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Flood predictions from metrics to classes by multiple machine learning algorithms coupling with clustering-deduced membership degree 认领 引用
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作者 ZHAI Xiaoyan ZHANG Yongyong +5 位作者 XIA Jun ZHANG Yongqiang TANG Qiuhong SHAO Quanxi CHEN Junxu ZHANG Fan 《Journal of Geographical Sciences》 SCIE CSCD 2026年第1期149-176,共28页
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting... Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach. 展开更多
关键词 flood regime metrics class prediction machine learning algorithms hydrological model
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Machine learning algorithms for ICU mortality prediction in community-acquired pneumonia:A standardized validation and calibration study on the NACef cohort 认领 引用
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作者 Hanane Belmouss Ahmed Aamouche 《Medical Data Mining》 CAS 2026年第3期41-49,共9页
Background:Mortality in intensive care due to community-acquired pneumonia remains high.Although machine learning models have demonstrated promising predictive performance,standardized validation and explicit calibrat... Background:Mortality in intensive care due to community-acquired pneumonia remains high.Although machine learning models have demonstrated promising predictive performance,standardized validation and explicit calibration assessment across independent clinical settings remain limited.Methods:This study systematically reviewed machine learning algorithms for mortality prediction in community-acquired pneumonia.A search across six databases identified 241 records,of which seven met the eligibility criteria.Standalone algorithms meeting predefined selection criteria were subsequently implemented within a standardized validation framework and evaluated on the NACef cohort(n=764;163 deaths,21.2%).Model performance was assessed using nested cross-validation and independent hold-out testing,with evaluation of both discrimination and calibration metrics.Results:Three standalone algorithms,XGBoost,LightGBM,and Logistic Regression,were selected for standardized implementation and evaluation.In nested cross-validation,all models achieved mean AUC values above 0.90.On the independent hold-out test set,uncalibrated AUC values were 0.941 for XGBoost,0.933 for LightGBM,and 0.915 for Logistic Regression,with small absolute differences across models.Recall ranged from 0.818(LightGBM)to 0.969(Logistic Regression),while precision ranged from 0.615(Logistic Regression)to 0.658(LightGBM).Calibration analysis indicated probability misalignment before recalibration.Platt scaling and isotonic regression improved calibration metrics,with isotonic regression achieving the lowest expected calibration error while maintaining comparable discrimination.Conclusion:Logistic Regression,XGBoost,and LightGBM demonstrated comparable discrimination,and calibration improved probability reliability,underscoring the importance of harmonized validation and explicit calibration assessment in clinical machine learning research. 展开更多
关键词 community-acquired pneumonia intensive care unit mortality machine learning algorithms model calibration nested cross-validation
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Neuromorphic devices assisted by machine learning algorithms 认领 引用 被引量：6
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作者 Ziwei Huo Qijun Sun +4 位作者 Jinran Yu Yichen Wei Yifei Wang Jeong Ho Cho Zhong Lin Wang 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2025年第4期178-215,共38页
Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks,e.g.,pattern processing,image recognition,and decisio... Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks,e.g.,pattern processing,image recognition,and decision making.It features parallel interconnected neural networks,high fault tolerance,robustness,autonomous learning capability,and ultralow energy dissipation.The algorithms of artificial neural network(ANN)have also been widely used because of their facile self-organization and self-learning capabilities,which mimic those of the human brain.To some extent,ANN reflects several basic functions of the human brain and can be efficiently integrated into neuromorphic devices to perform neuromorphic computations.This review highlights recent advances in neuromorphic devices assisted by machine learning algorithms.First,the basic structure of simple neuron models inspired by biological neurons and the information processing in simple neural networks are particularly discussed.Second,the fabrication and research progress of neuromorphic devices are presented regarding to materials and structures.Furthermore,the fabrication of neuromorphic devices,including stand-alone neuromorphic devices,neuromorphic device arrays,and integrated neuromorphic systems,is discussed and demonstrated with reference to some respective studies.The applications of neuromorphic devices assisted by machine learning algorithms in different fields are categorized and investigated.Finally,perspectives,suggestions,and potential solutions to the current challenges of neuromorphic devices are provided. 展开更多
