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Research on estimation of permeability coefficients in microbial geotechnical soils based on data-driven models

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2025年第1期25卷 22-31页

作者： Cheng, Mayao Chen, Linsheng Foshan Univ Coll Transportat & Civil Engn & Architecture Foshan 528225 Peoples R China

Microbial geotechnical soil permeability coefficient estimation prediction is extremely valuable for the development of soil engineering. The study proposes an integrated data-driven model combining three base learners, RVM, ANFIS and iTLBO-ELM, assigning corresponding weights to each base learner through the PLS integrated model combination model, and applying the model to the prediction of microbial geotechnical soil permeability coefficient estimation. The MAE of the proposed integrated model has lower values compared to the single model, but the two metrics MAPE and RMSE are not the lowest;however, the integrated model outperforms both iTLBO-ELM and RVM in terms of MAPE, and the estimated predictions of permeability coefficients for November data are better than those for May. For iTLBO-ELM and RVM, the MAPE of PLS decreased by 5.51% and 1.56% respectively in May and 3.46% and 1.24% respectively in October. The integrated data-driven model proposed in the study can effectively achieve the estimated prediction of microbial geotechnical soil permeability coefficients and facilitate the intelligent acquisition of engineering permeability coefficients.

关键词： data-driven models microorganisms geotechnical land permeability coefficients

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A review of the state of the art in solar photovoltaic output power forecasting using data-driven models

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ELECTRICAL ENGINEERING 2025年第4期107卷 4727-4770页

作者： Gupta, Ankur Kumar Singh, Rishi Kumar Maulana Azad Natl Inst Technol Dept Elect Engn Bhopal Madhya Pradesh India

The integration of Photovoltaic (PV) systems into grid has a detrimental effect on grid stability, dependability, reliability, efficiency, economy, planning and scheduling. Thus, a reliable PV output prediction is necessary for grid stability. This paper presents a detailed review on PV power forecasting technique. A detailed evaluation of forecasting techniques reveals that solar irradiance is highly correlated with PV output which makes it mandatory to classify the weather as well as analyze the cloudy motion. The researchers in literature dominantly preferred very short term and short-term horizon for PV forecasting. Different PV forecasting models categorized into physical, persistence, statistical, machine learning and hybrid model used in literature are studied and evaluated using various performance metrics such as root mean square error and mean absolute error for accuracy. A thorough comparison of various forecasting techniques suggests that deep neural networks with ensemble technique or hybrid techniques supersedes the traditional approaches of PV power forecasting in terms of efficiency and accuracy.

关键词： Forecast models Solar power Forecast horizon data-driven models Machine learning Solar radiation forecasting PV performance models

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State of health estimation of lithium-ion batteries based on feature optimization and data-driven models

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ENERGY 2025年 316卷

作者： Mu, Guixiang Wei, Qingguo Xu, Yonghong Li, Jian Zhang, Hongguang Yang, Fubin Zhang, Jian Li, Qi North Univ China Sch Energy & Power Engn Taiyuan 030051 Peoples R China Beijing Informat Sci & Technol Univ Mech Elect Engn Sch Beijing 100192 Peoples R China Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China Beijing Univ Technol Key Lab Enhanced Heat Transfer & Energy Conservat Beijing Key Lab Heat Transfer & Energy Convers MOEFac Environm & Life Beijing 100124 Peoples R China Univ Wisconsin Green Bay Richard J Resch Sch Engn Mech Engn Green Bay WI 54311 USA

With the widespread application of lithium-ion batteries in electric vehicles, accurately estimating their state of health (SOH) has become a key focus of research. This paper explores various feature optimization methods and data-driven models with different structures, and constructs various SOH estimation models suitable for lithiumion batteries. Based on battery testing data, multiple features are extracted from voltage and temperature to characterize the battery aging process. To reduce information redundancy among features, filtering methods, Principal Component Analysis (PCA), and Multi-dimensional Scaling (MDS) are applied for optimization, aiming to maximize feature information utilization. This paper compares four common and structurally different datadriven models: linear regression (LR), Gaussian process regression (GPR), support vector regression (SVR), and long short-term memory (LSTM) networks. The effectiveness of each model is validated using multi-feature inputs, and a multi-dimensional assessment of feature selection and data-driven model performance in SOH estimation is conducted, the average absolute error of all models under 60 % training set conditions is 0.8 %. The average absolute error of estimating the four batteries using the fused PCA features as input and the GPR model is less than 1.2 %. At the same time, using the optimized features as input reduces the average training time by 46.63 % compared to using multiple features as input. In summary, the combination of PCA features and GPR models has good performance in both estimation accuracy and computational efficiency for different batteries.

