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Mapping of Gold Prospectivity in the Qingchengzi Pb-Zn-Ag-Au Polymetallic District, China, with ensemble learning algorithms

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NATURAL RESOURCES RESEARCH 2025年第1期34卷 41-60页

作者： Zhang, Zhiqiang Wang, Gongwen Carranza, Emmanuel John M. Li, Yingjie Liu, Xinxing Peng, Wuxu Fan, Junjie Xu, Fengming Hebei GEO Univ Hebei Prov Collaborat Innovat Ctr Strateg key hype Shijiazhuang 050031 Peoples R China Hebei GEO Univ Hebei Key Lab Strateg Crit Mineral Resources Shijiazhuang 050031 Peoples R China Hebei GEO Univ Sch Earth Sci Shijiazhuang 050031 Peoples R China China Univ Geosci MNR Key Lab Explorat Theory & Technol Crit Mineral Beijing 100083 Peoples R China Univ Free State Fac Nat & Agr Sci Dept Geol Bloemfontein South Africa China Geol Survey Ctr Geophys Survey Langfang 065000 Peoples R China China Geol Survey Shenyang Ctr Shenyang 110000 Peoples R China

ensemble learning (EL) is a machine learning paradigm where multiple learning algorithms (base learners) are trained to solve the same problem. This study provides a comprehensive evaluation of widely used EL algorithms, including bagging, boosting, and stacking, highlighting their significant advantages in terms of accuracy and generalization of mineral prospectivity mapping (MPM). This study tested mapping of prospectivity for gold deposits in the Qingchengzi Pb-Zn-Ag-Au polymetallic district using single machine learning algorithms and EL algorithms. According to the critical and favorable geological factors for magmatic-related medium-temperature hydrothermal lode system for gold deposits, five targeting criteria were extracted from multi-source geoscience datasets (i.e., geological map, gravity and magnetic datasets, stream sediment geochemical datasets) for mineral prospectivity mapping. The receiver operating characteristic curve, the area under the curve, and learning curves were used to evaluate the performance of the tested single and ensemble machine learning algorithms. The results demonstrate that the stacking model, which combines multiple base models for hierarchical feature extraction, achieves the best predictive performance. The concentration-area fractal model was used to outline the prospective areas predicted by the EL algorithms, clarifying areas with very high prospectivity for gold mineralization in the study area.

关键词： Mineral prospectivity mapping ensemble learning algorithms Concentration-area fractal model Qingchengzi Pb-Zn-Ag-Au polymetallic district

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Comparison of tree-based ensemble learning algorithms for landslide susceptibility mapping in Murgul (Artvin), Turkey

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EARTH SCIENCE INFORMATICS 2024年第2期17卷 1459-1481页

作者： Usta, Ziya Akinci, Halil Akin, Alper Tunga Artvin Coruh Univ Dept Geomat Engn TR-08100 Artvin Turkiye Karadeniz Tech Univ Dept Geomat Engn TR-61080 Trabzon Turkiye

Turkey's Artvin province is prone to landslides due to its geological structure, rugged topography, and climatic characteristics with intense rainfall. In this study, landslide susceptibility maps (LSMs) of Murgul district in Artvin province were produced. The study employed tree-based ensemble learning algorithms, namely Random Forest (RF), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), and eXtreme Gradient Boosting (XGBoost). LSM was performed using 13 factors, including altitude, aspect, distance to drainage, distance to faults, distance to roads, land cover, lithology, plan curvature, profile curvature, slope, slope length, topographic position index (TPI), and topographic wetness index (TWI). The study utilized a landslide inventory consisting of 54 landslide polygons. Landslide inventory dataset contained 92,446 pixels with a spatial resolution of 10 m. Consistent with the literature, the majority of landslide pixels (70% - 64,712 pixels) were used for model training, and the remaining portion (30% - 27,734 pixels) was used for model validation. Overall accuracy, precision, recall, F1-score, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC-ROC) were considered as validation metrics. LightGBM and XGBoost were found to have better performance in all validation metrics compared to other algorithms. Additionally, SHapley Additive exPlanations (SHAP) were utilized to explain and interpret the model outputs. As per the LightGBM algorithm, the most influential factors in the occurrence of landslide in the study area were determined to be altitude, lithology, distance to faults, and aspect, whereas TWI, plan and profile curvature were identified as the least influential factors. Finally, it was concluded that the produced LSMs would provide significant contributions to decision makers in reducing the damages caused by landslides in the study area.

