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A novel hybrid methodology integrating pixel- and object-based techniques for mapping land use and land cover from high-resolution satellite data

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2024年第16期45卷 5640-5678页

作者： Ozturk, Muhammed Yusuf Colkesen, Ismail Gebze Tech Univ Dept Geomat Engn TR-41400 Gebze Kocaeli Turkiye

The classification of very high-resolution satellite imagery remains a focal point in remote sensing, attracting increased attention across diverse scientific disciplines. Various classification methods, including pixel- and object-based techniques, have been proposed, and their performances and limitations have been discussed in the literature. This paper presents a hybrid method that combines the strengths of pixel- and object-based methods in image classification to minimize errors associated with the segmentation process, particularly under-segmentation errors in object-based image analysis. The core concept behind the method lies in categorizing segmented image objects as either homogeneous or heterogeneous based on their class probability. In this process, the estimated possibilities from the object-based classification model are considered, and segments are designated as homogeneous or heterogeneous using a user-defined threshold. The object-based classification model determines the class labels for homogeneous image objects, while the heterogeneous ones, containing pixels representing different land cover classes, are classified using the pixel-based model. The performance of hybrid classification models, created by varying thresholds, is analysed using high-resolution WorldView-3 and WorldView-2 imagery and compared with pixel- and object-based classification results. For the implementation of image classification methods, Canonical Correlation Forest (CCF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) were employed. The findings indicated that employing the suggested hybrid strategy with a threshold value selected within a specific range (e.g. between 60% and 80%) and employing a robust classification algorithm that provides class probabilities (e.g. CCF) results in a statistically significant improvement in overall accuracy compared to pixel and object-based methods, with gains of 5% and 4%, respectively. Visual analysis of the

关键词： Hybrid classification object-based image analysis (OBIA) under-segmentation ensemble learning algorithms WorldView-2/3 imagery

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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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Structural Damage Identification Using ensemble Deep Convolutional Neural Network Models

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Computer Modeling in Engineering & Sciences 2023年第2期134卷 835-855页

作者： Mohammad Sadegh Barkhordari Danial Jahed Armaghani Panagiotis G.Asteris Department of Civil and Environmental Engineering Amirkabir University of TechnologyTehranIran Department of Urban Planning Engineering Networks and SystemsInstitute of Architecture and ConstructionSouth Ural State UniversityChelyabinskRussian Department of Computational Mechanics Laboratory School of Pedagogical and Technological EducationAthensGreece

The existing strategy for evaluating the damage condition of structures mostly focuses on feedback supplied by traditional visualmethods,which may result in an unreliable damage characterization due to inspector subjectivity or insufficient level of *** a result,a robust,reliable,and repeatable method of damage identification is *** learning algorithms for identifying structural damage are evaluated in this article,which use deep convolutional neural networks,including simple averaging,integrated stacking,separate stacking,and hybridweighted averaging ensemble and differential evolution(WAE-DE)*** identification is carried out on three types of *** proposed algorithms are used to analyze the damage of 4585 structural *** effectiveness of the ensemble learning techniques is evaluated using the confusion *** the testing dataset,the confusion matrix achieved an accuracy of 94 percent and a minimum recall of 92 percent for the best model(WAE-DE)in distinguishing damage types as flexural,shear,combined,or undamaged.

关键词： Machine learning ensemble learning algorithms convolutional neural network damage assessment structural damage

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Super learner machine-learning algorithms for compressive strength prediction of high performance concrete

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STRUCTURAL CONCRETE 2023年第2期24卷 2208-2228页

作者： Lee, Seunghye Ngoc-Hien Nguyen Karamanli, Armagan Lee, Jaehong Vo, Thuc P. Sejong Univ Deep Learning Architecture Res Ctr Dept Architectural Engn 209 Neungdong Ro Seoul 05006 South Korea HUTECH Univ CIRTech Inst Ho Chi Minh City Vietnam Istinye Univ Mech Engn Fac Engn & Nat Sci Istanbul Turkey La Trobe Univ Sch Comp Engn & Math Sci Bundoora Vic 3086 Australia

Because the proportion between the compressive strength of high-performance concrete (HPC) and its composition is highly nonlinear, more advanced regression methods are demanded to obtain better results. Super learner models, which are based on several ensemble methods including random forest regression (RFR), an adaptive boosting (AdaBoost), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), categorical gradient Boosting (CatBoost), are used to solve this complicated problem. A grid search method is employed to determine the best set of hyper-parameters of each ensemble algorithm. Two super learner models, which combine all six models or select the top three effective ones as the base learners, are then proposed to develop an accurate approach to estimate the compressive strength of HPC. The results on four popular datasets show significant improvement of the proposed super learner models in terms of prediction accuracy. It also reveals that their trained models always perform better than other methods since their errors (MAE, MSE, RMSE) are always much lower and values of R-2 are higher than those of the previous studies. The proposed super learner models can be used to provide a reliable tool for mixture design optimization of the HPC.

