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

IF 2.1 3区 地球科学 0 ARCHAEOLOGY Archaeological Prospection Pub Date : 2023-12-15 DOI:10.1002/arp.1923
Guechi Imen, Gherraz Halima, Korichi Ayoub, Alkama Djamel
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Abstract

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.

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利用 MaxEnt 机器学习模型预测阿尔及利亚西南部沙漠地区的城市遗址:萨乌拉地区案例研究
萨乌拉地区是北非著名的绿洲,具有国家和世界重要的遗产和考古意义。这项研究涉及考古预测建模,旨在创建能够预测在特定地区发现考古遗址、文化资源或过去景观使用证据的可能性的模型。这项研究的具体重点是预测撒哈拉沙漠中历史遗址的位置,采用了最大熵(MaxEnt)模型和六个地理环境标准,包括坡度、海拔(数字高程模型 [DEM])、与水的距离、归一化差异植被指数(NDVI)、肥力和是否靠近棕榈林。研究以 58 个历史遗址的数据为基础,包括对模型准确性的评估。研究强调了肥力变量的显著重要性,它占预测影响的 94.1%,使其成为预测撒哈拉历史遗址位置的最关键地理环境因素。这凸显了它在该地区形成定居模式和生存策略方面的关键作用,其次是距棕榈湾的距离变量(3.2%)和距河流的距离变量(2.3%)。事实证明,MaxEnt 模型适用于预测历史遗址位置,其平均 ROC 曲线下面积 (AUC) 得分为 0.859,效果显著。值得注意的是,预测概率较高的区域主要位于索拉河谷附近。研究结果有望帮助规划人员避开可能存在历史遗址的区域,从而保护考古遗址。
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来源期刊
Archaeological Prospection
Archaeological Prospection 地学-地球科学综合
CiteScore
3.90
自引率
11.10%
发文量
31
审稿时长
>12 weeks
期刊介绍: The scope of the Journal will be international, covering urban, rural and marine environments and the full range of underlying geology. The Journal will contain articles relating to the use of a wide range of propecting techniques, including remote sensing (airborne and satellite), geophysical (e.g. resistivity, magnetometry) and geochemical (e.g. organic markers, soil phosphate). Reports and field evaluations of new techniques will be welcomed. Contributions will be encouraged on the application of relevant software, including G.I.S. analysis, to the data derived from prospection techniques and cartographic analysis of early maps. Reports on integrated site evaluations and follow-up site investigations will be particularly encouraged. The Journal will welcome contributions, in the form of short (field) reports, on the application of prospection techniques in support of comprehensive land-use studies. The Journal will, as appropriate, contain book reviews, conference and meeting reviews, and software evaluation. All papers will be subjected to peer review.
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