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A new strategy for spatial predictive mapping of mineral prospectivity: Automated hyperparameter tuning of random forest approach
Computers & Geosciences ( IF 4.4 ) Pub Date : 2021-01-12 , DOI: 10.1016/j.cageo.2021.104688
Mehrdad Daviran , Abbas Maghsoudi , Reza Ghezelbash , Biswajeet Pradhan

Machine learning algorithms (e.g., random forest (RF)) have recently been performed in data-driven mineral prospectivity mapping. These methods are highly sensitive to hyperparameter values, since the predictive accuracy of them can significantly increase when the optimized hyperparameters are predefined and then adjusted to training procedure. The main goal of this contribution is to propose a hybrid genetic-based RF model, namely GRF, which is able to automatically adjust the optimized hyperparameters of RF with the excellent predictive accuracy. Therefore, three primary parameters of RF comprising NT, NS and d, were well-tuned employing genetic algorithm (GA) in establishing an efficient RF model. The proposed GRF model and also conventional RF were tested on mineralization-related geo-spatial dataset and the predictive models were generated for comparing the accuracy of the proposed GRF model with that of RF. The input dataset (e.g., multi-element geochemical signature, geological-structural layer and hydrothermal alteration evidences) which acquired from Feizabad district, NE Iran, were translated into mappable targeting criteria in the form of four predictor maps. In addition, the locations of 13 known Cu–Au deposits as prospect data and the locations of 13 randomly selected non-prospect data were used as target variables to train the models. Three authentic validation measures, K-fold cross-validation, confusion matrix and success-rate curves, were employed to evaluate the overall performance of two predictive models. Experimental results suggested the superiority of GRF model over the RF, as the favorable areas derived by GRF model occupy only 9% of the study area while predicting 100% of the known deposits.



中文翻译:

矿产前景空间预测制图的新策略:随机森林方法的自动超参数调整

机器学习算法(例如,随机森林(RF))最近已在数据驱动的矿物前瞻性制图中执行。这些方法对超参数值高度敏感,因为当预定义优化的超参数然后将其调整为训练程序时,它们的预测准确性会大大提高。该贡献的主要目的是提出一种基于遗传的混合RF模型,即GRF,该模型能够以出色的预测精度自动调整RF的优化超参数。因此,RF的三个主要参数包括N TN Sd使用遗传算法(GA)进行了微调,以建立有效的RF模型。在矿化相关的地理空间数据集上测试了建议的GRF模型以及常规的RF,并生成了预测模型,用于比较建议的GRF模型与RF的准确性。从伊朗东北部费扎巴德地区获得的输入数据集(例如,多元素地球化学特征,地质构造层和热液蚀变证据)被转换为可绘制目标的标准,以四个预测图的形式出现。此外,将13个已知的Cu-Au矿床的位置作为勘探数据,并将13个随机选择的非勘探数据的位置用作目标变量以训练模型。三种可靠的验证措施,K折交叉验证,混淆矩阵和成功率曲线,被用来评估两个预测模型的整体性能。实验结果表明,GRF模型优于RF,因为通过GRF模型得出的有利面积仅占研究面积的9%,同时预测了100%的已知矿床。

更新日期:2021-01-12
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