关键词： Landslide Landslide susceptibility Machine learning ensemble learning algorithms Murgul

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Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and ensemble learning algorithms

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SENSORS 2022年第7期22卷 2685页

作者： Xie, Boqiang Ding, Jianli Ge, Xiangyu Li, Xiaohang Han, Lijing Wang, Zheng Xinjiang Univ Sch Geog Sci Urumqi 830046 Peoples R China Xinjiang Univ Xinjiang Key Lab Oasis Ecol Urumqi 830046 Peoples R China Xinjiang Univ Environm Modelling Higher Educ Inst Key Lab Smart City Urumqi 830046 Peoples R China

Soil organic carbon (SOC), as the largest carbon pool on the land surface, plays an important role in soil quality, ecological security and the global carbon cycle. Multisource remote sensing data-driven modeling strategies are not well understood for accurately mapping soil organic carbon. Here, we hypothesized that the Sentinel-2 Multispectral Sensor Instrument (MSI) data-driven modeling strategy produced superior outcomes compared to modeling based on Landsat 8 Operational Land Imager (OLI) data due to the finer spatial and spectral resolutions of the Sentinel-2A MSI data. To test this hypothesis, the Ebinur Lake wetland in Xinjiang was selected as the study area. In this study, SOC estimation was carried out using Sentinel-2A and Landsat 8 data, combining climatic variables, topographic factors, index variables and Sentinel-1A data to construct a common variable model for Sentinel-2A data and Landsat 8 data, and a full variable model for Sentinel-2A data, respectively. We utilized ensemble learning algorithms to assess the prediction performance of modeling strategies, including random forest (RF), gradient boosted decision tree (GBDT) and extreme gradient boosting (XGBoost) algorithms. The results show that: (1) The Sentinel-2A model outperformed the Landsat 8 model in the prediction of SOC contents, and the Sentinel-2A full variable model under the XGBoost algorithm achieved the best results R-2 = 0.804, RMSE = 1.771, RPIQ = 2.687). (2) The full variable model of Sentinel-2A with the addition of the red-edge band and red-edge index improved R-2 by 6% and 3.2% over the common variable Landsat 8 and Sentinel-2A models, respectively. (3) In the SOC mapping of the Ebinur Lake wetland, the areas with higher SOC content were mainly concentrated in the oasis, while the mountainous and lakeside areas had lower SOC contents. Our results provide a program to monitor the sustainability of terrestrial ecosystems through a satellite perspective.

关键词： ensemble learning algorithms Landsat 8 Sentinel-2A Sentinel-1A soil organic carbon digital soil mapping

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Multisource remote sensing and ensemble learning for multidimensional monitoring of heavy metals on mine surfaces

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ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2025年第5期47卷 1-15页

作者： Li, Yanru Yang, Keming Gu, Xinru Peng, Lishun Chen, Xinyang China Univ Min & Technol Beijing Coll Geosci & Surveying Engn Beijing 100083 Peoples R China

This study aims to establish monitoring models for surface heavy metals in mining areas by utilizing multi-source remote sensing data and ensemble learning algorithms. By collecting heavy metal content data from soil and crop leaves within the study area, and combining it with data obtained from the Google Earth Engine platform, including Landsat 8, Sentinel-2 spectral data, vegetation indices, and VV and VH polarization information from Sentinel-1, along with terrain factors derived from the Digital Elevation Model such as elevation, hillshade, slope, and aspect, a total of 43 feature indicators were consolidated. Feature importance ranking (FI) and the successive projections algorithm (SPA) feature selection method were employed to filter feature factors, selecting different features for each type of heavy metal. In the soil, the optimal model for predicting Cr and Cd content is AdaBoost-MT, while the optimal model for inverting Zn, As, Hg, and Pb content is FISPA-AdaBoost-MT. In the crops, the optimal model for predicting the content of all six heavy metals is FISPA-AdaBoost-MT. This indicates that the combination of FI and SPA features effectively evaluates the heavy metal content in both soil and crops. Utilizing these multidimensional features, this study combines ensemble learning algorithms with multi-target regression techniques to construct inversion models for six types of heavy metals (Cr, Zn, As, Cd, Hg, and Pb) simultaneously. Based on the optimal prediction models, distribution maps of heavy metals in soil and crops within the study area were generated, achieving comprehensive, multidimensional monitoring of surface heavy metals in mining areas through overlay display.

关键词： Multi-source remote sensing data ensemble learning algorithms Feature selection Multi-target regression Heavy metal monitoring model

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Exploration of the Stacking ensemble Machine learning Algorithm for Cheating Detection in Large-Scale Assessment

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EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT 2023年第4期83卷 831-854页

作者： Zhou, Todd Jiao, Hong Winston Churchill High Sch Potomac MD USA Univ Maryland College Pk MD 20742 USA

Cheating detection in large-scale assessment received considerable attention in the extant literature. However, none of the previous studies in this line of research investigated the stacking ensemble machine learning algorithm for cheating detection. Furthermore, no study addressed the issue of class imbalance using resampling. This study explored the application of the stacking ensemble machine learning algorithm to analyze the item response, response time, and augmented data of test-takers to detect cheating behaviors. The performance of the stacking method was compared with that of two other ensemble methods (bagging and boosting) as well as six base non-ensemble machine learning algorithms. Issues related to class imbalance and input features were addressed. The study results indicated that stacking, resampling, and feature sets including augmented summary data generally performed better than its counterparts in cheating detection. Compared with other competing machine learning algorithms investigated in this study, the meta-model from stacking using discriminant analysis based on the top two base models-Gradient Boosting and Random Forest-generally performed the best when item responses and the augmented summary statistics were used as the input features with an under-sampling ratio of 10:1 among all the study conditions.

