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Machine learning with hyperparameter optimization applied in facies-supported permeability modeling in carbonate oil reservoirs

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SCIENTIFIC REPORTS 2025年第1期15卷 1-37页

作者： Al-Mudhafar, Watheq J. Hasan, Alqassim A. Abbas, Mohammed A. Wood, David A. Basrah Oil Co Basra Iraq Basra Univ Oil & Gas Basrah Iraq DWA Energy Ltd Lincoln England

Most carbonate reservoirs exhibit heterogeneous pore distribution, whereby the matrix displays low permeability, thus impeding the flow of oil. On the other hand, highly permeable fractures function as the main flow conduits within such reservoirs. Permeability measurements are obtained from core and well test analysis, which are too expensive and not available for many wells. Therefore, accurate permeability prediction is a vital step in developing an efficient field development plan, as it plays a pivotal role in the accurate distribution of 3D petrophysical properties throughout a reservoir. Machine learning (ML) algorithms are now widely applied to predict core permeability using conventional well logs to build a model for permeability prediction in uncored wells. This review considers the performance of six ML algorithms (LightGBM, CATBoost, XGBoost, Adaboost, random forest and gradient boosting) for permeability prediction from a high-quality dataset. The dataset incorporates multiple well-log inputs (gamma ray, caliper, density, neutron porosity, shallow and deep resistivity, total porosity, spontaneous potential, water saturation, depth, and facies) in addition to direct core permeability and porosity measurements. Data pre-processing techniques applied include missing data imputation, scale correction, normalization with three different transformations (log, Box-Cox, and NST) and outlier detection. To enhance the ML performance, two search algorithms (random search and Bayesian optimization) are compared in their ability to tune the ML hyperparameters. There is a need to identify a suitable parameter space, especially when the target variable range is changing. ML performance was evaluated with four evaluation metrics (RMSE, MAE, R2, and Adjusted R2). Results showed that the XGBoost algorithm with configuration of (RS as search algorithm, Box Cox as the normalization method, Z-score for outlier detection, without scale correction, old parameter space) deliv

关键词： Well-log data quality Machine learning Facies classification Permeability modelling hyperparameter optimization Carbonate reservoirs

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Generalized Population-Based Training for hyperparameter optimization in Reinforcement Learning

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第5期8卷 3450-3462页

作者： Bai, Hui Cheng, Ran Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

hyperparameter optimization plays a key role in the machine learning domain. Its significance is especially pronounced in reinforcement learning (RL), where agents continuously interact with and adapt to their environments, requiring dynamic adjustments in their learning trajectories. To cater to this dynamicity, the Population-Based Training (PBT) was introduced, leveraging the collective intelligence of a population of agents learning simultaneously. However, PBT tends to favor high-performing agents, potentially neglecting the explorative potential of agents on the brink of significant advancements. To mitigate the limitations of PBT, we present the Generalized Population-Based Training (GPBT), a refined framework designed for enhanced granularity and flexibility in hyperparameter adaptation. Complementing GPBT, we further introduce Pairwise Learning (PL). Instead of merely focusing on elite agents, PL employs a comprehensive pairwise strategy to identify performance differentials and provide holistic guidance to underperforming agents. By integrating the capabilities of GPBT and PL, our approach significantly improves upon traditional PBT in terms of adaptability and computational efficiency. Rigorous empirical evaluations across a range of RL benchmarks confirm that our approach consistently outperforms not only the conventional PBT but also its Bayesian-optimized variant.

关键词： Training optimization Statistics Sociology Steady-state Computational modeling Bayes methods Evolutionary reinforcement learning population-based training hyperparameter optimization

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Multi-objective hyperparameter optimization of artificial neural network in emulating building energy simulation

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ENERGY AND BUILDINGS 2025年 337卷

作者： Ibrahim, Mahdi Harkouss, Fatima Biwole, Pascal Fardoun, Farouk Ouldboukhitine, Salah-Eddine Univ Clermont Auvergne Inst Pascal CNRS SIGMA Clermont F-63000 Clermont Ferrand France Al Maaref Univ Fac Engn Beirut Lebanon PSL Res Univ Ctr Proc MINES Paris PERSEE CS 10207 F-06904 Sophia Antipolis France Calif State Polytech Univ Sch Engn Arcata CA 95521 USA Lebanese Univ Fac Engn Beirut Lebanon

