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Application of a Hybrid Model Based on ICEEMDAN, bayesian hyperparameter optimization GRU and the ARIMA in Nonferrous Metal Price Prediction

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CYBERNETICS AND SYSTEMS 2023年第1期54卷 27-59页

作者： Huang, Yu-ting Bai, Yu-long Ding, Lin Zhu, Ya-Jie Ma, Yong-Jie Northwest Normal Univ Coll Phys & Elect Engn Lanzhou 730070 Gansu Peoples R China

Nonferrous metals are the basic materials for national economic development. Accurate and robust price forecasting can effectively reduce investment costs and bring greater economic benefits to enterprises. But the violent fluctuation of nonferrous metal prices and the evolution of irregular cycles make metal price prediction difficult. A nonferrous metal price prediction model based on improved complementary ensemble empirical mode (ICEEMDAN) decomposition, a bayesian hyperparameter optimization gated recurrent neural network (GRU) and an integrated autoregressive moving average model (ARIMA) is proposed. First, ICEEMDAN is used to decompose the original metal price series, and then fuzzy entropy is applied to judge the complexity of confusion of the decomposed subsequence. An ARIMA model is used to predict the sequence with the minimum fuzzy entropy, and the other subsequences are predicted by the bayesian hyperparametric optimization GRU in this article. Finally, the prediction results are integrated to obtain the final prediction results of the original price series. To verify the reliability and practicability of the model in this article, different lengths of daily futures price series of zinc, aluminum, copper and gold prices on the London Metal Exchange and the Investing are used as the research data, three experiments utilizing 5 groups of metal price data and 10 comparison models are designed to verify the superiority of the proposed model. The results show that: (i) bayesian hyperparameter optimization can improve the prediction performance of a single model effectively. The hybrid method with fuzzy entropy can effectively improve the prediction accuracy. (ii) The proposed model outperforms the compared state-of-the art models in three-step metal prices forecasting.

关键词： bayesian hyperparameter optimization Fuzzy entropy gated recurrent neural network hybrid model nonferrous metals price prediction

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Assessment of landslide susceptibility mapping based on bayesian hyperparameter optimization: A comparison between logistic regression and random forest

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ENGINEERING GEOLOGY 2021年 281卷 105972-105972页

作者： Sun, Deliang Xu, Jiahui Wen, Haijia Wang, Danzhou Chongqing Normal Univ Key Lab GIS Applicat Res Chongqing 401331 Peoples R China Minist Educ Key Lab New Technol Construct Cities Mt Area Chongqing 400045 Peoples R China Natl Joint Engn Res Ctr Geohazards Prevent Reserv Chongqing 400044 Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Beijing Normal Univ Key Lab Environm Change & Nat Disaster Minist Educ Beijing 100875 Peoples R China

This study aims to develop two optimized models of landslide susceptibility mapping (LSM), i.e., logical regression (LR) and random forest (RF) models, premised on hyperparameter optimization using the bayesian algorithm, and compare their applicability in a typical landslide-prone area (Fengjie County, China). First, data for 1520 historical landslides occurring was collected from field investigations and literature reviews, to construct a spatial database of 16 conditioning factors. Subsequently, the bayesian algorithm was adopted to optimize the hyperparameters of the LR and RF models, premised on the dataset of all cells (including landslides and non-landslides). Finally, the two optimized models were estimated and compared with the area under curve (AUC) and confusion matrix. Based on the bayesian algorithm, the AUC value of the test dataset in LR model is improved by 4%, while the AUC value of the test dataset in RF model is improved by 10%, indicating that both models' hyperparameter optimization premised on the bayesian algorithm have delivered considerable impact on the accuracy of the models;so hyperparameter optimization is very important for models of LSM. Although both models exhibit reasonable performances, the optimized RF model premised on hyperparameter optimization has a better stability and predictive capability in case area. These findings make up for the crucial step in LSM (hyperparameter optimization) through the bayesian algorithm, and provide a comparison case between LR and RF models after comprehensive consideration of hyperparameter optimization, so as to increase the convincing power of the comparison of these models and provide a knowledge base for model comparison: comparison premised on hyperparameter optimization.

