干旱区生态环境知识资源中心(Arid): Gully Head-Cut Distribution Modeling Using Machine Learning Methods-A Case Study of NW Iran

Arid

DOI	10.3390/w12010016
	Gully Head-Cut Distribution Modeling Using Machine Learning Methods-A Case Study of NW Iran
	Arabameri, Alireza 1; Chen, Wei 2,3,4; Blaschke, Thomas 5; Tiefenbacher, John P.6; Pradhan, Biswajeet 7,8; Dieu Tien Bui 9
通讯作者	Arabameri, Alireza ; Dieu Tien Bui
来源期刊	WATER
EISSN	2073-4441
出版年	2020
卷号	12 期号:1
英文摘要	To more effectively prevent and manage the scourge of gully erosion in arid and semi-arid regions, we present a novel-ensemble intelligence approach-bagging-based alternating decision-tree classifier (bagging-ADTree)-and use it to model a landscape's susceptibility to gully erosion based on 18 gully-erosion conditioning factors. The model's goodness-of-fit and prediction performance are compared to three other machine learning algorithms (single alternating decision tree, rotational-forest-based alternating decision tree (RF-ADTree), and benchmark logistic regression). To achieve this, a gully-erosion inventory was created for the study area, the Chah Mousi watershed, Iran by combining archival records containing reports of gully erosion, remotely sensed data from Google Earth, and geolocated sites of gully head-cuts gathered in a field survey. A total of 119 gully head-cuts were identified and mapped. To train the models' analysis and prediction capabilities, 83 head-cuts (70% of the total) and the corresponding measures of the conditioning factors were input into each model. The results from the models were validated using the data pertaining to the remaining 36 gully locations (30%). Next, the frequency ratio is used to identify which conditioning-factor classes have the strongest correlation with gully erosion. Using random-forest modeling, the relative importance of each of the conditioning factors was determined. Based on the random-forest results, the top eight factors in this study area are distance-to-road, drainage density, distance-to-stream, LU/LC, annual precipitation, topographic wetness index, NDVI, and elevation. Finally, based on goodness-of-fit and AUROC of the success rate curve (SRC) and prediction rate curve (PRC), the results indicate that the bagging-ADTree ensemble model had the best performance, with SRC (0.964) and PRC (0.978). RF-ADTree (SRC = 0.952 and PRC = 0.971), ADTree (SRC = 0.926 and PRC = 0.965), and LR (SRC = 0.867 and PRC = 0.870) were the subsequent best performers. The results also indicate that bagging and RF, as meta-classifiers, improved the performance of the ADTree model as a base classifier. The bagging-ADTree model's results indicate that 24.28% of the study area is classified as having high and very high susceptibility to gully erosion. The new ensemble model accurately identified the areas that are susceptible to gully erosion based on the past patterns of formation, but it also provides highly accurate predictions of future gully development. The novel ensemble method introduced in this research is recommended for use to evaluate the patterns of gullying in arid and semi-arid environments and can effectively identify the most salient conditioning factors that promote the development and expansion of gullies in erosion-susceptible environments.
英文关键词	gully head-cuts machine learning modeling soil erosion Iran
类型	Article
语种	英语
国家	Iran ; Peoples R China ; Austria ; USA ; Australia ; South Korea ; Vietnam
开放获取类型	Green Submitted, gold
收录类别	SCI-E
WOS记录号	WOS:000519847200016
WOS关键词	LANDSLIDE SUSCEPTIBILITY ASSESSMENT ; ARTIFICIAL-INTELLIGENCE APPROACH ; MULTICRITERIA DECISION-MAKING ; ROTATION FOREST ENSEMBLE ; SUPPORT VECTOR MACHINE ; DATA-MINING TECHNIQUES ; ERROR PRUNING TREES ; EROSION SUSCEPTIBILITY ; LOGISTIC-REGRESSION ; SPATIAL PREDICTION
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315694
作者单位	1.Tarbiat Modares Univ, Dept Geomorphol, Tehran 3658117994, Iran; 2.Xian Univ Sci & Technol, Coll Geol & Environm, Xian 710054, Peoples R China; 3.Minist Land & Resources, Key Lab Coal Resources Explorat & Comprehens Util, Xian 710021, Peoples R China; 4.Shaanxi Prov Key Lab Geol Support Coal Green Expl, Xian 710054, Peoples R China; 5.Univ Salzburg, Dept Geoinformat Z GIS, A-5020 Salzburg, Austria; 6.Texas State Univ, Dept Geog, San Marcos, TX 78666 USA; 7.Univ Technol Sydney, Fac Engn & Informat Technol, CAMGIS, Sydney, NSW 2007, Australia; 8.Sejong Univ, Dept Energy & Mineral Resources Engn, 209 Neungdong Ro, Seoul 05006, South Korea; 9.Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam
推荐引用方式 GB/T 7714	Arabameri, Alireza,Chen, Wei,Blaschke, Thomas,et al. Gully Head-Cut Distribution Modeling Using Machine Learning Methods-A Case Study of NW Iran[J],2020,12(1).
APA	Arabameri, Alireza,Chen, Wei,Blaschke, Thomas,Tiefenbacher, John P.,Pradhan, Biswajeet,&Dieu Tien Bui.(2020).Gully Head-Cut Distribution Modeling Using Machine Learning Methods-A Case Study of NW Iran.WATER,12(1).
MLA	Arabameri, Alireza,et al."Gully Head-Cut Distribution Modeling Using Machine Learning Methods-A Case Study of NW Iran".WATER 12.1(2020).