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通过使用机器学习算法预测Quercus ilex (L.) 种类的潜在森林再生面积:摩洛哥东南部上方Ziz的案例

Mohamed Manaouch¹, Mohamed Sadiki², Quoc Bao Pham³

¹Department of Geography, Faculty of Humanities and Social Sciences, University of Ibn Tofail, 14000, Kenitra, Morocco. mohamed.manaouch@uit.ac.ma.

Environmental monitoring and assessment

|August 25, 2023

概括

机器学习模型有效地确定了摩洛哥Quercus ilex的最佳森林再生区域. 包装和天真贝叶斯算法显示出高准确度,指导了成本效益和及时的森林再生工作.

科学领域:

环境科学环境科学
生态生态学生态生态学
地理空间分析是什么

背景情况:

由于多种影响因素,重新造林地点的选择是复杂的,需要先进的技术.
机器学习 (ML) 已经在环境建模中证明了准确性,为改进复林规划提供了潜力.
树 (Quercus ilex) 是摩洛哥东南部土壤和水保护的关键本地物种.

研究的目的:

评估六种机器学习技术 (MLT) 用于划定最佳的森林再生区域.
确定最有效的MLT以准Quercus ilex在Ziz上部地区的森林再生.
为高效和成功的复林项目提供决策框架.

主要方法:

地质环境数据 (斜率,侧面,降雨量等) 从现有的Quercus ilex种群中收集.
应用六个机器学习算法来建模合适的复林区.
使用诸如曲线下的面积 (AUC) 等指标对模型性能进行比较分析.

主要成果:

包装 (AUC = 0.98) 和天真海湾 (AUC = 0.97) 在确定最佳森林再生区域方面表现出卓越的表现.
由包装和天真湾确定的极为有利的区域分别占研究区域的8%和17%.
最佳的区域主要位于西部,与巴索尼亚-巴约西亚地质和低侵蚀性有关.

结论:

机器学习,特别是包装和天真海湾,提供了一个强大的方法来优化重新造林地点的选择.
确定的有利区域为Quercus ilex的恢复提供了有针对性的方法,增强了土壤和水的保护.
这项研究为该地区的森林再生决策者提供了成本效益和时间效率的路线图.

关键词:

森林恢复森林恢复荷尔姆树是荷尔姆的树.机器学习机器学习半干旱地区半干旱地区网站的适用性网站的适用性

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