关键词： Landslide bayesian hyperparameter optimization Logical regression Random forest Landslide susceptibility mapping

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Bagging-based positive-unlabeled learning algorithm with bayesian hyperparameter optimization for three-dimensional mineral potential mapping

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COMPUTERS & GEOSCIENCES 2021年 154卷 104817-104817页

作者： Zhang, Zhiqiang Wang, Gongwen Liu, Chong Cheng, Lizhen Sha, Deming China Univ Geosci Sch Earth Sci & Resources Beijing 100083 Peoples R China Univ Quebec Abitibi Ternisamingue 445 Boul Univ Rouyn Noranda PQ J9X 5E4 Canada China Geol Survey Shenyang 110000 Peoples R China

Mineralization is a rare event. Hence, the geosciences datasets used for three-dimensional (3D) mineral potential mapping (MPM) are often imbalanced, consisting of scarce positive samples and abundant unlabeled data. Compared with selecting positive samples, it is challenging to select reliable negative samples in 3D MPM. However, the application of supervised machine learning algorithms in 3D MPM requires balanced positive and negative samples. Consequently, semi-supervised machine learning algorithms, which are only trained on positive samples, are widely used in 3D mineral prospecting. In this study, the bagging-based positive-unlabeled learning (PUL) algorithm that utilizes positive samples and unlabeled data in the training process was developed and applied to produce a 3D gold (Au) potential map in the Wulong Au district, China. This study employed bayesian hyperparameter optimization to tune the hyperparameters of the bagging-based PUL algorithm. The performance of the bagging-based PUL algorithm was further compared with that of the widely used random forest, weights-of-evidence, one-class support vector machine, and continuous weighting approach in 3D MPM. The results demonstrated that the bagging-based PUL algorithm outperformed the aforementioned widely used predictive methods. The 3D mineral targets obtained by the bagging-based PUL algorithm can be beneficial for subsurface Au exploration in the Wulong Au district of China.

关键词： 3D mineral potential mapping Bagging-based positive-unlabeled learning bayesian hyperparameter optimization Wulong Au district

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Solutions to Two- and Three-Dimensional Incompressible Flow Fields Leveraging a Physics-Informed Deep Learning Framework and Kolmogorov-Arnold Networks

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INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS 2025年第4期97卷 665-673页

作者： Jiang, Quan Gou, Zhiyong Guangxi Minzu Univ Sch Artificial Intelligence Nanning Peoples R China Guangxi Minzu Univ Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning Peoples R China

Physics-informed neural network (PINN) has become a potential technology for fluid dynamics simulations, but traditional PINN has low accuracy in simulating incompressible flows, and these problems can lead to PINN not converging. This paper proposes a physics-informed neural network method (KA-PINN) based on the Kolmogorov-Arnold Neural (KAN) network structure. It is used to solve two-dimensional and three-dimensional incompressible fluid dynamics problems. The flow field is reconstructed and predicted for the two-dimensional Kovasznay flow and the three-dimensional Beltrami flow. The results show that the prediction accuracy of KA-PINN is improved by about 5 times in two dimensions and 2 times in three dimensions compared with the fully connected network structure of PINN. Meanwhile, the number of network parameters is reduced by 8 to 10 times. The research results not only verify the application potential of KA-PINN in fluid dynamics simulations, but also demonstrate the feasibility of KAN network structure in improving the ability of PINN to solve and predict flow fields. This study can reduce the dependence on traditional numerical methods for solving fluid dynamics problems.

