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data-driven models for accurate groundwater level prediction and their practical significance in groundwater management

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JOURNAL OF HYDROLOGY 2022年 608卷 127630-127630页

作者： Sun, Jianchong Hu, Litang Li, Dandan Sun, Kangning Yang, Zhengqiu Beijing Normal Univ Coll Water Sci Beijing 100875 Peoples R China Beijing Key Lab Urban Hydrol Cycle & Sponge City Beijing 100875 Peoples R China Minist Educ Engn Res Ctr Groundwater Pollut Control & Remedia Beijing 100875 Peoples R China Environm Remediat Co Ltd Beijing Construct Engn Grp Beijing 100055 Peoples R China

The overexploitation of groundwater resource and its delicacy management has gained increasing attentions in recent years worldwide because of causing a series of serious environmental and geological problems. Currently, accurately predicting the groundwater level (GWL) is an important issue in effective groundwater management across scales. In the present study, three popularly-used data-driven models, which are an autoregressive inte-grated moving average (ARIMA), a back-propagation artificial neural network (BP-ANN) and long short-term memory (LSTM), were established in five zones with different hydrogeological properties to explore the model's accuracy in predicting the GWL at monthly and daily scales in a Northern Plain in China. The developed models were evaluated by both the Nash-Sutcliffe efficiency coefficient (NSE) and root mean square error (RMSE). The results indicate that the performance of the LSTM model is best at monthly time scales with the NSEs greater than 0.76 and RMSEs smaller than 1.15 m in each zone during the training period and demonstrate a good performance at daily time scales with the NSEs greater than 0.9 and the RMSEs smaller than 0.55 m at a local area. Meanwhile, the tempo-spatial distribution of the probability of drawdowns from the LSTM model was estimated by using the object-oriented spatial statistical (O2S2) method. The results show that cumulative drawdowns greater than 10 m are mainly concentrated in water source areas, with probabilities over 0.7 from 2003 to 2010 and declining to less than 0.3 from 2011 to 2014. The GWL rose generally in the study area from 2015 to 2018, but the probability of a drawdown with more than 5 m exceeded 0.8 in Zone V because of continuing groundwater exploitation. This study formulates a framework on developing effective data-driven models for predicting the GWL across scales which have the potential to aid groundwater management.

关键词： Groundwater management data-driven models LSTM O2S2 The Beijing plain

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Predicting mechanical behavior of different thin-walled tubes using data-driven models

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MATERIALS TODAY COMMUNICATIONS 2024年 40卷

作者： Kuleyin, Hamdi Karabacak, Yunus Emre Gumruk, Recep Recep Tayyip Erdogan Univ Dept Mech Engn Rize Turkiye Karadeniz Tech Univ Dept Mech Engn Trabzon Turkiye

The mechanical behavior of thin-walled tubes holds great significance in various engineering applications, ranging from aviation to civil engineering. This study introduces an innovative approach by utilizing machine learning techniques such as Gaussian Process Regression (GPR), Regression Trees (RTs), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs) to build data-driven models for predicting the mechanical behavior of different types of thin-walled tubes. To achieve this, we gather datasets encompassing various parameters, including material properties, pressure, and displacement. The dataset is a MATLAB array with dimensions of 2800x4. We partitioned the datasets into a training set (70 %, 1960 samples), a validation set (15 %, 420 samples), and a testing set (15 %, 420 samples). The R-squared values for the validation set are as follows: GPR (0.93), RT (0.88), ANN (0.84), and SVM (0.83). For the test set, the R-squared values are: GPR (0.80), RT (0.79), ANN (0.86), and SVM (0.82). Employing these machine learning techniques, we develop models that can predict mechanical properties for each tube category, such as compressional behavior and impact force. These models demonstrate promising accuracy and generalizability, making them valuable tools for engineering design and analysis.

