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Optimizing landslide susceptibility mapping using integrated forest by penalizing attributes model with ensemble algorithms

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EARTH SCIENCE INFORMATICS 2025年第2期18卷 1-27页

作者： Chen, Wei Wang, Chao Zhao, Xia Bai, Li He, Qingfeng Chen, Xi Zhao, Qifei Zhao, Ruixin Li, Tao Tsangaratos, Paraskevas Ilia, Ioanna Xian Univ Sci & Technol Coll Geol & Environm Xian 710054 Peoples R China Shandong Construct Reconnoissance Grp Co LTD 686 Wuyingshan West Rd Jinan 250031 Peoples R China Buertai Coal Mine Shendong Coal Grp Co Ltd Ordos 017209 Peoples R China Changan Univ Sch Highway Xian 710064 Peoples R China Liupanshui Normal Univ Sch Min & Civil Engn Liupanshui 553000 Guizhou Peoples R China Natl Tech Univ Athens Sch Min & Met Engn Dept Geol Sci Lab Engn Geol & Hydrogeol Zografos 15780 Greece

Landslide, as a significant global natural hazard, threatening human settlements and the natural environment. The present study introduces a novel approach to landslide susceptibility assessment by integrating the Forest Attribute Penalty (FPA) model with three ensemble algorithms-AdaBoost (AB), Rotation Forest (RF), and Random Subspace (RS)-and utilizing the Evidential Belief Function (EBF) to weight the classes of landslide-related factors. To evaluate the performance of the developed methodology, Yanchuan County, China, was chosen as the appropriate study area. Three hundred and eleven landslide areas were identified through remote sensing and field investigations, which were randomly divided into 70% for model training and 30% for model evaluation, whereas sixteen landslide - related factors were considered, such as elevation, slope aspect, profile curvature, plan curvature, convergence index, slope length, terrain ruggedness index, topographic position index, distance to roads, distance to rivers, NDVI, land use, soil, rainfall, and lithology. EBF was employed to analyze the spatial correlation between these factors and landslide occurrences, providing the class weights of each factor for the implementation of FPA and the ensemble models. The next step involved the generation of the landslide susceptibility maps based on the models, with findings showing that more than half of the study area is classified as very low susceptibility. Model performance was assessed using receiver operating characteristic (ROC) curves and other statistical metrics, with the RFFPA model achieving the highest predictive ability, with AUC values of 0.878 and 0.890 for training and validation datasets, respectively. The AFPA and RSFPA hybrid models, however, demonstrated weaker predictive abilities compared to the FPA model. The study highlights the importance of optimizing model performance and evaluating the suitability of ensemble approaches, emphasizing the role of topographical a

关键词： Landslide susceptibility Data driven Evidential belief function FPA ensemble algorithms

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A short-term photovoltaic output power forecasting based on ensemble algorithms using hyperparameter optimization

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ELECTRICAL ENGINEERING 2024年第5期106卷 5319-5337页

作者： Basaran, Kivanc Celikten, Azer Bulut, Hasan Manisa Celal Bayar Univ Dept Elect Engn Manisa Turkiye Ege Univ Dept Comp Engn Izmir Turkiye

The stochastic and intermittent nature of solar energy presents the power grid with the challenge of providing a stable, secure, and economical power supply, especially in the case of large-scale penetration. The prerequisite for addressing these challenges is accurate power output estimation from PV systems. In addition, accurate power estimation also ensures the correct sizing of PV systems for investors. In this study, the PV output prediction model has been developed based on ensemble algorithms using two years of real power and meteorological data from grid-connected PV systems. Grid search, random search, and Bayesian optimization were used to determine the optimal hyperparameters for ensemble algorithms. The originality of this study is that (i) the use of hyperparameter optimization for ensemble algorithms in predicting PV performance, (ii) the degradation rate of PV panels by ensemble algorithms using the first two years' data, and (iii) the performance comparison of ensemble algorithms using the hyperparameter optimization technique. The accuracy and precision of the prediction model are determined by the relative root mean square error (RMSE), mean absolute error (MAE), mean bias error (MBE), mean scaled error (MSE), coefficient of determination (R2), mean absolute percentage error (MAPE), and maximum absolute error (MaxAE). To the best of our knowledge, this is one of the first studies to address the optimization of all hyperparameters to find the best parameters for ensemble algorithms and PV panel degradation rates. The results show that the CatBoost algorithm has better performance than the other algorithms used. The performance metrics of the CatBoost algorithm were determined to be 0.9327 R2, 0.047 MSE, 0.0388 MAE, 0.0003 MBE, 0.069 RMSE, 18.7 MAPE, and 0.79 MaxAE.

