干旱区生态环境知识资源中心(Arid): Gully Erosion Susceptibility Assessment Using Different Machine Learning Algorithms: A Case Study of Shazand Watershed in Iran

Arid

DOI	10.1007/s10666-023-09910-4
	Gully Erosion Susceptibility Assessment Using Different Machine Learning Algorithms: A Case Study of Shazand Watershed in Iran
	Mohammady, Majid; Davudirad, Aliakbar
通讯作者	Mohammady, M
来源期刊	ENVIRONMENTAL MODELING & ASSESSMENT
ISSN	1420-2026
EISSN	1573-2967
出版年	2024
卷号	29 期号:2 页码:249-261
英文摘要	Soil, as a valuable natural resource, provides a large number of services and plays an important role in the environment and world economy. Soil degradation and erosion reduce the quality and quantity of the soil and are important natural and anthropogenic processes that affect many countries. Water erosion is the most common type of soil degradation in the world, and Asia has about 50% of the total water erosion area of the world. Gullies are a typical erosion type, and gully formation is an important process of soil erosion and degradation in semi-arid and arid areas, especially areas impacted by human activities and land uses. Because of arid and semi-arid climate, piping and gully erosion is an active phenomenon in the agricultural lands, bare land, and rangeland areas of the Shazand watershed, Markazi Province, central Iran. The goal of this research was to identify the priority conditioning factors of gully erosion, map the susceptibility of the Shazand watershed to gully erosion, and compare some of the applied machine learning techniques based on their accuracy. Prioritization of conditioning factors using a random forest (RF) algorithm demonstrated that distance from the roads, altitude, and rainfall has the greatest impact on gully occurrence in the Shazand watershed. The RF, boosted regression tree (BRT), functional discriminant analysis (FDA), generalized linear model (GLM), and mixture discriminant analysis (MDA) algorithms were applied to create gully erosion susceptibility maps in the study area. The receiver operating characteristic curve (ROC) and area under the curve (AUC) performance metrics were used to validate susceptibility maps. The AUC values of 0.850, 0.831, 0.760, 0.751, and 0.758 were achieved for the RF, BRT, FDA, GLM, and MDA algorithms, respectively. Due to the negative and destructive effects of gully erosion, its management and control is a critical component in the management of natural resources and land uses. Determining the importance of factors affecting erosion is very important to manage and reduce the erosion in the study area. The susceptibility maps of gully erosion prepared in this study are a substantial information resource for decision makers, planners, and engineers concerned with human impacts on natural resources and land uses. About 40% of the study area has high to very high susceptibility to the gully erosion, so control and management of this phenomenon is very important in Shazand watershed. The areas identified with high and very high erosion susceptibility in the Shazand watershed need more care to mitigate the consequences of gully erosion and soil degradation. Also, prioritizing factors will increase the focus on more important factors, and management activities will be more successful.
英文关键词	Boosted regression tree Functional discriminant analysis Gully erosion Generalized linear model Mixture discriminant analysis Random forest
类型	Article
语种	英语
开放获取类型	Green Submitted
收录类别	SCI-E
WOS记录号	WOS:001020515500001
WOS关键词	LANDSLIDE SUSCEPTIBILITY ; LOGISTIC-REGRESSION ; MANAGEMENT-PRACTICES ; SPATIAL PREDICTION ; RANDOM FOREST ; SOIL-EROSION ; REGION ; MODEL ; HIGHLANDS ; VALLEY
WOS类目	Environmental Sciences
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/2XILL650/403580
推荐引用方式 GB/T 7714	Mohammady, Majid,Davudirad, Aliakbar. Gully Erosion Susceptibility Assessment Using Different Machine Learning Algorithms: A Case Study of Shazand Watershed in Iran[J],2024,29(2):249-261.
APA	Mohammady, Majid,&Davudirad, Aliakbar.(2024).Gully Erosion Susceptibility Assessment Using Different Machine Learning Algorithms: A Case Study of Shazand Watershed in Iran.ENVIRONMENTAL MODELING & ASSESSMENT,29(2),249-261.
MLA	Mohammady, Majid,et al."Gully Erosion Susceptibility Assessment Using Different Machine Learning Algorithms: A Case Study of Shazand Watershed in Iran".ENVIRONMENTAL MODELING & ASSESSMENT 29.2(2024):249-261.