关键词： Lithium-ion battery State of health estimation Feature optimization data-driven models Principal component analysis Gaussian process regression

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data-driven models of steady state and transient operations of spiral-wound RO plant

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DESALINATION 2013年 316卷 154-161页

作者： Pascual, Xavier Gu, Han Bartman, Alex R. Zhu, Aihua Rahardianto, Anditya Giralt, Jaume Rallo, Robert Christofides, Panagiotis D. Cohen, Yoram Univ Rovira & Virgili Dept Engn Quim SCITA Tarragona 43007 Catalunya Spain Univ Calif Los Angeles Water Technol Res Ctr Dept Chem & Biomol Engn Los Angeles CA 90095 USA Univ Rovira & Virgili Dept Engn Informat & Matemat BioCENIT Tarragona 43007 Catalunya Spain

The development of data-driven RO plant performance models was demonstrated using the support vector regression model building approach. models of both steady state and unsteady state plant operation were developed based on a wide range of operational data obtained from a fully automated small spiral-wound RO pilot. Single output variable steady state plant models for flow rates and conductivities of the permeate and retentate streams were of high accuracy, with average absolute relative errors (AARE) of 0.70%-2.46%. Performance of a composite support vector regression (SVR) based model (for both streams) for flow rates and conductivities was of comparable accuracy to the single output variable models (AARE of 0.71%-2.54%). The temporal change in conductivity, as a result of transient system operation (induced by perturbation of either system pressure or flow rate), was described by SVR model, which utilizes a time forecasting approach, with performance level of less than 1% AARE for forecasting periods of 2 s to 3.5 min. The high level of performance obtained with the present modeling approach suggests that short-term performance forecasting models that are based on plant data, could be useful for advanced RO plant control algorithms, fault tolerant control and process optimization. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Desalination data-driven models Support vector regression Spiral-wound RO plant Process models

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data-driven models for Yacht Hull Resistance Optimization: Exploring Geometric Parameters Beyond the Boundaries of the Delft Systematic Yacht Hull Series

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IEEE ACCESS 2024年 12卷 76102-76120页

作者： Walker, Jake M. Coraddu, Andrea Oneto, Luca Delft Univ Technol Fac Mech Engn NL-2628 CD Delft Netherlands Univ Genoa Dept Informat Bioengn Robot & Syst Engn I-16126 Genoa Italy

Optimizing vessel hull resistance is pivotal for enhancing maritime performance and minimizing environmental impacts. Traditional methods combine expert intuition with data-driven models (DDMs), relying on parametrization to predict and optimize hull geometries using Experimental Fluid Dynamics (EFD) or Computational Fluid Dynamics (CFD) data. However, these conventional approaches are hampered by several limitations: they require significant human input, are computationally intensive and costly, and lack flexibility in adapting to new families of geometries or parameters beyond predefined ranges. Addressing these challenges, our research introduces a novel method that significantly reduces the need for human intervention, computational resources, and costs, while also improving the model's adaptability. By proposing a new a parametrization technique that accurately encompasses the Delft Systematic Yacht Hull Series (DSYHS), we demonstrate that DDMs can be effectively trained directly on EFD datasets. This eliminates the dependency on extensive CFD simulations or the generation of new EFD data tailored to a specific investigation. Our approach matches the performance of leading-edge CFD models, even in extrapolating conditions, with physical plausibility and minimal human oversight. The validation of our method under various and increasingly complex extrapolating scenarios, employing statistical analyses on the DSYHS EFD dataset and comparisons with state-of-the-art CFD models, underscores the effectiveness of our proposal. Furthermore, we demonstrated that our model can successfully optimize hull resistance when navigating geometric parameters outside the confines of the DSYHS validating our results through leading-edge CFD simulations. This work addresses the limitations of existing methodologies by offering a novel approach more accurate, efficient, cost-effective, flexible, automated, and robust to extrapolation for hull resistance optimization.