关键词： cheating detection stacking machine learning ensemble learning algorithms response time resampling oversampling SMOTE under-sampling dual resampling data augmentation

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Predicting accounting fraud using imbalanced ensemble learning classifiers - evidence from China

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ACCOUNTING AND FINANCE 2023年第3期63卷 3455-3486页

作者： Rahman, Md Jahidur Zhu, Hongtao Wenzhou Kean Univ Wenzhou Peoples R China Univ Edinburgh Edinburgh Scotland

The current research aims to launch effective accounting fraud detection models using imbalanced ensemble learning algorithms for China A-Share listed firms. Based on a sample of 33,544 Chinese firm-year instances from 1998 to 2017, this research respectively established one logistic regression and four ensemble learning classifiers (AdaBoost, XGBoost, CUSBoost, and RUSBoost) by 12 financial ratios and 28 raw financial data. Additionally, we divided the sample into the train and test observations to evaluate the classifiers' out-of-sample performance. In detail, we applied two metrics, namely, Area under the ROC (receiver operating characteristic) curve (AUC) and Area under the Precision-Recall curve (AUPR), to evaluate classifiers' discriminability. In the supplement test, this study put forward an algebraic fused model on the basis of the four ensemble learning classifiers and introduced the sliding window technique. The empirical results showed that the ensemble learning classifiers can detect accounting fraud for the imbalanced China A-listed firms far more effectively than the logistic regression model. Moreover, imbalanced ensemble learning classifiers (CUSBoost and RUSBoost) effectively performed better than the common ensemble learning models (AdaBoost and XGBoost) in average. The algebraic fused model in the supplement test also obtained the highest average AUC and AUPR among all the employed algorithms. Our results offer firm support for the potential role of Machine learning (ML)-based Artificial Intelligence (AI) approaches in reliably predicting accounting fraud with high accuracy. Similarly, for the Chinese settings, our ML-based AI offers utmost advantage in forecasting accounting fraud. Finally, this paper fills the research gap on the applications of imbalanced ensemble learning in accounting fraud detection for Chinese listed firms.

关键词： Accounting fraud detection Artificial intelligence China A-share CUSBoost ensemble learning algorithms Machine learning RUSBoost

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Human fall detection using neuro-fuzzy models based on ensemble learning

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PROGRESS IN ARTIFICIAL INTELLIGENCE 2022年第3期11卷 219-232页

作者： Kordnoori, Shirin Sharifi, Arash Shah-Hosseini, Hamed Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran

Human falling may be due to a violent act, a heart attack or perhaps physical illness. Every year, many old people are being treated for injuries or even die in hospitals which caused by falling. From there, there is a long-standing need for a timely and inexpensive system that automatically identifying a falling person and then alert, which reduces the death rate and increases the likelihood of survival. Due to the low accuracy and a lot of faults in the recognition of the systems released in past years, this paper presented human falling detection by using neuro-fuzzy models and ensemble learning algorithms which presented to solve these problems. This showed the influence of the ensemble learning on performance of neuro-fuzzy models. However, it should be noted that feature selection and extraction methods in processing the dataset have their own impact. In this case, five kinds of feature selection/extraction algorithms are used. Five neuro-fuzzy models are used in this research: normalized radial basis function (NRBF) network, radial basis function (RBF) network, adaptive neuro-fuzzy inference system (ANFIS), local linear model trees (LOLIMOT) and generalized regression neural networks (GRNN). LOLIMOT model, by using correlation based features selection algorithm, reached the highest answer with the accuracy of 0.796. The results of these models are entered into the two ensemble learning algorithms, single majority vote and weighted majority vote, which weighted majority vote algorithm reached an accuracy of 0.87917 by using principal component analysis algorithm, and was the highest answer among of all the models used in this paper.

