clay mineral content in the Kazova Plain. The findings highlight the importance of parent material, weathering processes, and specific soil properties, such as exchangeable cations, in shaping clay mineral distribution. This knowledge not only contributes to a deeper understanding of soil formation in semi-arid environments but also practical applications. For instance, by predicting the abundance of SVMR, known for its high cation exchange capacity land managers can develop targeted strategies for optimizing fertilizer application in the Kazova Plain.
英文关键词	Clay mineral Covariate Modeling Spatial distribution Prediction Artificial intelligence
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001261161000001
WOS关键词	SOIL ORGANIC-CARBON ; LAND-USE ; PREDICTION
WOS类目	Soil Science
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403931
推荐引用方式 GB/T 7714	Gunal, Hikmet,Acir, Nurullah. Spatial variability of clay minerals in a semi-arid region of Turkiye[J],2024,38.
APA	Gunal, Hikmet,&Acir, Nurullah.(2024).Spatial variability of clay minerals in a semi-arid region of Turkiye.GEODERMA REGIONAL,38.
MLA	Gunal, Hikmet,et al."Spatial variability of clay minerals in a semi-arid region of Turkiye".GEODERMA REGIONAL 38(2024).