关键词： bayesian hyperparameter optimization flow field reconstruction and prediction fluid dynamics Kolmogorov-Arnold network physics-informed neural network

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The Driving Factors and Interactions Affecting the Prediction of Indoor Environmental Quality Satisfaction in Children's Homes

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CHILD INDICATORS RESEARCH 2025年 1-44页

作者： Liu, Sisi Zhao, Jingyuan Ji, Ruixin Changan Univ Sch Architecture Xian Peoples R China

In order to maintain the advancement of child protection, the active construction of Children's Homes has become a significant undertaking at the grassroots level of Chinese governance. This study aims to identify the drivers and their interactions that influence the Indoor environmental quality of Children's Homes in Xi'an from a child-centric perspective. To this end, physical environment measurements and questionnaires were employed to collect quantitative data, and Random forest and bayesian hyperparameter optimisation algorithms were used to quantify the relative importance ranking of the drivers and their interaction effects. The results demonstrate that the four drivers with the largest contribution are Interior decoration satisfaction, Color satisfaction, Usage requirements satisfaction, and Desk satisfaction. Additionally, the driver under different output labels exhibits varying contributions. Color satisfaction has the largest contribution when the output label is dissatisfaction, while Interior decoration satisfaction has the largest contribution when the output label is satisfaction. Furthermore, there are significant interactions between most of the drivers. The interaction is significantly stronger when the output label is satisfaction than when the output label is dissatisfaction. The present study offers quantitative recommendations for the construction of child-friendly Children's Homes, which will contribute to the enhancement of the child welfare system.

关键词： The Children's Home Children participation Indoor environmental quality satisfaction Child welfare Random forest bayesian hyperparameter optimization

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Parameter identification of minifrac numerical tests using a gradient boosting-based proxy model and genetic algorithm

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INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS 2024年第3期48卷 793-821页

作者： Abreu, Rafael Mejia, Cristian Roehl, Deane Pereira, Leonardo Cabral Pontif Catholic Univ Rio De Janeiro Dept Civil & Environm Engn Rio De Janeiro RJ Brazil Pontificia Univ Catolica Rio de Janeiro Tecgraf Inst Rio de Janeiro RJ Brazil Petrobras Petr Brasileiro SA Rio De Janeiro RJ Brazil

In recent years, the petroleum industry has devoted considerable attention to studying fluid flow inside fracture channels due to the discovery of naturally fractured reservoirs. The behavior prediction of these reservoirs is a well-known challenging task, in which the initial stage consists of identifying reservoir hydromechanical parameters. This work proposes an artificial intelligence-based approach to identify hydromechanical parameters from borehole injection pressure curves acquired through minifrac tests. This approach combines proxy modeling with a stochastic optimization algorithm to match observed and predicted borehole pressure curves. Therefore, a gradient boosting-based proxy model is built to predict borehole pressure curves, considering a proper strategy to develop time series modeling. Moreover, a bayesian optimization algorithm is applied to compute the gradient boosting hyperparameters. In this optimization scenario, this paper proposes an appropriate objective function established from the assumed time series prediction strategy and the k-fold cross-validation. Finally, a genetic algorithm is adopted to identify unknown hydromechanical parameters, solving an inverse problem. Based on the proposed workflow, a study of the importance of the hydromechanical parameters is developed. To assess the methodology applicability, the approach is employed to identify parameters in synthetic and field minifrac tests. The results present how this approach can adequately identify hydromechanical parameters of hydraulic fracturing problems.

关键词： bayesian hyperparameter optimization genetic algorithm gradient boosting machine minifrac test parameter identification

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Supervised Mineral Prospectivity Mapping via Class-Balanced Focal Loss Function on Imbalanced Geoscience Datasets

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MATHEMATICAL GEOSCIENCES 2023年第7期55卷 989-1010页