关键词： ensemble learning algorithms NRBF RBF ANFIS network LOLIMOT GRNN

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Enhancing Mesothelioma Cancer Diagnosis through ensemble learning Techniques 3

Enhancing Mesothelioma Cancer Diagnosis through Ensemble Lea...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Degadwala, Sheshang Dave, Shrinal S Vyas, Dhairya Patel, Nandini A Gohil, Vinit I Rana, Kevil Sigma University Dept. of Comp. Eng Gujarat India University of Massachusetts Boston United States The Maharaja Sayajirao University of Baroda Gujarat Vadodara India Gujarat India G H Patel College of Eng. & Tech. Anand Gujarat India

ISBN: (纸本)9798350343632

In the relentless battle against mesothelioma, a rare and aggressive form of cancer with a dire prognosis, this research explores the transformative potential of ensemble learning techniques, namely Bagging Tree, Random Forest, and ensemble Extra Tree, in revolutionizing the diagnosis of mesothelioma. Traditional diagnostic methods have struggled with late-stage detection and limited treatment options, but this study presents a paradigm shift, leveraging advanced ML algorithms to process treasure of persistent information, comprising clinical records, imaging scans, and biomarker profiles. The Bagging Tree algorithm, Random Forest, and ensemble Extra Tree are harnessed to create a diverse ensemble, extract informative features, and enhance the predictive power of the diagnostic model. Through meticulous experimentation, this research demonstrates the unprecedented accuracy of the ensemble approach in distinguishing mesothelioma from other thoracic diseases, offering hope for early intervention and personalized treatment strategies. Additionally, the model interpretability provides valuable insights for clinicians, bridging the gap between artificial intelligence and human expertise, and fostering trust in the integration of these cutting-edge tools into clinical practice. © 2023 IEEE.

关键词： Advanced Diagnostics Bagging Tree ensemble Extra Tree ensemble learning algorithms Mesothelioma Random Forest

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Higher-order rumor and anti-rumor propagation and data-driven optimal control on hypergraphs

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CHAOS SOLITONS & FRACTALS 2025年 193卷

作者： Zhong, Xiaojing Luo, Chaolong Zhang, Jing Liu, Guiyun Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China

To address the "explosive"propagation phenomenon in social networks, we propose a novel hypergraph propagation model that captures the higher-order interaction process between rumor and anti-rumor. By calculating the propagation threshold and analyzing the global stability of equilibrium points, our research indicates that the higher-order structure is a component of the propagation threshold, directly affecting the final state of the propagation dynamics. Additionally, we design data-driven control algorithms, which integrates deep neural networks and ensemble learning algorithms, to autonomously seek suboptimal control strategies for rumor within the framework of optimal control theory. This approach enhances the efficiency and adaptability of traditional control methods. Simulation experiments demonstrate that the control algorithm effectively regulates rumor propagation, achieving a control cost deviation of only 0.0016 from the optimal control theory, while substantially improving the control speed compared to conventional methods.

关键词： Higher-order interaction Hypergraph Propagation model Stability Data-driven optimal control ensemble learning algorithms

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Interpretable predictive modeling, sustainability assessment, and cost analysis of cement-based composite containing secondary raw materials

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CONSTRUCTION AND BUILDING MATERIALS 2025年 473卷

作者： Asif, Usama Memon, Shazim Ali Nazarbayev Univ Sch Engn & Digital Sci Dept Civil & Environm Engn Nur Sultan 010000 Kazakhstan Univ Maryland Dept Civil & Environm Engn College Pk MD 20742 USA

The use of secondary cementitious raw materials (SCMs) in concrete, such as silica fume (SF) and ground granulated blast furnace slag (GGBS), can be a worthy option to reduce COQ emissions arising from the use of cement in concrete. Previously, several experimental and machine learning (ML)-based research studies have been conducted to promote the use of SCMs in concrete. However, prior studies have the following shortcomings: (a) a smaller database, (b) use of individual ML models, which often suffer from overfitting, poor robustness, and limited predictive accuracy, (c) non-availability of experimental validation, raising concerns about the reliability of ML-based predictions, (d) absence of sustainability and economic assessments, which are essential for the large-scale adoption of SCMs, and (e) non-existence of a comprehensive predictive model for estimating the compressive strength (CS) of binary and ternary blended concrete containing SF and GGBS. Therefore, to address these shortcomings, this study presents an innovative approach that employs a larger database, comparative analysis of individual and ensemble learning algorithms (ELAs), and experimental validation to estimate the CS of concrete while considering the sustainability and economic aspects of SCMs. A comprehensive database with eight input parameters of concrete was used to develop ML-based models such as multi-linear regression (MLR), artificial neural network (ANN), gene expression programming (GEP), and ELAs, including modified random forest (RF), bagging, and boosting techniques to anticipate the CS of concrete. The efficacy of the proposed models was validated by performing experimental investigations, statistical evaluations, K-fold tests, and external validation criteria. Shapley's Additive explanations (SHAP) analysis was also performed to explain the impact of each individual and the combined influence of input parameters on the CS of concrete. Comparative analysis shows that GEP ranks as

关键词： Secondary cementitious raw materials ensemble learning algorithms Gene expression programming Shapley's Additive explanations Compressive strength Sustainability assessments

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