关键词 neuromorphic devices machine learning algorithms artificial synapses memristors field-effect transistors
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A Comparison among Different Machine Learning Algorithms in Land Cover Classification Based on the Google Earth Engine Platform: The Case Study of Hung Yen Province, Vietnam 认领 引用
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作者 Le Thi Lan Tran Quoc Vinh Phạm Quy Giang 《Journal of Environmental & Earth Sciences》 CAS 2025年第1期132-139,共8页
Based on the Google Earth Engine cloud computing data platform,this study employed three algorithms including Support Vector Machine,Random Forest,and Classification and Regression Tree to classify the current status ... Based on the Google Earth Engine cloud computing data platform,this study employed three algorithms including Support Vector Machine,Random Forest,and Classification and Regression Tree to classify the current status of land covers in Hung Yen province of Vietnam using Landsat 8 OLI satellite images,a free data source with reasonable spatial and temporal resolution.The results of the study show that all three algorithms presented good classification for five basic types of land cover including Rice land,Water bodies,Perennial vegetation,Annual vegetation,Built-up areas as their overall accuracy and Kappa coefficient were greater than 80%and 0.8,respectively.Among the three algorithms,SVM achieved the highest accuracy as its overall accuracy was 86%and the Kappa coefficient was 0.88.Land cover classification based on the SVM algorithm shows that Built-up areas cover the largest area with nearly 31,495 ha,accounting for more than 33.8%of the total natural area,followed by Rice land and Perennial vegetation which cover an area of over 30,767 ha(33%)and 15,637 ha(16.8%),respectively.Water bodies and Annual vegetation cover the smallest areas with 8,820(9.5%)ha and 6,302 ha(6.8%),respectively.The results of this study can be used for land use management and planning as well as other natural resource and environmental management purposes in the province. 展开更多
关键词 Google Earth Engine Land Cover Landsat Machine Learning Algorithm
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Use of machine learning algorithms to assess the state of rockburst hazard in underground coal mine openings 认领 引用 被引量：17
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作者 Lukasz Wojtecki Sebastian Iwaszenko +2 位作者 Derek B.Apel Mirosawa Bukowska Janusz Makówka 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2022年第3期703-713,共11页
The risk of rockbursts is one of the main threats in hard coal mines. Compared to other underground mines, the number of factors contributing to the rockburst at underground coal mines is much greater.Factors such as ... The risk of rockbursts is one of the main threats in hard coal mines. Compared to other underground mines, the number of factors contributing to the rockburst at underground coal mines is much greater.Factors such as the coal seam tendency to rockbursts, the thickness of the coal seam, and the stress level in the seam have to be considered, but also the entire coal seam-surrounding rock system has to be evaluated when trying to predict the rockbursts. However, in hard coal mines, there are stroke or stress-stroke rockbursts in which the fracture of a thick layer of sandstone plays an essential role in predicting rockbursts. The occurrence of rockbursts in coal mines is complex, and their prediction is even more difficult than in other mines. In recent years, the interest in machine learning algorithms for solving complex nonlinear problems has increased, which also applies to geosciences. This study attempts to use machine learning algorithms, i.e. neural network, decision tree, random forest, gradient boosting, and extreme gradient boosting(XGB), to assess the rockburst hazard of an active hard coal mine in the Upper Silesian Coal Basin. The rock mass bursting tendency index WTGthat describes the tendency of the seam-surrounding rock system to rockbursts and the anomaly of the vertical stress component were applied for this purpose. Especially, the decision tree and neural network models were proved to be effective in correctly distinguishing rockbursts from tremors, after which the excavation was not damaged. On average, these models correctly classified about 80% of the rockbursts in the testing datasets. 展开更多
关键词 Hard coal mining Rockburst hazard Machine learning algorithms
