干旱区生态环境知识资源中心(Arid): Utilizing the MaxEnt machine learning model to forecast urban heritage sites in the desert regions of southwestern Algeria: A case study in the Saoura region

Arid

DOI	10.1002/arp.1923
	Utilizing the MaxEnt machine learning model to forecast urban heritage sites in the desert regions of southwestern Algeria: A case study in the Saoura region
	Imen, Guechi; Halima, Gherraz; Ayoub, Korichi; Djamel, Alkama
通讯作者	Imen, G
来源期刊	ARCHAEOLOGICAL PROSPECTION
ISSN	1075-2196
EISSN	1099-0763
出版年	2024
卷号	31 期号:1 页码:23-35
英文摘要	The Saoura region, a renowned oasis in North Africa with heritage and archaeological significance of both national and universal importance, has witnessed a gradual deterioration over time. This research involves archaeological predictive modelling, aiming to create models capable of predicting the likelihood of discovering archaeological sites, cultural resources or evidence of past landscape use within a specific region. The study specifically focuses on predicting the locations of historical sites in the Sahara Desert, employing the maximum entropy (MaxEnt) model and six geo-environmental criteria, including slope, elevation (digital elevation model [DEM]), distance from water, normalized difference vegetation index (NDVI), fertility and proximity to palm groves. The research is based on data from 58 historical sites and includes an assessment of the model's accuracy. The study highlights the remarkable significance of the fertility variable, which accounts for 94.1% of the predictive influence, making it the most crucial geo-environmental factor in forecasting the location of historical sites in the Sahara. This underscores its pivotal role in shaping settlement patterns and subsistence strategies within the region, followed by the distance variable from the palm cove (3.2%) and the distance variable from the river (2.3%). The MaxEnt model proves to be suitable for predicting historical site positions, with an impressive average area under the ROC curve (AUC) score of 0.859, reflecting its effectiveness. Notably, areas with a high prediction probability are predominantly situated near the Saoura Valley. The study's findings hold the potential to assist planners in safeguarding archaeological sites by avoiding areas where historical sites are likely to be present.
英文关键词	cultural resource management MaxEnt model predictive modelling Saoura Valley urban heritage sites
类型	Article
语种	英语
收录类别	SCI-E ; AHCI
WOS记录号	WOS:001125810200001
WOS关键词	SPECIES DISTRIBUTION
WOS类目	Archaeology ; Geosciences, Multidisciplinary
WOS研究方向	Archaeology ; Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/402928
推荐引用方式 GB/T 7714	Imen, Guechi,Halima, Gherraz,Ayoub, Korichi,et al. Utilizing the MaxEnt machine learning model to forecast urban heritage sites in the desert regions of southwestern Algeria: A case study in the Saoura region[J],2024,31(1):23-35.
APA	Imen, Guechi,Halima, Gherraz,Ayoub, Korichi,&Djamel, Alkama.(2024).Utilizing the MaxEnt machine learning model to forecast urban heritage sites in the desert regions of southwestern Algeria: A case study in the Saoura region.ARCHAEOLOGICAL PROSPECTION,31(1),23-35.
MLA	Imen, Guechi,et al."Utilizing the MaxEnt machine learning model to forecast urban heritage sites in the desert regions of southwestern Algeria: A case study in the Saoura region".ARCHAEOLOGICAL PROSPECTION 31.1(2024):23-35.