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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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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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Early prediction of postpartum dyslipidemia in gestational diabetes using machine learning models

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SCIENTIFIC REPORTS 2025年第1期15卷 1-13页

作者： Jiang, Zhifa Chen, Xiekun Lai, Yuhang Liu, Jingwen Ye, Xiangyun Chen, Ping Zhang, Zhen Huizhou First Maternal & Child Hlth Care Hosp Obstet & Gynaecol Huizhou 516000 Guangdong Peoples R China Huizhou Univ Sch Comp Sci & Engn Huizhou 516000 Guangdong Peoples R China

This study addresses a gap in research on predictive models for postpartum dyslipidemia in women with gestational diabetes mellitus (GDM). The goal was to develop a machine learning-based model to predict postpartum dyslipidemia using early pregnancy clinical data, and the model's robustness was evaluated through both internal and temporal validation. Clinical data from 15,946 pregnant women were utilized. After cleaning, the data were divided into two sets: Dataset A (n = 1,116), used for training and evaluating the model, and Dataset B (n = 707), used for temporal validation. Several machine learning algorithms were applied, and the performance of the model was assessed with Dataset A, while Dataset B was used to validate the model across a different time period. Feature significance was evaluated through Information Value (IV), model importance analysis, and SHAP (SHapley Additive exPlanations) analysis. The results showed that among the five machine learning algorithms tested, tree-based ensemble models, such as XGBoost, LightGBM, and Random Forest, outperformed others in predicting postpartum dyslipidemia. In Dataset A, these models achieved accuracies of 70.54%, 70.54%, and 69.64%, respectively, with AUC-ROC values of 73.10%, 71.94%, and 76.14%. Temporal validation with Dataset B indicated that XGBoost performed best, achieving an accuracy of 81.05% and an AUC-ROC of 87.92%. The predictive power of the model was strengthened by key variables such as total cholesterol, fasting glucose, triglycerides, and BMI, with total cholesterol being identified as the most important feature. Further IV and SHAP analyses confirmed the pivotal role of these variables in predicting dyslipidemia. The study concluded that the XGBoost-based predictive model for postpartum dyslipidemia in GDM showed strong and consistent performance in both internal and temporal validations. By introducing new variables, the model can identify high-risk groups during early pregnancy, supporting ea

关键词： Machine learning ensemble learning algorithms Diabetes Gestational Postpartum period Dyslipidemias

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AI-powered smart prediction of axial load in CFST columns: a sustainable and resilient structural engineering approach

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2025年第5期10卷 1-40页

作者： Gupta, Megha Prakash, Satya Ghani, Sufyan Sharda Univ Dept Civil Engn Greater Noida India

This paper presents a novel ensemble learning-based framework for accurately predicting the ultimate axial compressive load-carrying capacity of S/RCFST columns, contributing to the development of resilient and sustainable structural solutions. A comprehensive dataset of 932 experimental samples was compiled, encompassing key design parameters such as column dimensions, material properties, and load capacities. Five ensemble learning algorithms, Decision Tree, Adaptive Boosting, Extreme Gradient Boosting, Categorical Gradient Boosting (CatBoost), and Boosted Regression Tree, were systematically evaluated with extensive hyperparameter tuning to enhance predictive accuracy. The CatBoost model exhibited superior performance, achieving exceptional precision and robustness during the training and testing phases. Trend Consistency Analysis and Monte Carlo Simulations were integrated to validate model reliability, ensuring practical applicability. A graphical user interface was developed to facilitate seamless adoption by engineers and researchers, enabling real-time applications in building information Modeling environments. The findings underscore the potential of AI-driven predictive models in smart infrastructure development, promoting efficiency, accuracy, and sustainability in structural engineering practice.

关键词： Concrete-filled steel tube (CFST) Ultimate axial load-carrying capacity (UACLC) ensemble learning algorithms CatBoost smart infrastructure Sustainable design Sensitivity analysis Graphical user interface (GUI) Building information modeling (BIM) Structural resilience Reproducibility

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A Three-Stage Based ensemble learning for Improved Software Fault Prediction: An Empirical Comparative Study

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2018年第1期11卷 1229-1247页

作者： Yohannese, Chubato Wondaferaw Li, Tianrui Bashir, Kamal Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Sichuan Peoples R China

Software Fault Prediction (SFP) research has made enormous endeavor to accurately predict fault proneness of software modules, thus maximize precious software test resources, reduce maintenance cost and contributes to produce quality software products. In this regard, Machine learning (ML) has been successfully applied to solve classification problems for SFP. However, SFP has many challenges that are created due to redundant and irrelevant features, class imbalance problem and the presence of noise in software defect datasets. Yet, neither of ML techniques alone handles those challenges and those may deteriorate the performance depending on the predictor's sensitiveness to data corruptions. In the literature, it is widely claimed that building ensemble classifiers from preprocessed datasets and combining their predictions is an interesting method of overcoming the individual problems produced by each classifier. This statement is usually not supported by thorough empirical studies considering problems in combined implementation with resolving different types of challenges in defect datasets and, therefore, it must be carefully studied. Thus, the objective of this paper is to conduct large scale comprehensive experiments to study the effect of resolving those challenges in SFP in three stages in order to improve the practice and performance of SFP. In addition to that, the paper presents a thorough and statistically sound comparison of these techniques in each stage. Accordingly, a new three-stage based ensemble learning framework that efficiently handles those challenges in a combined form is proposed. The experimental results confirm that the proposed framework has exhibited the robustness of combined techniques in each stage. Particularly high performance results have achieved using combined ELA on selected features of balanced data after removing noise instances. Therefore, as shown in this study, ensemble techniques used for SFP must be carefully examined and c

关键词： Software Fault Prediction Software Testing ensemble learning algorithms Feature Selection Data Balancing Noise Filtering

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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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