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Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin,Asir Region,Saudi Arabia 认领 引用 被引量：34
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作者 Ahmed Mohamed Youssef Hamid Reza Pourghasemi 《Geoscience Frontiers》 SCIE CAS CSCD 2021年第2期639-655,共17页
The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector Machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artifici... The current study aimed at evaluating the capabilities of seven advanced machine learning techniques(MLTs),including,Support Vector Machine(SVM),Random Forest(RF),Multivariate Adaptive Regression Spline(MARS),Artificial Neural Network(ANN),Quadratic Discriminant Analysis(QDA),Linear Discriminant Analysis(LDA),and Naive Bayes(NB),for landslide susceptibility modeling and comparison of their performances.Coupling machine learning algorithms with spatial data types for landslide susceptibility mapping is a vitally important issue.This study was carried out using GIS and R open source software at Abha Basin,Asir Region,Saudi Arabia.First,a total of 243 landslide locations were identified at Abha Basin to prepare the landslide inventory map using different data sources.All the landslide areas were randomly separated into two groups with a ratio of 70%for training and 30%for validating purposes.Twelve landslide-variables were generated for landslide susceptibility modeling,which include altitude,lithology,distance to faults,normalized difference vegetation index(NDVI),landuse/landcover(LULC),distance to roads,slope angle,distance to streams,profile curvature,plan curvature,slope length(LS),and slope-aspect.The area under curve(AUC-ROC)approach has been applied to evaluate,validate,and compare the MLTs performance.The results indicated that AUC values for seven MLTs range from 89.0%for QDA to 95.1%for RF.Our findings showed that the RF(AUC=95.1%)and LDA(AUC=941.7%)have produced the best performances in comparison to other MLTs.The outcome of this study and the landslide susceptibility maps would be useful for environmental protection. 展开更多
关键词 Landslide susceptibility Machine learning algorithms Variables importance Saudi Arabia
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Predicting the daily return direction of the stock market using hybrid machine learning algorithms 认领 引用 被引量：16
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作者 Xiao Zhong David Enke 《Financial Innovation》 2019年第1期435-454,共20页
Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on f... Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks. 展开更多
关键词 Daily stock return forecasting Return direction classification Data representation Hybrid machine learning algorithms Deep neural networks(DNNs) Trading strategies
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Quantification of the concrete freeze-thaw environment across the Qinghai–Tibet Plateau based on machine learning algorithms 认领 引用 被引量：1
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作者 QIN Yanhui MA Haoyuan +3 位作者 ZHANG Lele YIN Jinshuai ZHENG Xionghui LI Shuo 《Journal of Mountain Science》 SCIE CSCD 2024年第1期322-334,共13页
The reasonable quantification of the concrete freezing environment on the Qinghai-Tibet Plateau(QTP)is the primary issue in frost resistant concrete design,which is one of the challenges that the QTP engineering manag... The reasonable quantification of the concrete freezing environment on the Qinghai-Tibet Plateau(QTP)is the primary issue in frost resistant concrete design,which is one of the challenges that the QTP engineering managers should take into account.In this paper,we propose a more realistic method to calculate the number of concrete freeze-thaw cycles(NFTCs)on the QTP.The calculated results show that the NFTCs increase as the altitude of the meteorological station increases with the average NFTCs being 208.7.Four machine learning methods,i.e.,the random forest(RF)model,generalized boosting method(GBM),generalized linear model(GLM),and generalized additive model(GAM),are used to fit the NFTCs.The root mean square error(RMSE)values of the RF,GBM,GLM,and GAM are 32.3,4.3,247.9,and 161.3,respectively.The R2values of the RF,GBM,GLM,and GAM are 0.93,0.99,0.48,and 0.66,respectively.The GBM method performs the best compared to the other three methods,which was shown by the results of RMSE and R2values.The quantitative results from the GBM method indicate that the lowest,medium,and highest NFTC values are distributed in the northern,central,and southern parts of the QTP,respectively.The annual NFTCs in the QTP region are mainly concentrated at 160 and above,and the average NFTCs is 200 across the QTP.Our results can provide scientific guidance and a theoretical basis for the freezing resistance design of concrete in various projects on the QTP. 展开更多