作者： Zhang, Zhiqiang Li, Yingjie Wang, Gongwen Carranza, Emmanuel John M. Yang, Shuren Sha, Deming Fan, Junjie Zhang, Xiang Dong, Yulong Hebei GEO Univ Hebei Key Lab Strateg Crit Mineral Resources Shijiazhuang 050031 Hebei Peoples R China Hebei GEO Univ Sch Earth Sci Shijiazhuang 050031 Hebei Peoples R China Hebei GEO Univ Hebei Prov Collaborat Innovat Ctr Strateg Crit Min Shijiazhuang 050031 Hebei Peoples R China China Univ Geosci Sch Earth Sci & Resources Beijing 100083 Peoples R China Univ Free State Fac Nat & Agr Sci Dept Geol Bloemfontein South Africa China Geol Survey Shenyang Geol Survey Shenyang 110000 Peoples R China China Geol Survey Geophys Survey Ctr Langfang 065000 Peoples R China

Deep learning algorithms are increasingly being used in mineral prospectivity mapping (MPM). As mineralization is a rare event, there are significantly fewer ore-bearing locations than non-ore-bearing locations. Therefore, a geoscience dataset used for training in MPM is typically imbalanced. However, few studies have addressed the training data imbalance issue in deep learning modeling for MPM. In addition to the imbalance in the number of samples in the training classes (i.e., ore-bearing and non-ore-bearing locations), the information-overlapping training samples, which cause an imbalance of information in feature space, also affect the performance of deep learning modeling for MPM. This study provides a novel class-balanced focal loss function (CBFL) to address the abovementioned data imbalance issue. Here, the long short-term memory network and the CBFL were utilized to produce a three-dimensional prospectivity model in the Wulong Au district, China. The hyperparameters of the deep learning model were tuned automatically using bayesian hyperparameter optimization. The performance of the CBFL was compared with that of the widely used cross-entropy loss and focal loss functions in deep learning modeling for MPM, and the results demonstrated that the CBFL outperformed the other loss functions. The three-dimensional targets delineated in this study can benefit subsurface mineral exploration in the future.

关键词： Long short-term memory Class-balanced focal loss function Imbalanced datasets bayesian hyperparameter optimization Mineral prospectivity mapping Wulong Au district

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BHO-XGBoost Prediction Model for Biological Activity of ERα Inhibitor Compounds 11

BHO-XGBoost Prediction Model for Biological Activity of ERα...

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11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Du, Yanxuan Xu, Zhengjie Huang, Jiaxin Lu, Cunhao Chen, Jian Glorious Sun School of Business and Management Donghua University Shanghai China School of Public Administration Xiangtan University Xiangtan China School of Mechanical Engineering Yangzhou University Yangzhou China

ISBN: (纸本)9798350333664

Breast cancer is the most common malignancy in women worldwide. The pathogenesis of the disease is closely related to the estrogen receptor alpha subtype (ERa). This paper presents a new optimization modeling method for breast cancer drug candidates. First of all, the research of random forest scoring mechanism was improved, using the maximum mutual information number - the Pearson correlation coefficient - distance correlation coefficient of three kinds of relevant indicators, select 20 each optimal molecular descriptors. Then, the bayesian hyperparameter optimization method is used to optimize the parameters of extreme gradient boosting (XGBoost). The research results show that the model proposed in this paper has the best prediction performance compared with classical prediction models such as unoptimized XGBoost and SVR, and the goodness of fit is improved by at least 26.67%. © 2023 IEEE.

关键词： Activity prediction bayesian hyperparameter optimization Breast cancer Feature selection Random forest XGBoost

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An integrated machine learning framework with uncertainty quantification for three-dimensional lithological modeling from multi-source geophysical data and drilling data

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ENGINEERING GEOLOGY 2023年第1期324卷

作者： Zhang, Zhiqiang Wang, Gongwen Carranza, Emmanuel John M. Liu, Chong Li, Junjian Fu, Chao Liu, Xinxing Chen, Chao Fan, Junjie Dong, Yulong Hebei GEO Univ Hebei Key Lab Strateg Crit Mineral Resources Shijiazhuang 050031 Hebei Peoples R China Hebei GEO Univ Sch Earth Sci Shijiazhuang 050031 Hebei Peoples R China China Univ Geosci Sch Earth Sci & Resources Beijing 100083 Peoples R China Univ Free State Fac Nat & Agr Sci Dept Geog Bloemfontein South Africa Univ Quebec Abitibi Temisamingue 445 Boul Univ Rouyn Noranda PQ J9X 5E4 Canada China Geol Survey Tianjin 300170 Peoples R China China Geol Survey Geophys Survey Ctr Langfang 065000 Peoples R China