关键词： PV output power Forecasting Degradation rate ensemble algorithms Hyperparameter optimization

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Novel Deep Hybrid and ensemble algorithms for Improving GPS Navigation Positioning Accuracy

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IEEE ACCESS 2023年 11卷 53518-53530页

作者： Aydin, Tolga Erdem, Ebru Ataturk Univ Dept Comp Engn TR-25240 Erzurum Turkiye

GPS (Global Positioning System) has been a widespread system used for various purposes in today's world and it is essential to suggest innovative effective solutions to improve its use and functions. The present study proposes GPS coordinate conversion models based on Machine Learning (ML) and Deep Learning (DL) algorithms in order to "improve accuracy of GPS conversion and positioning services". 23 different models are tested on two different data sets to achieve this purpose. The study primarily aims to improve positioning accuracy of navigation systems by using GPS data through hybrid and ensemble algorithms. The proposed DL-based models are named as GPSCNNs and GPSLSTM. GPSCNNs contain "Xception, VGG16, VGG19, Alexnet, CNN1, CNN2, CNN3" deep learning algorithms in their structure. Of these algorithms, "Xception, VGG16, VGG19, Alexnet" are pre-trained models. "CNN1" consists of 2 Convolution, 2 Average Pool, 1 Flatten, and 5 Dense layers. "CNN2" consists of 1 Convolution, 1 Max Pool, 1 Flatten, and 4 Dense layers. "CNN3" consists of 4 Convolution, 4 Batch Normalization, 2 Max Pool, 1 Flatten, and 3 Dense layers. GPSLSTM contains 1 LSTM and 1 Dense layer in its structure. Raw GPS data are fed into the models as input, which was followed by obtaining information about the features of the data and getting coordinate data as input. The results show that ensemble models provide the most accurate positioning and GPSCNNs and GPSLSTM were quite promising in boosting this accuracy.

关键词： Global Positioning System Predictive models Data models Stacking Kalman filters Wiener filters Filtering algorithms ensemble algorithms GPS GPSCNNs GPSLSTM

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Revealing spatial variability of groundwater level in typical ecosystems of the Tarim Basin through ensemble algorithms and limited observations

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JOURNAL OF HYDROLOGY 2023年 620卷

作者： Wei, Yang Wang, Fei Hong, Bo Yang, Shengtian Chengdu Univ Sch Architecture & Civil Engn Chengdu Peoples R China Xinjiang Water Qingqing Environm Monitoring Techno Urumqi Peoples R China Beijing Normal Univ Coll Water Sci Beijing Key Lab Urban Hydrol Cycle & Sponge City T Beijing Peoples R China

Owing to the lack of hydrological monitoring stations and groundwater observations, particularly for natural habitats, the distribution of groundwater along the Mountainous (alluvial fan)-Oasis-Desert System (MODS) in the middle reaches of the Tarim River has not yet been systematically investigated. The performance of ensemble algorithms with multi-source/multi-scale (30 m, 90 m, 250 m, 500 m, and 1000 m) remote sensing data to predict the groundwater levels of the MODS of the Tarim Basin should be investigated. To improve the current knowledge on the spatial distribution of groundwater in this region, we conducted a study on the applicability of ensemble learning algorithms for groundwater assessments in typical regions of dryland, namely the Oasis-Taklimakan Desert (OTD), Nature land in Oasis system (NLOS), Irrigation area (IA), and Oasis system (OS). The results showed that the R-2 values of the groundwater level prediction accuracy for the four ecosystems were 0.92, 0.96, 0.86, and 0.50, respectively, and their corresponding optimal ensemble algorithms were AR_GRBFN, AR_RF, RotationForest_MLP, and AR_ GRBFN. The scales corresponding to the optimal prediction accuracy of OTD, NLOS, IA, and OS were 250 m, 30 m, 250 m, and 90 m, respectively, and the effect of the scale on their respective groundwater level prediction accuracies (maximum value compared to minimum value) were 11.48%, 21.63%, 26.73%, and 10.59%. Random Subspace, Rotation Forest, and Additive Regression improved the pre-diction accuracy of the base learning method by 76%, 72%, and 64%, respectively, followed by Bagging, whereas Dagging greatly increased prediction errors. Rotation Forest and Random Subspace showed stable performances and guaranteed relatively low prediction errors. Among all ensemble algorithms, Additive Regression helped the base learners obtain relatively optimal prediction accuracies with high probabilities. Except for the OS system, groundwater level could be predicted with much

关键词： Groundwater level Tarim basin ensemble algorithms Scale effect Remote sensing

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Remote Interference Discrimination Testbed Employing AI ensemble algorithms for 6G TDD Networks

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SENSORS 2023年第4期23卷 2264页

作者： Zhang, Hanzhong Zhou, Ting Xu, Tianheng Hu, Honglin Chinese Acad Sci Shanghai Adv Res Inst Shanghai 201210 Peoples R China ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Shanghai Univ Sch Microelect Shanghai 200444 Peoples R China Shanghai Frontier Innovat Res Inst Shanghai 201100 Peoples R China