关键词 Freeze-thaw cycles Quantification Machine learning algorithms Qinghai-Tibet Plateau Concrete
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Medical Data Clustering and Classification Using TLBO and Machine Learning Algorithms 认领 引用 被引量：1
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作者 Ashutosh Kumar Dubey Umesh Gupta Sonal Jain 《Computers, Materials & Continua》 SCIE EI 2022年第3期4523-4543,共21页
This study aims to empirically analyze teaching-learning-based optimization(TLBO)and machine learning algorithms using k-means and fuzzy c-means(FCM)algorithms for their individual performance evaluation in terms of c... This study aims to empirically analyze teaching-learning-based optimization(TLBO)and machine learning algorithms using k-means and fuzzy c-means(FCM)algorithms for their individual performance evaluation in terms of clustering and classification.In the first phase,the clustering(k-means and FCM)algorithms were employed independently and the clustering accuracy was evaluated using different computationalmeasures.During the second phase,the non-clustered data obtained from the first phase were preprocessed with TLBO.TLBO was performed using k-means(TLBO-KM)and FCM(TLBO-FCM)(TLBO-KM/FCM)algorithms.The objective function was determined by considering both minimization and maximization criteria.Non-clustered data obtained from the first phase were further utilized and fed as input for threshold optimization.Five benchmark datasets were considered from theUniversity of California,Irvine(UCI)Machine Learning Repository for comparative study and experimentation.These are breast cancer Wisconsin(BCW),Pima Indians Diabetes,Heart-Statlog,Hepatitis,and Cleveland Heart Disease datasets.The combined average accuracy obtained collectively is approximately 99.4%in case of TLBO-KM and 98.6%in case of TLBOFCM.This approach is also capable of finding the dominating attributes.The findings indicate that TLBO-KM/FCM,considering different computational measures,perform well on the non-clustered data where k-means and FCM,if employed independently,fail to provide significant results.Evaluating different feature sets,the TLBO-KM/FCM and SVM(GS)clearly outperformed all other classifiers in terms of sensitivity,specificity and accuracy.TLBOKM/FCM attained the highest average sensitivity(98.7%),highest average specificity(98.4%)and highest average accuracy(99.4%)for 10-fold cross validation with different test data. 展开更多
关键词 K-means FCM TLBO TLBO-KM TLBO-FCM TLBO-KM/FCM machine learning algorithms
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Recent innovation in benchmark rates (BMR):evidence from influential factors on Turkish Lira Overnight Reference Interest Rate with machine learning algorithms 认领 引用 被引量：4
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作者 Öer Depren Mustafa Tevfik Kartal Serpil KılıçDepren 《Financial Innovation》 2021年第1期942-961,共20页
Some countries have announced national benchmark rates,while others have been working on the recent trend in which the London Interbank Offered Rate will be retired at the end of 2021.Considering that Turkey announced... Some countries have announced national benchmark rates,while others have been working on the recent trend in which the London Interbank Offered Rate will be retired at the end of 2021.Considering that Turkey announced the Turkish Lira Overnight Reference Interest Rate(TLREF),this study examines the determinants of TLREF.In this context,three global determinants,five country-level macroeconomic determinants,and the COVID-19 pandemic are considered by using daily data between December 28,2018,and December 31,2020,by performing machine learning algorithms and Ordinary Least Square.The empirical results show that(1)the most significant determinant is the amount of securities bought by Central Banks;(2)country-level macroeconomic factors have a higher impact whereas global factors are less important,and the pandemic does not have a significant effect;(3)Random Forest is the most accurate prediction model.Taking action by considering the study’s findings can help support economic growth by achieving low-level benchmark rates. 展开更多
关键词 Benchmark rate Determinants Machine learning algorithms Turkey
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Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process 认领 引用 被引量：12
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作者 Hamid Reza Pourghasemi Nitheshnirmal Sadhasivam +1 位作者 Narges Kariminejad Adrian L.Collins 《Geoscience Frontiers》 SCIE CAS CSCD 2020年第6期2207-2219,共13页