关键词： Thin-walled tubes Mechanical behavior data-driven models Machine learning techniques

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Bacteria cells estimation in wastewater treatment plants using data-driven models ⁎

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IFAC-PapersOnLine 2024年第14期58卷 718-723页

作者： Fahad Aljehani Ibrahima N’Doye Pei-Ying Hong Mohammad K. Monjed Taous-Meriem Laleg-Kirati Computer Electrical and Mathematical Sciences and Engineering Division (CEMSE) King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia Department of Biology Faculty of Science Umm Al-Qura University Makkah 21961 Saudi Arabia National Institute for Research in Digital Science and Technology (INRIA) Saclay France

Estimating or predicting the concentrations of bacteria cells is crucial for achieving better control of the operation of wastewater treatment plants. However, measuring the bacterial concentration along the influent wastewater stage to the treated effluent process is challenging as it involves lab access and trained personnel. Additionally, wastewater plants are generally nonlinear systems involving time-varying physical and biological characteristics, increasing the difficulty in estimating the bacterial concentration from a model-based approach. This paper proposes data-driven models based on four machine-learning models to estimate the bacterial cell density with a limited dataset in a wastewater treatment plant. The performance results demonstrate that the machine-learning models (i.e., K-Nearest Neighbour (kNN), Random Forest (RF), Gradient Boosting Regression (GBR), Extreme Gradient Boosting (XGB)) have the potential to estimate accurately the bacterial concentration. RF displays better bacteria estimation in the influent by 10.7% compared to GBR and 7.4% compared to XGB and kNN. Whereas for the effluent, XGB improved the estimation by 12.8%, 2.4%, 14.6% compared to GBR, RF, and kNN, respectively. Also, results show that conductivity as a single feature is the most significant parameter affecting the bacterial cell estimation in the influent stage for the four machine learning algorithms. Similarly, the chemical oxygen demand (COD) and turbidity have pronounced effects in the effluent stage. These results reveal potential signs of designing a universal data-driven model-based approach applicable for bacteria estimation at influent and effluent based on the minimum feature combinations (conductivity, COD, and turbidity).

关键词： Wastewater treatment plant data-driven models bacterial estimation models machine learning algorithms

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B-AMA: A Python-coded protocol to enhance the application of data-driven models in hydrology

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ENVIRONMENTAL MODELLING & SOFTWARE 2023年 160卷

作者： Amaranto, Alessandro Mazzoleni, Maurizio RSE Ric Sistema Energet Sustainable Dev & Energy Sources Dept Via R Rubattino 54 I-20134 Milan Italy Vrije Univ Amsterdam Inst Environm Studies Amsterdam Netherlands

In this manuscript, we present B-AMA (Basic data-driven models for All), an easy, flexible, fully coded Pythonwritten protocol for the application of data-driven models (DDM) in hydrology. The protocol, which is open source and freely available for academic and non-commercial purposes, has been realized to allow early career scientists, with a basic background in programming, to develop DDM ensuring that no stones are left unturned through their implementation. B-AMA embeds data splitting, feature selection, hyperparameter optimization, and performance metrics. A Jupyter notebook with a practical workflow is available to guide the users through the protocol employment, while visualization tools allow efficient investigation and communication of results. We tested B-AMA across four hydrological applications to explore DDM applicability across temporal resolutions, time series lengths, and autocorrelations. B-AMA showed great accuracy and reasonable computational time, making the protocol ideal for educational purposes and for the development of DDM-based forecasts of hydrological time-series.

关键词： Modeling protocol data-driven models Hydrological predictions

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Comparison and interpretation of data-driven models for simulating site-specific human-impacted groundwater dynamics in the North China Plain

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JOURNAL OF HYDROLOGY 2023年 616卷

作者： Jing, Hao He, Xin Tian, Yong Lancia, Michele Cao, Guoliang Crivellari, Alessandro Guo, Zhilin Zheng, Chunmiao Harbin Inst Technol Sch Environm Harbin Peoples R China Southern Univ Sci & Technol Sch Environm Sci & Engn State Environm Protect Key Lab Integrated Surface Shenzhen Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Water Cycle Simulat & Regulat Beijing Peoples R China China Univ Geosci Sch Water Resources & Environm Beijing Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China EIT Inst Adv Study Ningbo Peoples R China