The Internet-of-Things (IoT) massive access is a significant scenario for sixth-generation (6G) communications. However, low-power IoT devices easily suffer from remote interference caused by the atmospheric duct under the 6G time-division duplex (TDD) mode. It causes distant downlink wireless signals to propagate beyond the designed protection distance and interfere with local uplink signals, leading to a large outage probability. In this paper, a remote interference discrimination testbed is originally proposed to detect interference, which supports the comparison of different types of algorithms on the testbed. Specifically, 5,520,000 TDD network-side data collected by real sensors are used to validate the interference discrimination capabilities of nine promising AI algorithms. Moreover, a consistent comparison of the testbed shows that the ensemble algorithm achieves an average accuracy of 12% higher than the single model algorithm.

关键词： remote interference interference discrimination testbed ensemble algorithms Bagging

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Prediction of Sinter Chemical Composition Based on ensemble Learning algorithms

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JOURNAL OF SUSTAINABLE METALLURGY 2023年第3期9卷 1168-1179页

作者： Niu, Lele Liu, Zhengjian Zhang, Jianliang Sun, Qingke Schenk, Johannes Wang, Jiabao Wang, Yaozu Univ Sci & Technol Beijing Sch Met & Ecol Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Sch Intelligence Sci & Technol Beijing 100083 Peoples R China Univ Sci & Technol Beijing Inst Artificial Intelligence Beijing 100083 Peoples R China Univ Leoben Chair Ferrous Met A-8700 Leoben Austria

Effective prediction of sinter chemical composition enables more timely capture of production abnormalities and reduces the time for process parameters correction, thus optimizing sintering as well as blast furnace production. In this study, two ensemble algorithms, Random Forest and XGBoost, are used to model and predict the sinter chemical composition. The prediction results show that both ensemble algorithms are able to achieve small Mean Square Error (MSE) and Mean Absolute Error (MAE) values with the chemical composition of the raw materials as training parameters, and except for FeO, the prediction hit rate of all other components was above 85%. There is almost no significant difference between the prediction results of the two ensemble algorithms. With the addition of sintering process parameters and fuel/flux parameters as training parameters, reductions in MAE and MSE as well as increases in prediction hit rate were achieved for each chemical composition. Finally, the trained Random Forest model with chemical composition combined with process parameters or fuel/flux parameters achieved prediction hit rate of 94.45%, 83.39%, 95.29%, 89.44%, 90.96%, 97.72%, 99.39%, and 90.35% for Fe, FeO, CaO, SiO2, Al2O3, basicity, MgO, and P, respectively.

关键词： Ironmaking ensemble algorithms Sinter chemical composition Prediction Hit rate

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ensemble algorithms for Microblog Summarization

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IEEE INTELLIGENT SYSTEMS 2018年第3期33卷 4-14页

作者： Dutta, Soumi Chandra, Vibhash Mehra, Kanav Das, Asit Kumar Chakraborty, Tanmoy Ghosh, Saptarshi IIEST Shibpur Sibpur India IIIT Delhi Delhi India IIT Kharagpur Kharagpur W Bengal India

We investigate whether off-the-shelf summarization algorithms can be combined to produce better quality summaries. To this end, we propose ensemble schemes that can combine the outputs of multiple base summarization a... 详细信息

关键词： Web Sites Off The Shelf Summarization algorithms ensemble Schemes Multiple Base Summarization algorithms ensemble algorithms Microblog Summarization Intelligent Systems Clustering algorithms Computational Modeling Frequency Measurement Task Analysis Tropical Cyclones Classification algorithms Blogs Microblog Summarization ensemble Summarization Graph Based Summarization Learning To Rank

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Convolution-based ensemble learning algorithms to estimate the bond strength of the corroded reinforced concrete

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CONSTRUCTION AND BUILDING MATERIALS 2022年 359卷

作者： Cavaleri, Liborio Barkhordari, Mohammad Sadegh Repapis, Constantinos C. Armaghani, Danial Jahed Ulrikh, Dmitrii Vladimirovich Asteris, Panagiotis G. Univ Palermo Dept Civil Environm Aerosp & Mat Engn Palermo Italy Amirkabir Univ Technol Dept Civil & Environm Engn Tehran Iran Univ West Attica Dept Civil Engn Lab Civil Engn Struct Athens Greece South Ural State Univ Inst Architecture & Construct Dept Urban Planning Engn Networks & Syst 76 Lenin Prospect Chelyabinsk 454080 Russia Sch Pedag & Technol Educ Dept Computat Mech Lab Athens 14121 Greece