This investigation assessed the efficacy of 10 widely used machine learning algorithms(MLA)comprising the least absolute shrinkage and selection operator(LASSO),generalized linear model(GLM),stepwise generalized linea... This investigation assessed the efficacy of 10 widely used machine learning algorithms(MLA)comprising the least absolute shrinkage and selection operator(LASSO),generalized linear model(GLM),stepwise generalized linear model(SGLM),elastic net(ENET),partial least square(PLS),ridge regression,support vector machine(SVM),classification and regression trees(CART),bagged CART,and random forest(RF)for gully erosion susceptibility mapping(GESM)in Iran.The location of 462 previously existing gully erosion sites were mapped through widespread field investigations,of which 70%(323)and 30%(139)of observations were arbitrarily divided for algorithm calibration and validation.Twelve controlling factors for gully erosion,namely,soil texture,annual mean rainfall,digital elevation model(DEM),drainage density,slope,lithology,topographic wetness index(TWI),distance from rivers,aspect,distance from roads,plan curvature,and profile curvature were ranked in terms of their importance using each MLA.The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE(root mean square error),MAE(mean absolute error),and R-squared.Based on the comparisons among MLA,the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared,and was therefore selected as the best model.The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance.According to the GESM generated using RF,most of the study area is predicted to have a low(53.72%)or moderate(29.65%)susceptibility to gully erosion,whereas only a small area is identified to have a high(12.56%)or very high(4.07%)susceptibility.The outcome generated by RF model is validated using the ROC(Receiver Operating Characteristics)curve approach,which returned an area under the curve(AUC)of 0.985,proving the excellent forecasting ability of the model.The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion. 展开更多
关键词 Machine learning algorithm Gully erosion Random forest Controlling factors Variable importance
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Discrimination of periodontal pathogens using Raman spectroscopy combined with machine learning algorithms 认领 引用 被引量：2
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作者 Juan Zhang Yiping Liu +6 位作者 Hongxiao Li Shisheng Cao Xin Li Huijuan Yin Ying Li Xiaoxi Dong Xu Zhang 《Journal of Innovative Optical Health Sciences》 SCIE EI CSCD 2022年第3期23-35,共13页
Periodontitis is closely related to many systemic diseases linked by different periodontal pathogens.To unravel the relationship between periodontitis and systemic diseases,it is very important to correctly discrimina... Periodontitis is closely related to many systemic diseases linked by different periodontal pathogens.To unravel the relationship between periodontitis and systemic diseases,it is very important to correctly discriminate major periodontal pathogens.To realize convenient,effcient,and high-accuracy bacterial species classification,the authors use Raman spectroscopy combined with machine learning algorithms to distinguish three major periodontal pathogens Porphyromonas gingivalis(Pg),Fusobacterium nucleatum(Fn),and Aggregatibacter actinomycetemcomitans(Aa).The result shows that this novel method can successfully discriminate the three abovementioned periodontal pathogens.Moreover,the classification accuracies for the three categories of the original data were 94.7%at the sample level and 93.9%at the spectrum level by the machine learning algorithm extra trees.This study provides a fast,simple,and accurate method which is very beneficial to differentiate periodontal pathogens. 展开更多
关键词 Raman spectroscopy periodontal pathogen machine learning algorithm discrimination
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Predicting Future Cryptocurrency Prices Using Machine Learning Algorithms 认领 引用
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作者 Vaibhav Saha 《Journal of Data Analysis and Information Processing》 2023年第4期400-419,共20页
Cryptocurrency price prediction has garnered significant attention due to the growing importance of digital assets in the financial landscape. This paper presents a comprehensive study on predicting future cryptocurre... Cryptocurrency price prediction has garnered significant attention due to the growing importance of digital assets in the financial landscape. This paper presents a comprehensive study on predicting future cryptocurrency prices using machine learning algorithms. Open-source historical data from various cryptocurrency exchanges is utilized. Interpolation techniques are employed to handle missing data, ensuring the completeness and reliability of the dataset. Four technical indicators are selected as features for prediction. The study explores the application of five machine learning algorithms to capture the complex patterns in the highly volatile cryptocurrency market. The findings demonstrate the strengths and limitations of the different approaches, highlighting the significance of feature engineering and algorithm selection in achieving accurate cryptocurrency price predictions. The research contributes valuable insights into the dynamic and rapidly evolving field of cryptocurrency price prediction, assisting investors and traders in making informed decisions amidst the challenges posed by the cryptocurrency market. 展开更多