Artificial Neural Networks (ANNs) play a significant role in emulating Building Energy Simulation (BES), forecasting building energy consumption, and optimizing energy retrofit measures. Determining the appropriate architecture for an ANN that can manage multiple predictions simultaneously is a complex task that requires extensive experimentation and validation to achieve optimal performance. To address this challenge, a novel methodology referred to as Multi-Objective hyperparameter optimization of ANN (MOHO-ANN) is introduced. This approach involves aligning ANN prediction results with data to achieve optimal performance by tuning the ANN's hyperparameters. The methodology consists of calibrating the BES model, creating data using a model sampling step for ANN training, and formulating multi-objective optimization using hyperparameter tuning to obtain the set of optimal ANN architectures. Lastly, a Multi-Criteria Decision-Making (MCDM) step is employed to select the optimal ANN. This method is applied to retrofit an existing building by incorporating weather data, passive, and renewable retrofit measures. The ANN is used to predict hourly energy consumption, energy generation, and thermal comfort in the retrofit scenario. 1.75 million hourly data points have been used to train, validate, and test the ANN model. The results underscore the practicality of MOHO-ANN procedure in achieving predictions, demonstrating a coefficient of determination (R2) exceeding 0.98. Further research directions should consider employing the optimal architectural model to perform retrofit optimization by incorporating climate change.

关键词： Artificial Neural Network Machine learning hyperparameter optimization Building energy simulation Multi criteria decision making Multi-objective optimization

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hyperparameter optimization for Machine Learning Algorithms with Application to the MNIST and CIFAR-10 Datasets

Hyperparameter Optimization for Machine Learning Algorithms ...

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作者： Ying, DuoDuo University of California Los Angeles

学位级别：M.S., Master of Science/Master of Surgery

Deep learning algorithms are increasingly popular for complex prediction and classification tasks, and hyperparameter configurations play an important role in algorithm performance. However, the best hyperparameter tuning strategy still remain unresolved. While grid search and random search can be used to detect better hyperparameters, they are costly for big deep learning algorithms and may not produce the optimal result. Bayesian optimization balancing the exploration and exploitation trade-off shows significant improvement over grid search and random search in both efficiency and accuracy, but the algorithm makes computation on the entire domain, which can be still costly especially in higher dimension settings. In this paper, we propose a space adjustment algorithm selecting top percent points at each iteration that can be incorporated in additional to Bayesian optimization framework to further reduce experimental cost and improve optimization efficiency. We show our algorithm’s adaptable nature to the response surface of hyperparameter configuration space. We demonstrate our algorithm’s outstanding performance compared with Efficient Global optimization through a variety of test functions and an application to machine learning datasets.

关键词： Bayesian optimization Efficient global optimization Experimental design hyperparameter optimization Response surface methodology Machine learning

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A Pragmatic Approach to Fetal Monitoring via Cardiotocography Using Feature Elimination and hyperparameter optimization

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2024年第4期16卷 882-906页

作者： Hardalac, Firat Akmal, Haad Ayturan, Kubilay Acharya, U. Rajendra Tan, Ru-San Gazi Univ Dept Elect Elect Engn TR-06560 Ankara Turkiye Bahria Univ Dept Elect Engn Islamabad 44000 Pakistan Univ Southern Queensland Sch Math Phys & Comp Artificial Intelligence Applicat Lab Springfield Qld 4300 Australia Natl Heart Ctr Singapore Natl Heart Res Inst Singapore Singapore 169609 Singapore Duke NUS Med Sch 8 Coll Rd Singapore 169857 Singapore

Cardiotocography (CTG) is used to assess the health of the fetus during birth or antenatally in the third trimester. It concurrently detects the maternal uterine contractions (UC) and fetal heart rate (FHR). Fetal distress, which may require therapeutic intervention, can be diagnosed using baseline FHR and its reaction to uterine contractions. Using CTG, a pragmatic machine learning strategy based on feature reduction and hyperparameter optimization was suggested in this study to classify the various fetal states (Normal, Suspect, Pathological). An application of this strategy can be a decision support tool to manage pregnancies. On a public dataset of 2126 CTG recordings, the model was assessed using various standard CTG dataset specific and relevant classifiers. The classifiers' accuracy was improved by the proposed method. The model accuracy was increased to 97.20% while using Random Forest (best classifier). Practically speaking, the model was able to correctly predict 100% of all pathological cases and 98.8% of all normal cases in the dataset. The proposed model was also implemented on another public CTG dataset having 552 CTG signals, resulting in a 97.34% accuracy. If integrated with telemedicine, this proposed model could also be used for long-distance "stay at home" fetal monitoring in high-risk pregnancies.