data-driven models (DDMs) have gained increasing popularity in groundwater hydrology in recent years due to the advancement of machine learning algorithms and the flexibility of easily accessible data. For groundwater purposes, the differences in deep learning (DL) algorithms compared with traditional tree-based machine learning (TB) algorithms have not been fully investigated, and the importance of different input features for groundwater level simulation has rarely been addressed. In this study, we test and validate six DDMs for simulating the groundwater levels of the North China Plain (NCP) at selected boreholes. The NCP is a large alluvial aquifer system (144,000 km2) overexploited by massive water withdrawals since the 1960s. In our simulations, four DDMs were tree-based (random forest, XGBoost, gradient boosting regression, LightGBM), and two were deep learning algorithms (Vanilla-LSTM and encoder-decoder-LSTM). The results showed that deep -learning-based DDMs provided a better correlation to observed data than tree-based models. Additionally, encoder-decoder-LSTM had the best model performance among all DDMs, and it had the ability to generate compelling results (R2 = 0.61, RMSE = 0.73 m), although each individual driving factor had a low correlation to the simulation target. GINI coefficient analysis and permutation feature importance analysis were used to determine the ranking of different model driving factors for the interpretable results. The results showed that the factors related to human activities had a much stronger impact on groundwater level variation than other factors. A preprocessing procedure of the driving factors helps produce satisfactory simulations aimed at sustainable water management and aquifer restoration, especially in data-scarce areas.

关键词： Groundwater dynamics data-driven models Machine learning LSTM North China Plain

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Predicting personalized thermal comfort in stratified micro-environments using turbulent jet theories and data-driven models

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BUILDING AND ENVIRONMENT 2023年第1期230卷

作者： Tian, Xue Lin, Zhang City Univ Hong Kong Dept Architecture & Civil Engn Hong Kong Peoples R China City Univ Hong Kong Div Bldg Sci & Technol Hong Kong Peoples R China

Stratified micro-environments offer different air distribution stratifications for each occupant and work as a solution for satisfying individual thermal preferences and improving energy efficiency. It is essential to accu-rately predict the thermal comfort provided in the micro-environments for efficient control. A computational fluid dynamics (CFD) model, validated against experimental measurements, is used to generate data with different conditions of operations systematically. Two acknowledged jet theories, the Abramovich and the Koestel systems, are applied with linear regression models to predict the predicted mean vote (PMV) and the draft rating (DR). data-driven models include (1) back propagation neural network (BPNN), as a presentative of artificial neural network (ANN), and (2) support vector machine (SVM), as a presentative of non-ANN, are compared with the jet theory models. The demarcation value of the Archimedes number (Ar) between the free-jet condition and the non-free-jet condition is 0.0625. Jet theory models are applied for thermal comfort predictions with free-jet conditions, achieving low mean absolute error (MAE) values of the PMV and the DR, i.e., below 0.2 and 3%, respectively. A sufficient accuracy is also reached for experimental measurements in published studies, with the lowest MAE values of 0.09 for the PMV and 2.10% for the DR. data-driven models are capable of similarly accurate predictions, advancing their generality for both the free-jet and non-free-jet conditions. The input sets handled by jet theory models perform better than the monitored supply air parameters, showing their value for improving data-driven models with limited data size.

关键词： Individual thermal preference Stratified micro-environments Personalized air conditioning Turbulent jet theory data-driven models

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PV power forecasting based on data-driven models: a review

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INTERNATIONAL JOURNAL OF SUSTAINABLE ENGINEERING 2021年第6期14卷 1733-1755页

作者： Gupta, Priya Singh, Rhythm Indian Inst Technol Roorkee Dept Hydro & Renewable Energy Roorkee 247667 Uttarakhand India

Accurate PV power forecasting techniques are a prerequisite for the optimal management of the grid and its stability. This paper presents a review of the recent developments in the field of PV power forecasting, mainly focusing on the literature which uses ML techniques. The ML techniques (sub-branch of artificial intelligence) are extensively used due to their ability to solve nonlinear and complex data structures. PV power forecasting can either be direct, or indirect, which involves solar irradiance forecast model, plane of array irradiance estimation model, and PV performance model. This paper presents a review of both of these pathways of PV power forecasting based on the proposed methodology, forecast horizons and the considered input parameters. In case of unavailability of historical PV power for a new PV plant and in case of failure of real-time data acquisition, indirect PV power forecasting can be a viable alternative. Although the performance ranking of various ML models is complicated and no model is universal, recent studies suggest that methodologies like deep neural networks and ensemble or hybrid models outperform conventional methods for short-term PV forecasting. Recent articles also present the various intelligent optimisation and data-preparation techniques to improve performance accuracy.

