The Random Forest (RF) and K nearest neighbors (KNN) machine learning (ML) algorithms were evaluated for their ability to predict ophiolite occurrences, in the East Vardar Zone (EVZ) of central North Macedonia. A predictive map of the investigated area was created using three data sources: geophysical data (digital elevation model, gravity and geomagnetic), multispectral optical satellite images (Landsat 7 ETM + and their derivatives), and geological data (distance to fault map and ophiolite outcrops map). The research included a comparison and discussion on the statistical and geological findings derived from different training dataset class ratios in relation to a testing dataset characterized by significant class imbalance. The results suggest that the precise selection of a suitable class balance for the training dataset is a critical factor in achieving accurate ophiolite prediction with RF and KNN algorithms. The analysis of feature importance revealed that the Bouguer gravity anomaly map, total intensity of the Earth’s magnetic field reduced to the pole map, distance to fault map, band ratio BR3 map obtained from multispectral satellite images, and digital elevation model are the most significant features for predicting ophiolites within the EVZ. KNN showed poorer results compared to RF in terms of both the evaluation metrics and visual analysis of prediction maps. The methods applied in this research can be applied for predictive mapping of complex geo-tectonic units covered by dense vegetation, and may indicate the presence of these units even if they were not previously mapped, particularly when geophysical data are used as features.

对北马其顿中部东瓦尔达尔区 (EVZ) 的随机森林 (RF) 和 K 最近邻 (KNN) 机器学习 (ML) 算法预测蛇绿岩出现的能力进行了评估。使用三个数据源创建了调查区域的预测地图：地球物理数据（数字高程模型、重力和地磁）、多光谱光学卫星图像（Landsat 7 ETM + 及其衍生物）和地质数据（到断层图和蛇绿岩的距离）露头地图）。该研究包括对不同训练数据集类别比例得出的统计和地质结果与以严重类别不平衡为特征的测试数据集进行比较和讨论。结果表明，为训练数据集精确选择合适的类平衡是使用 RF 和 KNN 算法实现准确蛇绿岩预测的关键因素。特征重要性分析表明，布格重力异常图、地球磁场总强度简化为极图、距断层距离图、多光谱卫星图像波段比BR3图、数字高程模型是最显着的特征用于预测 EVZ 内的蛇绿岩。与 RF 相比，KNN 在评估指标和预测图可视化分析方面的结果较差。本研究中应用的方法可用于对茂密植被覆盖的复杂地质构造单元进行预测绘图，并且即使以前未绘制这些单元，也可以指示这些单元的存在，特别是当地球物理数据用作特征时。