关键词： compressive strength ensemble learning algorithms high-performance concrete (HPC) machine learning super learner

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Development and evaluation of MgO-grit iron aggregate heavy density concrete for high temperature radiation shielding: Experimental and machine learning approach

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CONSTRUCTION AND BUILDING MATERIALS 2024年 439卷

作者： Wahab, Sarmed Khan, Inayat Ullah Khan, Muhammad Nasir Ayaz Ashraf, Mahmud Univ Engn & Technol Dept Civil Engn Taxila Pakistan Univ Engn & Technol Dept Civil Engn Peshawar Pakistan Natl Univ Sci & Technol Dept Struct Engn MCE Risalpur Campus Islamabad 24080 Pakistan Deakin Univ Sch Engn Geelong Vic 3216 Australia

This research focuses on the development and evaluation of heavy density concrete (HDC) for radiation shielding, utilizing both experimental and machine learning techniques. Various HDC specimens with different proportions (25 %, 50 %, 75 %, and 100 %) of grit iron aggregate replacing normal weight aggregate, in addition to control specimens with no grit iron scale aggregate, were cast. These samples were subjected to testing at temperatures ranging from room temperature to 1200 degrees C to assess properties such as compressive strength, rebound number, ultrasonic pulse velocity, density loss, mass loss, linear attenuation coefficient (LAC), mass attenuation coefficient (MAC), half-value layer (HVL), tenth-value layer (TVL), and mean free path (MFP). ensemble learning algorithms were employed using experimental data to predict compressive strength and new empirical expressions were formulated for mechanical and radiation shielding properties, including LAC, HVL, and TVL. The addition of grit iron aggregate, combined with MgO, demonstrated a significant improvement in the mechanical and radiation shielding properties of HDC. This research holds promise for applications in nuclear reactors operating at high temperatures.

关键词： MgO-Grit Iron Aggregate Heavy Density Concrete High Temperature Radiation Shielding ensemble learning algorithms Nuclear reactors

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Integrating Machine learning ensembles for Landslide Susceptibility Mapping in Northern Pakistan

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REMOTE SENSING 2024年第6期16卷 988页

作者： Ali, Nafees Chen, Jian Fu, Xiaodong Ali, Rashid Hussain, Muhammad Afaq Daud, Hamza Hussain, Javid Altalbe, Ali Chinese Acad Sci Inst Rock & Soil Mech State Key Lab Geomech & Geotech Engn Wuhan 430071 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China China Pakistan Joint Res Ctr Earth Sci Islamabad 45320 Pakistan Hubei Key Lab Geoenvironm Engn Wuhan 430071 Peoples R China Zhejiang Normal Univ Sch Math Sci Jinhua 321004 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China China Univ Geosci Badong Natl Observat & Res Stn Geohazards Wuhan 430074 Peoples R China Prince Sattam Bin Abdulaziz Univ Dept Comp Sci Al Kharj 11942 Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Jeddah 21589 Saudi Arabia

Natural disasters, notably landslides, pose significant threats to communities and infrastructure. Landslide susceptibility mapping (LSM) has been globally deemed as an effective tool to mitigate such threats. In this regard, this study considers the northern region of Pakistan, which is primarily susceptible to landslides amid rugged topography, frequent seismic events, and seasonal rainfall, to carry out LSM. To achieve this goal, this study pioneered the fusion of baseline models (logistic regression (LR), K-nearest neighbors (KNN), and support vector machine (SVM)) with ensembled algorithms (Cascade Generalization (CG), random forest (RF), Light Gradient-Boosting Machine (LightGBM), AdaBoost, Dagging, and XGBoost). With a dataset comprising 228 landslide inventory maps, this study employed a random forest classifier and a correlation-based feature selection (CFS) approach to identify the twelve most significant parameters instigating landslides. The evaluated parameters included slope angle, elevation, aspect, geological features, and proximity to faults, roads, and streams, and slope was revealed as the primary factor influencing landslide distribution, followed by aspect and rainfall with a minute margin. The models, validated with an AUC of 0.784, ACC of 0.912, and K of 0.394 for logistic regression (LR), as well as an AUC of 0.907, ACC of 0.927, and K of 0.620 for XGBoost, highlight the practical effectiveness and potency of LSM. The results revealed the superior performance of LR among the baseline models and XGBoost among the ensembles, which contributed to the development of precise LSM for the study area. LSM may serve as a valuable tool for guiding precise risk-mitigation strategies and policies in geohazard-prone regions at national and global scales.