关键词 Cryptocurrency Price Prediction Machine Learning Algorithms Feature Engineering Performance Metrics
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Machine learning supervised algorithms for gas hydrate identification and saturation estimation in marine reservoirs using well log data:A case study of NGHP-01-19B 认领 引用
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作者 Yi-fan Wu Zheng Su +5 位作者 Takeshi Tsuji Dai-dai Wu Guang-rong Jin Chao Yang Chuang-ji Feng Neng-you Wu 《China Geology》 CAS CSCD 2026年第3期519-534,I0023-I0027,共16页
Gas hydrates are increasingly recognized as a significant unconventional energy resource and a key factor in marine geohazards and the global carbon cycle.However,accurately identifying and quantifying hydrate-bearing... Gas hydrates are increasingly recognized as a significant unconventional energy resource and a key factor in marine geohazards and the global carbon cycle.However,accurately identifying and quantifying hydrate-bearing formations remains challenging due to complex geophysical signatures and heterogeneous distribution.This study evaluates twelve supervised machine learning(ML)algorithms for two key tasks:Classification of hydrate-bearing layers and regression-based estimation of hydrate saturation,using well log and pore-water geochemical data from Site NGHP-01-19B.Two physically independent labeling frameworks are employed:One based on Archie's law using resistivity(1350 samples,29%hydratebearing),and another based on a three-phase velocity model(890 samples,25%hydrate-bearing).A diverse set of models,including tree-based ensembles(Decision Tree,Random Forest,GBDT,XGBoost,Light GBM,Cat Boost,Bagging,Ada Boost),kernel methods(SVM,SVR),instance-based learning(KNN),neural networks(MLP),and Gaussian Process models(GPR,GPC),are systematically compared using cross-validation and grid search.Ensemble methods consistently performed best in classification,with Ada Boost and GBDT,achieving test accuracies above 0.94(Archie)and 0.98(velocity-based).For regression,GPR delivered the most accurate hydrate saturation estimates(R2>0.99),while GBDT and Random Forest provided a strong balance of accuracy and computational efficiency.Notably,depth below seafloor(TDEP),though not a direct geophysical input,significantly enhanced model performance by acting as a proxy for stratigraphic and thermodynamic conditions.Group-based validation confirmed that random-sample splitting overestimates performance due to depth-wise autocorrelation,highlighting the importance of geologically informed model assessment.Overall,the consistent performance of ML models across both labeling schemes and input feature sets underscores their robustness and transferability,supporting their use as a reliable toolset for offshore gas hydrate reservoir characterization. 展开更多
关键词 Gas hydrate Machine learning algorithm Classification Regression Well log data Archie’s law Marine geohazards Global carbon cycle
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Identifying key factors influencing maize stalk lodging resistance through wind tunnel simulations with machine learning algorithms 认领 引用 被引量：1
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作者 Guanmin Huang Ying Zhang +3 位作者 Shenghao Gu Weiliang Wen Xianju Lu Xinyu Guo 《Artificial Intelligence in Agriculture》 SCIE EI CSCD 2025年第2期316-326,共11页
Climate change has intensified maize stalk lodging,severely impacting global maize production.While numerous traits influence stalk lodging resistance,their relative importance remains unclear,hindering breeding effor... Climate change has intensified maize stalk lodging,severely impacting global maize production.While numerous traits influence stalk lodging resistance,their relative importance remains unclear,hindering breeding efforts.This study introduces an combining wind tunnel testing with machine learning algorithms to quantitatively evaluate stalk lodging resistance traits.Through extensive field experiments and literature review,we identified and measured 74 phenotypic traits encompassing plant morphology,biomass,and anatomical characteristics in maize plants.Correlation analysis revealed a median linear correlation coefficient of 0.497 among these traits,with 15.1%of correlations exceeding 0.8.Principal component analysis showed that the first five components explained 90%of the total variance,indicating significant