关键词： Cardiotocography Fetal wellbeing Machine learning Classification hyperparameter optimization Feature elimination

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Integrated Ensemble Learning, Feature Selection, and hyperparameter optimization for Accurate Mapping of Typical Vegetation on Landslides in the Upper Reaches of the Yellow River Using Gaofen-2 Imagery

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 15702-15720页

作者： Zhou, Zhe Hu, Xiasong Feng, Guojie Liu, Changyi Li, Xilai Qinghai Univ Sch Geol Engn Xining 810016 Peoples R China Qinghai Prov Nat Resources Comprehens Survey & Mon Xining 810001 Peoples R China Qinghai Univ State Key Lab Plateau Ecol & Agr Xining 810016 Peoples R China

Vegetation plays a crucial role in terrestrial ecosystems, especially in its influence on shallow landslide occurrences. However, in landslide-prone regions, such as the Xiazangtan area in the upper reaches of the Yellow River, sparse and fragmented vegetation presents classification challenges. This study explores vegetation composition utilizing very-high-resolution Gaofen-2 satellite imagery and three feature selection (FS) methods: ReliefF, recursive feature elimination, and logistic regression, optimized with tree-structured Parzen estimator. Five machine learning algorithms were evaluated, including random forest (RF), support vector machine (SVM), extreme gradient boosting, light gradient boosting machine (LightGBM), and category boosting. The results show that the integration of FS with ensemble learning consistently achieves an overall accuracy of over 0.8. The ReliefF-LightGBM combination outperforms others, showcasing superior efficiency. Notably, the boosting algorithm in ensemble learning combined with FS outperforms traditional SVM and RF. This study demonstrates the effectiveness of combining hyperparameter optimization, FS methods, and the boosting algorithm of ensemble learning for vegetation classification purposes. This approach provides a reliable means of accurately identifying and detecting vegetation in the monitoring area, thereby offering an effective strategy for mitigating shallow landslides in the surrounding regions.

关键词： Vegetation mapping Landslides Accuracy Boosting Remote sensing Soil Ensemble learning feature selection (FS) Gaofen-2 hyperparameter optimization Xiazangtan landslide

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Automating hyperparameter optimization in geophysics with Optuna: A comparative study

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GEOPHYSICAL PROSPECTING 2024年第5期72卷 1778-1788页

作者： Almarzooq, Hussain bin Waheed, Umair Saudi Aramco Geophys Imaging Dept Dhahran 31311 Saudi Arabia King Fahd Univ Petr & Minerals Dept Geosci Dhahran Saudi Arabia

Deep learning has gained attraction amongst geophysicists for solving complex longstanding problems. Nevertheless, proper hyperparameter optimization methodologies remain critically underexplored in geophysical deep learning research. This paper attempts to first highlight the importance of hyperparameter optimization and then showcase two geophysics-related deep learning examples where a grid search and Optuna framework (an automated optimization approach) were used for hyperparameter optimization. We consider two geophysical problems related to denoising seismic traces and the inversion of seismic traces for velocity information. In both cases, models created based on Optuna hyperparameter optimization were able to perform better than those created through grid search. The most significant advantage of Optuna, however, is having quantifiable results to justify the choice of a neural network architecture, depth and other hyperparameters rather than relying on inefficient methods of exploring the hyperparameter space such as a trial-and-error or grid search. This study aims to stimulate further exploration and adoption of these frameworks, pushing the boundaries of current deep learning based geophysical problem-solving methodologies towards full automation.

关键词： deep learning hyperparameter optimization inversion noise Optuna framework seismic signal processing

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hyperparameter optimization for Convolutional Neural Networks with Genetic Algorithms and Bayesian optimization 8

Hyperparameter Optimization for Convolutional Neural Network...