关键词： landslide susceptibility mapping machine learning baseline learning algorithms ensemble learning algorithms

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Predicting the mechanical properties of plastic concrete: An optimization method by using genetic programming and ensemble learners

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CASE STUDIES IN CONSTRUCTION MATERIALS 2024年 20卷

作者： Asif, Usama Javed, Muhammad Faisal Abuhussain, Maher Ali, Mujahid Khan, Waseem Akhtar Mohamed, Abdullah Nazarbayev Univ Dept Civil Engn Astana Kazakhstan Ghulam Ishaq Khan Inst Engn Sci & Technol Dept Civil Engn Topi Pakistan Univ Louisiana Lafayette Dept Civil Engn Lafayette LA 70503 USA Silesian Tech Univ Fac Transport & Aviat Engn Dept Transport Syst Traff Engn & Logist Krasinskiego 8 St PL-40019 Katowice Poland Umm Al Qura Univ Coll Engn & Comp Al Qunfudah Dept Civil & Environm Engn Mecca Saudi Arabia Future Univ Egypt Res Ctr New Cairo 11835 Egypt

This study presents a comparative analysis of individual and ensemble learning algorithms (ELAs) to predict the compressive strength (CS) and flexural strength (FS) of plastic concrete. Multilayer perceptron neuron network (MLPNN), Support vector machine (SVM), random forest (RF), and decision tree (DT) were used as base learners, which were then combined with bagging and Adaboost methods to improve the predictive performance. In addition, gene expression programming (GEP) was used to develop computational equations that can be used to predict the CS and FS of plastic concrete. An extensive database containing 357 and 125 data points was obtained from the literature, and the eight most impactful ingredients were used in the model's development. The accuracy of all models was assessed using several statistical measures, including an error matrix, Akaike information criterion (AIC), K-fold cross-validation, and other external validation equations. Furthermore, sensitivity and SHAP analysis were performed to evaluate input variables' relative significance and impact on the anticipated CS and FS. Based on statistical measures and other validation criteria, GEP outpaces all other individual models, whereas, in ELAs, the SVR ensemble with Adaboost and RF modified with the Bagging technique demonstrated superior performance. SHapley Additive exPlanations (SHAP) and sensitivity analysis reveal that plastic, cement, water, and the age of the specimens have the highest influence, while superplasticizer has the lowest impact, which is consistent with experimental studies. Moreover, GUI and GEP-based simple mathematical correlation can enhance the practical scope of this study and be an effective tool for the pre-mix design of plastic concrete.

关键词： Plastic concrete Machine learning Compressive strength Flexural strength Sustainability ensemble learning algorithms Gene expression programming

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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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Ultra-Short-Term Building Cooling Load Prediction Model Based on Feature Set Construction and ensemble Machine learning

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IEEE ACCESS 2020年 8卷 178733-178745页

作者： Ding, Yan Su, Hao Kong, Xiangfei Zhang, Zhenqin Tianjin Univ Sch Environm Sci & Engn Tianjin Key Lab Built Environm & Energy Applicat Tianjin 300072 Peoples R China Hebei Univ Technol Sch Energy & Environm Engn Tianjin 300401 Peoples R China

As the requirements for the optimal control of building systems increase, the accuracy and speed of load predictions should also increase. However, the accuracy of load predictions is related to not only the prediction algorithm, but also the feature set construction. Therefore, this study develops a short-term building cooling load prediction model based on feature set construction. The impacts of four different feature set construction methods-feature extraction, correlation analysis, K-means clustering, and discrete wavelet transform (DWT)-on the prediction accuracy are compared. To ensure that the effect of the feature set construction method is universal, three different prediction algorithms are used. The influences of the sample dimension and prediction time horizon on the prediction accuracy are also analysed. The prediction model is developed based on an ensemble learning algorithm utilising the cubist algorithm, and the performance of the prediction model is improved when DWT is used for constructing the feature set. Compared with other commonly used prediction models, the proposed model exhibits the best performance, with R-squared and CV-RMSE values of 99.8% and 1.5%, respectively.

关键词： Load modeling Predictive models Buildings Feature extraction Prediction algorithms Computational modeling Cooling Cooling load prediction feature extraction ensemble learning algorithms discrete wavelet transform

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Making AdaBoost Less Prone to Overfitting on Noisy Datasets 6

Making AdaBoost Less Prone to Overfitting on Noisy Datasets

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6th International Conference on Web Research (ICWR)

作者： Modarres, Zainab Ghadiri Shabankhah, Mahmood Kamandi, Ali Univ Tehran Coll Engn Sch Engn Sci Tehran Iran

ISBN: (纸本)9781728110516

AdaBoost is perhaps one of the most well-known ensemble learning algorithms. In simple terms, the idea in AdaBoost is to train a number of weak learners in an increamental fashion where each new learner tries to focus more on those samples that were misclassfied by the preceding classifiers. Consequently, in the presence of noisy data samples, the new leraners will somehow memorize the data, which in turn will lead to an overfitted model. The main objective of this paper is to provide a generalized version of the AdaBoost algorithm that avoids overfitting, and performs better when the data samples are corrupted with noise. To this end, we make use of another ensemble learning algorithm called ValidBoost [15], and introduce a mechanism to dynamically determine the thresholds for both the error rate of each classifier and the error rate in each iteration. These threshholds enable us to control the error rate of the algorithm. Experimental simulations have been made on several benchmark datasets including Web datasets such as "Website Phishing Data Set" and "Page Blocks Classification Data Set" to evaluate the performance of our proposed algorithm.

关键词： ensemble learning algorithms Boosting Adaboost Overfitting Noise Web Datasets Zero_One_Loss

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