trait interactions.Through feature engineering and gradient boosting regression,we developed a high-precision wind speed-ear displacement prediction model(R2=0.93)and identified 29 key traits critical for stalk lodging resistance.Sensitivity analysis revealed plant height as the most influential factor(sensitivity coefficient:−3.87),followed by traits of the 7th internode including epidermis layer thickness(0.62),pith area(−0.60),and lignin content(0.35).Our methodological framework not only provides quantitative insights into maize stalk lodging resistance mechanisms but also establishes a systematic approach for trait evaluation.The findings offer practical guidance for breeding programs focused on enhancing stalk lodging resistance and yield stability under climate change conditions,with potential applications in agronomic practice optimization and breeding strategy development. 展开更多
关键词 Maize Stalk lodging Wind tunnel testing Machine learning algorithms Indicator identification
Empirical tropospheric zenith wet delay models with strong generalization capability based on a robust machine learning fusion algorithm 认领 引用
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作者 Jiahao Zhang Qin Liang Yunqing Huang 《Geodesy and Geodynamics》 EI CSCD 2026年第2期211-224,共14页
Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.H... Tropospheric zenith wet delay(ZWD)plays a vital role in the analysis of space geodetic observations.In recent years,machine learning methods have been increasingly applied to improve the accuracy of ZWD calculations.However,a single machine learning model has limited generalization capabilities.To address these limitations,this study introduces a novel machine learning fusion(MLF)algorithm with stronger generalization capabilities to enhance ZWD modeling and prediction accuracy.The MLF algorithm utilizes a two-layer structure integrating extra trees(ET),backpropagation neural network(BPNN),and linear regression models.By comparing the root mean square error(RMSE)of these models,we found that both ET-based and MLF-based models outperform RF-based and BPNN-based models in terms of internal and external accuracy,across both surface meteorological data-based and blind models.The improvement in exte rnal accuracy is particularly significant in the blind models.Our re sults show that the MLF(with an RMSE of 3.93 cm)and ET(3.99 cm)models outperform the traditional GPT3model(4.07 cm),while the RF(4.21 cm)and BPNN(4.14 cm)have worse external accuracies than the GPT3 model.It is worth noting that the BPNN suffered from overfitting during external accuracy tests,which was avoided by the MLF.In summary,regardless of the availability of surface meteorological data,the MLF-based empirical models demonstrate superior internal and external accuracy compared to the other tested models in this study. 展开更多
关键词 Tropospheric zenith wet delay Machine learning Extra trees Machine learning fusion algorithm Empirical models
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Prediction of wear loss quantities of ferro-alloy coating using different machine learning algorithms 认领 引用 被引量：16
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作者 Osman ALTAY Turan GURGENC +1 位作者 Mustafa ULAS Cihan OZEL 《Friction》 SCIE EI CAS CSCD 2020年第1期107-114,共8页
In this study,experimental wear losses under different loads and sliding distances of AISI 1020 steel surfaces coated with(wt.%)50FeCrC‐20FeW‐30FeB and 70FeCrC‐30FeB powder mixtures by plasma transfer arc welding w... In this study,experimental wear losses under different loads and sliding distances of AISI 1020 steel surfaces coated with(wt.%)50FeCrC‐20FeW‐30FeB and 70FeCrC‐30FeB powder mixtures by plasma transfer arc welding were determined.The dataset comprised 99 different wear amount measurements obtained experimentally in the laboratory.The linear regression(LR),support vector machine(SVM),and Gaussian process regression(GPR)algorithms are used for predicting wear quantities.A success rate of 0.93 was obtained from the LR algorithm and 0.96 from the SVM and GPR algorithms. 展开更多
关键词 surface coating plasma transfer arc(PTA)welding wear prediction machine learning algorithms
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Modeling potential wetland distributions in China based on geographic big data and machine learning algorithms 认领 引用 被引量：6
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作者 Hengxing Xiang Yanbiao Xi +5 位作者 Dehua Mao Tianyuan Xu Ming Wang Fudong Yu Kaidong Feng Zongming Wang 《International Journal of Digital Earth》 SCIE EI 2023年第1期3706-3724,共19页