关键词： Computational fluid dynamics data-driven models DelftBlue Delft Systematic Yacht Hull Series extrapolation hull parametrization hull resistance optimization sailing yachts

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data-driven models of Monotone Systems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2024年第8期69卷 5294-5309页

作者： Makdesi, Anas Girard, Antoine Fribourg, Laurent Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst F-91190 Gif sur Yvette France Univ Paris Saclay CNRS Lab Methodes Formelles ENS Paris Saclay F-91190 Gif sur Yvette France

In this article, we consider the problem of computing from data guaranteed set-valued over-approximations of unknown monotone functions with additive disturbances. We provide a characterization of a simulating map that provably contains all monotone functions that are consistent with the data. This map is also minimal in the sense that any set-valued map containing all consistent monotone functions would also include the map we are proposing. We show that this minimal simulating map is interval-valued and admits a simple construction on a finite partition induced by the data. As the complexity of the partition increases with the amount of data, we also consider the problem of computing minimal interval-valued simulating maps defined on partitions that are fixed a priori. We present an efficient algorithm for their computation. We then use those data-driven over-approximations to build models for partially unknown systems where the unknown part is monotone. The resulting models are used to construct finite-state symbolic abstractions, paving the way for discrete controller synthesis methods to be applied. We extend our approach to handle systems with bounded derivatives and introduce an algorithm to calculate the bounds on those derivatives and on the disturbances from the data. We present several numerical experiments to test the performance of the introduced method and show that the data-driven abstractions are suitable for controller synthesis purposes.

关键词： Computational modeling data models Nonlinear systems Dynamical systems Probabilistic logic Predictive models Picture archiving and communication systems data-driven abstraction data-driven models monotone maps monotone systems symbolic control

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Shale gas production evaluation framework based on data-driven models

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Petroleum Science 2023年第3期20卷 1659-1675页

作者： You-Wei He Zhi-Yue He Yong Tang Ying-Jie Xu Ji-Chang Long Kamy Sepehrnoori State Key Laboratoryofiland Gas Rseroir Geoogy and Exloitation Southwest Petroleum UniverstyChengdu00SichuanChina Department of Petroleum and Geosystems Engineering The University of Texas at AustinAustinTX78712UA

Increasing the production and utilization of shale gas is of great significance for building a clean and low-carbon energy *** decline of gas production has been widely observed in shale gas *** to forecast shale gas production is still challenging due to complex fracture networks,dynamic fracture properties,frac hits,complicated multiphase flow,and multi-scale flow as well as data quality and *** work develops an integrated framework for evaluating shale gas well production based on data-driven ***,a comprehensive dominated-factor system has been established,including geological,drilling,fracturing,and production *** processing and visualization are required to ensure data quality and determine final data set.A shale gas production evaluation model is developed to evaluate shale gas production ***,the random forest algorithm is used to forecast shale gas *** prediction accuracy of shale gas production level is higher than 95%based on the shale gas reservoirs in ***-one wells are randomly selected to predict cumulative gas production using the optimal regression *** proposed shale gas production evaluation frame-work overcomes too many assumptions of analytical or semi-analytical models and avoids huge computation cost and poor generalization for numerical modelling.

关键词： Shale gas Production evaluation Production prediction data-driven models Carbon neutrality

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The multi-objective optimization of combustion system operations based on deep data-driven models

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ENERGY 2019年 182卷 37-47页

作者： Tang, Zhenhao Zhang, Zijun Northeast Elect Power Univ Sch Automat Engn Jilin Jilin Peoples R China City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China

Advancing methods for modeling combustion systems and optimizing their operations is beneficial to improve the combustion performance. This paper develops a deep data-driven framework for the optimization of combustion system operations. First, a deep belief network based method is developed to model both of the combustion efficiency and the NOx emission. Next, a multi-objective optimization model is developed by integrating the deep belief network based models, the considered operational constraints, and the control variable constraints. Two objectives, maximizing the combustion efficiency and minimizing the NOx emission, are considered in the optimization. Due to the nonlinearity and complexity of the optimization model, traditional exact solution methods are not applicable to solve it. A recently presented swarm intelligence method, the JAYA algorithm, is applied to obtain the optimal solutions of the developed optimization model. Advantages of using JAYA are proved by benchmarking against well-known computational intelligence methods. The feasibility and effectiveness of the developed framework for optimizing the combustion process using industrial data is validated by computational experiments. Results demonstrate the potential of further improving both of the combustion efficiency and NOx emission by optimizing control settings of the combustion system. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Deep learning Swarm intelligence Combustion process Multi-objective optimization data-driven models

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Applications of various data-driven models for the prediction of groundwater quality index in the Akot basin, Maharashtra, India