Reinforced concrete bond strength deterioration is one of the most serious problems in the construction industry. It is one of the most common factors impacting structural deterioration and the major cause of premature decadence of reinforced concrete structures. Therefore, developing an accurate model with the lowest variance and high reliability for the bond strength of corroded reinforced concrete is very important. The current work evaluates the efficiency of convolution-based ensemble learning algorithms. To address these issues, convolution -based ensemble learning models are developed using a database collected from the previous experimental studies of relative bond strength for corroded reinforced concrete. Seven parameters are considered as inputs, including bar diameter, the ratio of concrete cover to bar diameter, the ratio of the bar diameter to embedment length, transverse reinforcement ratio, yielding strength, concrete compressive strength and the corrosion level. Results indicate that the convolution-based integrated stacking model produces excellent predictions with the coefficient of determination, a-20 index, and mean square error values of 0.84, 0.75, and 0.022. Since machine learning based models are commonly black-box models, the Shapley Additive Explanations (SHAP) method is used to explicitly relate the predicted output to the inputs. Based on SHAP, the corrosion level, concrete compressive strength, transverse reinforcement ratio, and the concrete cover to bar diameter ratio are ranked among the most influential parameters on the bond strength of the corroded reinforced concrete.

关键词： Corrosion Bond strength ensemble algorithms Deep learning model Corroded reinforced concrete Pull-out test

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Comparative Analysis of Weighted ensemble and Majority Voting algorithms for Intrusion Detection in OpenStack Cloud Environments

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第12期14卷 741-747页

作者： Patil, Pravin Kale, Geetanjali Bivalkar, Nidhi Kolhatkar, Agneya Pune Inst Comp Technol Dept Comp Engn Pune Maharashtra India

- In the ever-evolving landscape of cybersecurity, the detection of malicious activities within cloud environments remains a critical challenge. This research aims to compare the effectiveness of two ensemble algorithms, the weighted ensemble algorithm and the majority voting algorithm, in the context of intrusion detection within an OpenStack cloud environment. To conduct this study, a dataset was generated using a network of 10 virtual machines, simulating the complex dynamics of a real cloud infrastructure. Various attack scenarios were simulated, and system metrics including CPU usage, memory utilization, and network traffic were monitored and logged. The weighted ensemble algorithm combines the predictions of multiple individual models with varying weights, while the majority voting algorithm aggregates predictions from multiple models. Through a rigorous experimental setup, these algorithms were applied to the generated dataset, and their performance was evaluated using standard metrics such as accuracy, precision, recall, and F1-score. These findings provide valuable insights into the strengths and weaknesses of ensemble algorithms for intrusion detection in cloud environments. It highlights the importance of selecting appropriate algorithms based on specific security requirements and threat profiles. Different attack scenarios may require different algorithmic approaches to achieve optimal results. Overall, this study contributes to the understanding of ensemble techniques in cloud security and offers a foundation for further research in optimizing intrusion detection strategies within dynamic and complex cloud environments. By identifying the strengths and weaknesses of different ensemble algorithms, cybersecurity professionals can make informed decisions in selecting the most suitable approach to enhance the security of cloud environments.

关键词： Intrusion detection ensemble algorithms cloud security openstack weighted ensemble majority voting

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DaE2: Unmasking malicious URLs by leveraging diverse and efficient ensemble machine learning for online security

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COMPUTERS & SECURITY 2025年 148卷

作者： Omolara, Abiodun Esther Alawida, Moatsum Univ Abuja Dept Comp Sci Gwagwalada Nigeria Abu Dhabi Univ Dept Comp Sci Abu Dhabi 59911 U Arab Emirates

Over 5.44 billion people now use the Internet, making it a vital part of daily life, enabling communication, ecommerce, education, and more. However, this huge Internet connectivity also raises concerns about online privacy and security, particularly with the rise of malicious Uniform Resource Locators (URLs). Recently, conventional ensemble models have attracted attention due to their notable benefits of reducing the variance in models, enhancing predictive performance, improving prediction accuracy, and demonstrating high generalization potential. But, its application in addressing the challenge of malicious URLs is still an open problem. These URLs often hide behind static links in emails or web pages, posing a threat to individuals and organizations. Despite blacklisting services, many harmful sites evade detection due to inadequate scrutiny or recent creation. Hence, to improve URL detection, a Diverse and Efficient ensemble (DaE2) machine learning algorithm was developed using four ensemble models, that is, AdaBoost, Bagging, Stacking, and Voting to classify URLs. After preprocessing, the experimental result shown that all models achieved over 80 % accuracy, with AdaBoost reaching 98.5 % and Stacking offering the fastest runtime. AdaBoost and Bagging also delivered strong performance, with F1 scores of 0.980 and 0.976, respectively.

关键词： Cybercriminals Deception Internet Malicious URL Phishing attacks Machine learning ensemble algorithms

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