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.

中文翻译：

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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产地：以 Saoura 地区为例

萨乌拉地区是北非著名的绿洲，具有对国家和世界都重要的遗产和考古意义，但随着时间的推移，它的情况逐渐恶化。这项研究涉及考古预测模型，旨在创建能够预测在特定区域内发现考古遗址、文化资源或过去景观使用证据的可能性的模型。该研究特别侧重于预测撒哈拉沙漠历史遗迹的位置，采用最大熵（MaxEnt）模型和六种地理环境标准，包括坡度、高程（数字高程模型[DEM]）、距水的距离、归一化差异植被指数 (NDVI)、肥力和靠近棕榈树林的程度。该研究基于 58 个历史遗址的数据，并对模型的准确性进行了评估。该研究强调了生育力变量的显着意义，它占预测影响的94.1%，使其成为预测撒哈拉历史遗址位置最关键的地理环境因素。这强调了其在塑造该地区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%）。MaxEnt 模型被证明适合预测历史遗址位置，其 ROC 曲线下平均面积 (AUC) 得分令人印象深刻，达到 0.859，反映了其有效性。值得注意的是，预测概率较高的地区主要位于 Saoura 山谷附近。该研究的结果有可能帮助规划者避开可能存在历史遗址的区域，从而保护考古遗址。