Choosing Selecting suitable sites for afforestation is a complex process that is influenced by various factors that require the use of new models and methods in order to create better results. The main purpose of this study is to investigate the use of a machine learning framework to map the best sites for afforestation with J. excelsa, an important species for soil and water conservation in Firuzkuh County, Tehran Province, Iran. Existing stands of J. excelsa were located. Measures of 14 environmental variables were compiled at each site. Three machine learning algorithms–Fuzzy ARTMAP (FAM), Multi-layers perceptron (MLP), and Classification tree analysis (CTA) – were used to model ideal locations for growing the tree. They were compared in terms of success rate. The best performance was achieved by CTA (area under curve (AUC) = 0.899). MLP (AUC = 0.892) was second best, and FAM (AUC = 0.835) had the lowest success. All three models achieved very good to excellent results; however, the CTA model was the most effective. Locations of high and very high favorability for J. excelsa comprise between 8% and 18% of the study area. The factors that are most important for the locations of replanting are those with bedrock of the Cl geological group and where rainfall ranges from 350 mm/year and 450 mm/year. This study offers support to decision makers for improving (lower cost and less time) selection of planting sites that are more likely to support tree survival to achieve natural restoration.

选择 选择合适的植树造林地点是一个复杂的过程，受各种因素的影响，需要使用新的模型和方法才能创造更好的结果。本研究的主要目的是调查使用机器学习框架来绘制J. excelsa的最佳造林地点，J. excelsa是伊朗德黑兰省 Firuzkuh 县水土保持的重要物种。J. excelsa现有林分位于。在每个站点编制了 14 个环境变量的测量值。三种机器学习算法——模糊 ARTMAP (FAM)、多层感知器 (MLP) 和分类树分析 (CTA)——被用来模拟生长树的理想位置。他们在成功率方面进行了比较。CTA 实现了最佳性能（曲线下面积 (AUC) = 0.899）。MLP (AUC = 0.892) 次之，FAM (AUC = 0.835) 成功率最低。三个模型都取得了非常好的成绩；然而，CTA 模型是最有效的。对J. excelsa有利和非常有利的位置占研究区域的 8% 到 18%。对补植位置最重要的因素是具有Cl 的基岩的因素。地质组，降雨量从 350 毫米/年到 450 毫米/年不等。本研究为决策者提供支持，以改善（降低成本和缩短时间）选择更有可能支持树木生存以实现自然恢复的种植地点。