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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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ensemble algorithms in reinforcement learning

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2008年第4期38卷 930-936页

作者： Wiering, Marco A. van Hasselt, Hado Univ Groningen Dept Artificial Intelligence NL-9400 AK Groningen Netherlands Univ Utrecht Dept Informat & Comp Sci Intelligent Syst Grp NL-3508 TB Utrecht Netherlands

This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and final performance by combining the chosen actions or action probabilities of different RL algorithms. We designed and implemented four different ensemble methods combining the following five different RL algorithms: Q-learning, Sarsa, actor-critic (AC), QV-learning, and AC learning automaton. The intuitively designed ensemble methods, namely, majority voting (MV), rank voting, Boltzmann multiplication (BM), and Boltzmann addition, combine the policies derived from the value functions of the different RL algorithms, in contrast to previous work where ensemble methods have been used in RL for representing and learning a single value function. We show experiments on five maze problems of varying complexity;the first problem is simple, but the other four maze tasks are of a dynamic or partially observable nature. The results indicate that the BM and MV ensembles significantly outperform the single RL algorithms.

关键词： dynamic mazes ensemble algorithms partially observable environments reinforcement learning (RL)

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ensemble algorithms for DNA Motif Finding 13

Ensemble Algorithms for DNA Motif Finding

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13th International Conference on Electronics, Information, and Communication (ICEIC)

作者： Kim, Juho Yu, Seunghak Yoon, Sungroh Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea Korea Univ Dept IT Convergence Seoul South Korea Seoul Natl Univ Interuniv Semicond Res Ctr Seoul South Korea Seoul Natl Univ Bioinformat Inst Seoul South Korea

ISBN: (纸本)9781479937974

Understanding the regulation of gene expression is one of the major research problems in biology. An important part of this problem is to identify the binding sites in DNA for transcription factors. These binding sites are called motifs. The performance of de novo motif discovery algorithms is limited. To address the issue, ensemble methods have been proposed in order to enhance the prediction accuracy by exploiting the information obtained from multiple algorithms. This paper reviews the latest developments of integrated algorithms in DNA motif finding.

关键词： bioinformatics computational biology ensemble algorithms integrated algorithms motifs finding

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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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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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SUCCESS OF ensemble algorithms IN CLASSIFICATION OF ELECTRICAL IMPADENCE SPECTROSCOPY BREAST TISSUE RECORDS

SUCCESS OF ENSEMBLE ALGORITHMS IN CLASSIFICATION OF ELECTRIC...

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22nd IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Eroglu, Kubra Mehmetoglu, Eteri Kilic, Niyazi Istanbul Arel Univ Elekt Elekt Muhendisligi Bolumu Istanbul Turkey Istanbul Univ Elekt Elekt Muhendisligi Bolumu Istanbul Turkey

ISBN: (纸本)9781479948741

In this study was performed by using records from breast tissue electrical impedance spectroscopy analysis. The aim of the study is to reveal the impact of ensemble algorithms on success of the classification performance in the classification of normal and pathological breast tissue classification. For this purpose have been used three different ensemble algorithms they are bagging, adaboost, random subspaces and three main basic classifiers, which are RF, YSA, DVM. The results obtained are supplemented with performance analysis and ensemble algorithms have been demonstrated to increase classification performance results. The results obtained by the combined use of adaboost ensemble algorithm with RF basic classifier demonstrate, that the success rate was higher than the others (%89.62).

关键词： breast tissue electrical impedance spectroscopy ensemble algorithms bagging adaboost random subspaces

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Minimalist ensemble algorithms for genome-wide protein localization prediction

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BMC BIOINFORMATICS 2012年第1期13卷 1-12页

作者： Lin, Jhih-Rong Mondal, Ananda Mohan Liu, Rong Hu, Jianjun Univ S Carolina Dept Comp Sci & Engn Columbia SC 29208 USA Claflin Univ Dept Math & Comp Sci Columbia SC 29115 USA

Background: Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms. Results: This paper proposed a novel method for rational design of minimalist ensemble algorithms for practical genome-wide protein subcellular localization prediction. The algorithm is based on combining a feature selection based filter and a logistic regression classifier. Using a novel concept of contribution scores, we analyzed issues of algorithm redundancy, consensus mistakes, and algorithm complementarity in designing ensemble algorithms. We applied the proposed minimalist logistic regression (LR) ensemble algorithm to two genome-wide datasets of Yeast and Human and compared its performance with current ensemble algorithms. Experimental results showed that the minimalist ensemble algorithm can achieve high prediction accuracy with only 1/3 to 1/2 of individual predictors of current ensemble algorithms, which greatly reduces computational complexity and running time. It was found that the high performance ensemble algorithms are usually composed of the predictors that together cover most of available features. Compared to the best individual predictor, our ensemble algorithm improved the prediction accuracy from AUC score of 0.558 to 0.707 for the Yeast dataset and from 0.628 to 0.646 for the Human dataset. Compared with popular weighted voting based ensemble algorithms, our classifier-based ensemble algorithms achieved much better performance without suffering from inclusion of too many individual predictors. Conclusions: We prop

关键词： Protein subcellular localization ensemble algorithms Classifiers Logistic regression

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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年第PartA期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 2264-2264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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