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IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Puentes G, David E. Barrios H, Carlos J. Navaux, Philippe O. A. Univ Ind Santander SC3UIS Bucaramanga Colombia Univ Fed Rio Grande do Sul Informat Inst Porto Alegre RS Brazil

ISBN: (数字)9781665488587

ISBN: (纸本)9781665488587

Deep learning (DL) has emerged as a leading approach to solving complex problems in multiple areas. Its success is mainly due to technological development, large volumes of data, and various algorithms based on artificial neural networks (ANN). However, their functioning and performance are sensitive to the configuration of hyperparameters, which result from trial and error processes based on the knowledge and creativity of experts. In this sense, to reduce subjective associated with human intervention, an approach suggests tackling the tuning of hyperparameters as an optimization problem. Here, the objective is to find a configuration that maximizes some performance metrics. The present work presents an implementation and comparison of three optimization strategies (Random Search, Bayesian optimization, and Genetic Algorithm) for image classification in the CIFAR-10 dataset using convolutional neural networks, showing that algorithm performance and their solutions are tightly bound with the kind of architecture and search space.

关键词： hyperparameter optimization deep learning genetic algorithms bayesian optimization convolutional neural network

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Streamlining hyperparameter optimization for radiation emulator training with automated Sherpa

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GEOSCIENCE LETTERS 2024年第1期11卷 19页

作者： Roh, Soonyoung Kim, Park Sa Song, Hwan-Jin Kyungpook Natl Univ Ctr Atmospher REmote Sensing CARE Daegu South Korea Korea Meteorol Adm Natl Inst Meteorol Sci Jeju Do South Korea Kyungpook Natl Univ Dept Atmospher Sci Weather Extremes Educ & Res Team BK21 Daegu South Korea Kyungpook Natl Univ KNU Inst Basic Sci KNU LAMP Res Ctr Daegu South Korea

This study aimed to identify the optimal configuration for neural network (NN) emulators in numerical weather prediction, minimizing trial and error by comparing emulator performance across multiple hidden layers (1-5 layers), as automatically defined by the Sherpa library. Our findings revealed that Sherpa-applied emulators consistently demonstrated good results and stable performance with low errors in numerical simulations. The optimal configurations were observed with one and two hidden layers, improving results when two hidden layers were employed. The Sherpa-defined average neurons per hidden layer ranged between 153 and 440, resulting in a speedup relative to the CNT of 7-12 times. These results provide valuable insights for developing radiative physical NN emulators. Utilizing automatically determined hyperparameters can effectively reduce trial-and-error processes while maintaining stable outcomes. However, further experimentation is needed to establish the most suitable hyperparameter values that balance both speed and accuracy, as this study did not identify optimized values for all hyperparameters. The study aimed to enhance the efficiency of neural network emulators for numerical weather prediction by reducing trial and error through the use of Sherpa-defined neurons across multiple hidden layers. Optimal configurations with one and two hidden layers were identified, leading to a speed increase of 7-12 times and maintaining stable performance with minimal errors. While automatically determined hyperparameters have shown promise in decreasing trial and error, additional research is needed to achieve the optimal balance between speed and accuracy in weather prediction.

关键词： Sherpa library hyperparameter optimization Neural-network emulators Numerical weather prediction RRTMG-K radiation

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TransUNet Image 3D Reconstruction with hyperparameter optimization

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2025年第3期32卷 1133-1142页

作者： Wang, Xiaofang Luo, Zhihao Gou, Mingrui Mao, Kerui Zhou, Liang Geely Univ China Chengdu 641423 Sichuan Peoples R China

Ancient architecture is characterized by its complexity and exquisite structure, but most existing images are in 2D format. This study proposes a TransUNet-based 3D reconstruction method with hyperparameter optimization for depth prediction to enhance the effectiveness, accuracy, and efficiency of reconstructing ancient buildings from 2D images. The method employs Restricted Boltzmann Machine (RBM) for depth prediction and an optimized ant colony algorithm for network parameter optimization. Experiments demonstrate that the proposed method achieves an average F1 score of 96.8% in reconstructing ancient buildings, outperforming other algorithms in terms of processing time and efficiency. The results validate the superiority of the proposed algorithm in processing images of ancient architecture, improving measurement accuracy and reducing execution time. This study contributes to the digitization and preservation of cultural heritage.

关键词： hyperparameter optimization neural networks TransUNet 3D Reconstruction

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