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2022年第12期29卷 17591-17605页

作者： Elbeltagi, Ahmed Pande, Chaitanya B. Kouadri, Saber Islam, Abu Reza Md. Towfiqul Mansoura Univ Fac Agr Agr Engn Dept Mansoura 35516 Egypt St Gadge Baba Amravati Univ Amravati 444602 Maharashtra India Kasdi Merbah Univ Ouargla Hydraul Fac Appl Sci Dept Civil Engn Lab Water & Environm Engn Sahara Milieu GEEMS Ouargla Algeria Begum Rokeya Univ Dept Disaster Management Rangpur 5400 Bangladesh CAAST CSAWM MPKV Rahuri Rahuri India

data-driven models are important to predict groundwater quality which is controlling human health. The water quality index (WQI) has been developed based on the physicochemical parameters of water samples. In this area, water quality is medium to poor and is found in saline zones;very high pH ranges are directly affected on the water quality in this study area. Conventional WQI computation demands more time and is often observed with enormous errors during the calculation of sub-indices. In the present work, four standalone methods such as additive regression (AR), M5P tree model (M5P), random subspace (RSS), and support vector machine (SVM) were employed to predict WQI based on variable elimination technique. The groundwater samples were collected from the Akot basin area, located in the Akola district, Maharashtra, in India. A total of nine different input combinations were developed in this study. The datasets were demarcated into two classes (ratio 80:20) for model construction (training dataset) and model verification (testing dataset) using a fivefold cross-validation approach. The models were assessed using statistical and graphical appraisal metrics. The best input combinations varied among the model, generally, the optimal input variables (EC, pH, TDS, Ca, Mg, and Cl) during the training and validation stages. Results show that AR outperformed the other data-driven models (R-2 = 0.9993, MAE = 0.5243, RMSE = 0.0.6356, %RAE = 3.8449, and RRSE% = 3.9925). The AR is proposed as an ideal model with satisfactory results due to enhanced prediction precision with the minimum number of input parameters and can thus act as the reliable and precise method in the prediction of WQI at the Akot basin.

关键词： Water quality index data-driven models Groundwater Additive regression

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Applications of data-driven models for Daily Discharge Estimation Based on Different Input Combinations

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WATER RESOURCES MANAGEMENT 2022年第7期36卷 2201-2221页

作者： Kumar, Manish Elbeltagi, Ahmed Pande, Chaitanya B. Ahmed, Ali Najah Chow, Ming Fai Pham, Quoc Bao Kumari, Anuradha Kumar, Deepak GBPUA&T Dept Soil & Water Conservat Engn Coll Technol Pantnagar 263145 Uttarakhand India Dr RPCAU Coll Agr Engn Pusa 848125 Bihar India Mansoura Univ Fac Agr Agr Engn Dept Mansoura 35516 Egypt MPKV CAAST CSAWM Rahuri Maharashtra India Univ Tenaga Nasl UNITEN Inst Energy Infrastruct IEI Kajang 43000 Selangor Malaysia Monash Univ Malaysia Sch Engn Discipline Civil Engn Jalan Lagoon Selatan Bandar Sunway 47500 Selangor Malaysia Univ Silesia Katowice Fac Nat Sci Inst Earth Sci Bedzinska St 60 PL-41200 Sosnowiec Poland

Accurate and reliable discharge estimation is considered vital in managing water resources, agriculture, industry, and flood management on the basin scale. In this study, five data-driven tree-based algorithms: M5-Pruned model-M5P (Model-1), Random Forest-RF (Model-2), Random Tree-RT (Model-3), Reduced Error Pruning Tree-REP Tree (Model-4), and Decision Stump-DS (Model-5) have been examined to measure the daily discharge of Govindpur site at Burhabalang river, India. The proposed models will be calibrated by daily 10-years time-series hydrological data (i.e., river stage (h) and daily discharge (Q)) measured from 2004 to 2013. In these models, 70% and 30% of the dataset were used for the training and testing stage for the reliability of the developed models. The precision of the models was optimized by investigating five different scenarios based on various time-lags combinations. Model's performance has been assessed and evaluated using five statistical metrics, namely, correlation coefficient (R-2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), and Root Relative Squared Error (RRSE). Results showed that Model-3 outperforms as compared to other proposed models. Machine learning models have been examined five scenarios of input variables during training and testing phases. In comparison of the Model-5 struggled in capturing the river's flow rate and showed poor performance in scenarios where R-2 metric values ranged from 0.64 to 0.94. Therefore, it can be concluded that the RT model could be used as a robust model for sustainable flood plain management.

关键词： Flood management data-driven models Daily discharge Burhabalang river

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