Nowadays, it is commonplace for geological surveys to integrate multi-source geophysical data and drilling data in order to construct three-dimensional (3D) lithological models. In this context, manual translation of complex geophysical data into parameters used for 3D lithological modeling is challenging. Machine learning has recently shown great potential in 3D lithological modeling. However, the performance of machine learning algorithm is influenced by the imbalance in number of categories of lithological samples. In addition, the uncertainty associated with 3D lithological modeling by machine learning has rarely been quantified. This study presents a novel integrated machine learning framework to address the imbalance issue and to quantify uncertainty in 3D lithological modeling. As its novelty, our integrated machine learning framework can subdivide total uncertainty into aleatoric and epistemic uncertainties in the 3D lithological modeling procedure by stochastic gradient Langevin boosting. Another innovation of this study is the use of bayesian hyperparameter optimization for automatic tuning of hyperparameters of the integrated machine learning framework. The 3D lithological and uncertainty modeling case study in the Jiaojia-Sanshandao gold district of China demonstrated the superiority of our proposed integrated machine learning framework. The proposed framework has great potential in integrating multi-source geophysical and drilling data for 3D lithological and uncertainty modeling in engineering geology.

关键词： 3D lithological model Lithologic imbalance Aleatoric uncertainty Epistemic uncertainty bayesian hyperparameter optimization Machine learning

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Prediction method for the dynamic response of expressway lateritic soil subgrades on the basis of bayesian optimization CatBoost

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SOIL DYNAMICS AND EARTHQUAKE ENGINEERING 2024年 186卷

作者： Huang, Xuanjia Liu, Weizheng Guo, Qing Tan, Jiming Cent South Univ Sch Civil Engn Changsha 410075 Peoples R China Natl Engn Res Ctr High Speed Railway Construct Tec Changsha 410075 Peoples R China

Due to the limited features and poor accuracy of current methods for predicting the dynamic response of subgrades, this paper proposes an innovative approach that combines subgrade dynamic response field tests and machine learning (ML) technology. This method uses bayesian optimization XGBoost (BO-XGBoost), bayesian optimization LightGBM (BO-LightGBM), and bayesian optimization CatBoost (BO-CatBoost) models to analyze the effects of physical properties and stress conditions on the dynamic stress, dynamic acceleration, and dynamic displacement of the subgrade. The optimal ML model was selected on the basis of the residuals, coefficient of determination (R2), mean squared error (MSE), and mean absolute error of the prediction results. Using SHapley additive exPlanations (SHAP), the global importance, feature importance, and feature interaction behaviours of the optimal ML model input features were explained, and the main controlling features affecting the dynamic stress, dynamic acceleration, and dynamic displacement of the subgrade were obtained. The research results indicate that the prediction results of the BO-XGBoost, BO-LightGBM, and BO-CatBoost models for dynamic stress, dynamic acceleration, and dynamic displacement are mostly within the 10 % error range, and the R2 values of these three models are greater than 0.98. On the basis of the comparison results of the hyperparameter combinations, the objective of MSE (MSEobj), and the error evaluation metrics, the BO-CatBoost model yields the highest prediction accuracy, making it the optimal ML prediction model. This prediction method can quickly and intelligently obtain the main controlling features of dynamic stress, dynamic acceleration, and dynamic displacement, including depth (H), axle load (P), frequency (f), and moisture content (w). The boundary conditions for these four features are as follows: H > -1.3 m, P > 10 ton, f > 3.7 Hz, and w >18.1 %. The research results contribute to enhancing the service perf

关键词： Lateritic soil subgrade Machine learning bayesian hyperparameter optimization Vehicle test Dynamic response Prediction method

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