Climate change and human activities have reduced the area and degraded the functions and services of wetlands in China.To protect and restore wetlands,it is urgent to predict the spatial distribution of potential wetl... Climate change and human activities have reduced the area and degraded the functions and services of wetlands in China.To protect and restore wetlands,it is urgent to predict the spatial distribution of potential wetlands.In this study,the distribution of potential wetlands in China was simulated by integrating the advantages of Google Earth Engine with geographic big data and machine learning algorithms.Based on a potential wetland database with 46,000 samples and an indicator system of 30 hydrologic,soil,vegetation,and topographic factors,a simulation model was constructed by machine learning algorithms.The accuracy of the random forest model for simulating the distribution of potential wetlands in China was good,with an area under the receiver operating characteristic curve value of 0.851.The area of potential wetlands was 332,702 km2,with 39.0%of potential wetlands in Northeast China.Geographic features were notable,and potential wetlands were mainly concentrated in areas with 400-600 mm precipitation,semi-hydric and hydric soils,meadow and marsh vegetation,altitude less than 700 m,and slope less than 3°.The results provide an important reference for wetland remote sensing mapping and a scientific basis for wetland management in China. 展开更多
关键词 Potential wetland distribution machine learning algorithms geographic big data China wetland geographic features
Causal Representation Learning for Trustworthy Industrial Process Modeling 认领 引用
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作者 Liang Cao Fan Yang Youqing Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第7期1758-1760,共3页
Dear Editor,Industrial processes have become increasingly complex and dynamic,making robust and interpretable modeling techniques indispensable for safe and efficient operation.Although contemporary machine learning a... Dear Editor,Industrial processes have become increasingly complex and dynamic,making robust and interpretable modeling techniques indispensable for safe and efficient operation.Although contemporary machine learning algorithms exhibit strong predictive capabilities[1],[2],it remains challenging to ensure trustworthy performance—i.e.,models that reliably generalize to diverse operating regimes while offering intuitive explanations of their behavior.Achieving high accuracy with robust interpretability and stability(especially under process upsets or nonstationarities)stands as a pressing need in modern industrial environments[3],[4]. 展开更多
关键词 robust interpretability nonstationarities causal representation learning machine learning algorithms generalize diverse operating regimes offering intuitive explanations their trustworthy industrial process modeling robust interpretability stabi robust interpretable modeling techniques
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Machine learning for soil parameter inversion enhanced with Bayesian optimization 认领 引用 被引量：1
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作者 Anfeng HU Chi WANG +3 位作者 Senlin XIE Zhirong XIAO Tang LI Ang XU 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2025年第11期1034-1051,共18页
Machine learning(ML)has strong potential for soil settlement prediction,but determining hyperparameters for ML models is often intricate and laborious.Therefore,we apply Bayesian optimization to determine the optimal ... Machine learning(ML)has strong potential for soil settlement prediction,but determining hyperparameters for ML models is often intricate and laborious.Therefore,we apply Bayesian optimization to determine the optimal hyperparameter combinations,enhancing the effectiveness of ML models for soil parameter inversion.The ML models are trained using numerical simulation data generated with the modified Cam-Clay(MCC)model in ABAQUS software,and their performance is evaluated using ground settlement monitoring data from an airport runway.Five optimized ML models—decision tree(DT),random forest(RF),support vector regression(SVR),deep neural network(DNN),and one-dimensional convolutional neural network(1D-CNN)—are compared in terms of their accuracy for soil parameter inversion and settlement prediction.The results indicate that Bayesian optimization efficiently utilizes prior knowledge to identify the optimal hyperparameters,significantly improving model performance.Among the evaluated models,the 1D-CNN achieves the highest accuracy in soil parameter inversion,generating settlement predictions that closely match real monitoring data.These findings demonstrate the effectiveness of the proposed approach for soil parameter inversion and settlement prediction,and reveal how Bayesian optimization can refine the model selection process. 展开更多
关键词 ABAQUS software Bayesian optimization Machine learning(ML)algorithms Parameter inversion Settlement prediction
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