关键词： PV power forecasting forecast horizon data-driven models machine learning deep learning ensemble methods solar radiation forecasting POA irradiance PV performance models

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Re-Evaluation of Oil Bearing for Wells with Long Production Histories in Low Permeability Reservoirs Using data-driven models

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ENERGIES 2023年第2期16卷 677页

作者： Xue, Yongchao Cao, Chong Jin, Qingshuang Wang, Qianyu China Univ Petr State Key Lab Petr Resources & Prospecting Beijing 102249 Peoples R China

The re-evaluation of oil-bearing wells enables finding potential oil-bearing areas and estimating the results of well logging. The re-evaluation of oil bearing is one of the key procedures for guiding the development of lower production wells with long-term production histories. However, there are many limitations to traditional oil-bearing assessment due to low resolution and excessive reliance on geological expert experience, which may lead to inaccurate and uncertain predictions. Based on information gain, three data-driven models were established in this paper to re-evaluate the oil bearing of long-term production wells. The results indicated that the RF model performed best with an accuracy of 95.07%, while the prediction capability of the neural network model was the worst, with only 79.8% accuracy. Moreover, an integrated model was explored to improve model accuracy. Compared with the neural network, support vector machine, and random forest models, the accuracy of the fusion model was improved by 20.9%, 8.5%, and 1.4%, which indicated that the integrated model assisted in enhancing the accuracy of oil-bearing prediction. Combined with the long-term production characteristics of oil wells in the actual oil field, the potential target sweet spot was found, providing theoretical guidance for the effective development of lower production wells in the late period of oilfield development.

关键词： formation evaluation oil bearing data-driven models long production history low permeability reservoirs

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Development of the Questionnaire on the Acceptance of Automated Driving (QAAD): data-driven models for Level 3 and Level 5 automated driving

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TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR 2021年 83卷 42-59页

作者： Weigl, Klemens Schartmueller, Clemens Riener, Andreas Steinhauser, Marco TH Ingolstadt Human Comp Interact Grp Ingolstadt Germany Catholic Univ Eichstatt Ingolstadt Dept Psychol Eichstatt Germany Johannes Kepler Univ Linz Linz Austria

Automated driving comes with many promises like zero traffic casualties that are, however, only realizable given their technological development and public acceptance for wide-spread deployment. To investigate the potential acceptance, we developed a new data-driven ques-tionnaire focusing on drivers and barriers of the anticipated possible (non-)adoption of automated driving (AD). Therefore, we conducted a cross-sectional questionnaire study with 725 re-spondents (351 female, 374 male) ranging from 18 to 96 years. We applied exploratory and confirmatory factor analyses and structural equation modeling, to pursue the overarching goal to develop the QAAD questionnaire (short and long version for SAE Level 3 (L3) and 5 (L5) AD). Hence, we identified the three latent factors PRO (positive aspects), CON (negative aspects), and NDRTs (non-driving related tasks) of L3 (short: 9 items;long: 16) and L5 (short: 11, long: 17), respectively. Additionally, we queried general questions on AD (8 items) and extracted the two factors Early Adoption/Pro AD and Sustainability. Our findings and the goodness-of-fit indices suggest data-driven models for L3 and L5 automated driving and on general aspects focusing on early adoption and sustainability in the context of AD. They can be applied in future research settings, in particular, in (quasi-)experimental L3 and L5 AD studies and in population surveys on AD. The evidence of the presented study should be validated and compared to other question-naires on AD in different countries around the globe.

关键词： Automated driving Level 3 Level 5 Questionnaire development data-driven models Sustainability

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Unlocking the Potential of Remanufacturing Through Machine Learning and data-driven models-A Survey

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ALGORITHMS 2024年第12期17卷 562-562页

作者： Kim, Yong Han Ye, Wei Kumar, Ritbik Bail, Finn Dvorak, Julia Tan, Yanchao May, Marvin Carl Chang, Qing Athinarayanan, Ragu Lanza, Gisela Sutherland, John W. Li, Xingyu Nath, Chandra Purdue Univ Environm & Ecol Engn 500 Cent Dr W Lafayette IN 47907 USA Purdue Univ Sch Engn Technol 401 N Grant St W Lafayette IN 47907 USA Karlsruhe Inst Technol KIT Wbk Inst Prod Sci Kaiserstr 12 D-76131 Karlsruhe Germany Univ Virginia Dept Mech & Aerosp Engn Charlottesville VA 22903 USA

As a key strategy for achieving a circular economy, remanufacturing involves bringing end-of-use (EoU) products or cores back to a 'like new' condition, providing more affordable and sustainable alternatives to new products. Despite the potential for substantial resources and energy savings, the industry faces operational challenges. These challenges arise from uncertainties surrounding core quality and functionality, return times, process variation required to meet product specifications, and the end-of-use (EoU) product values, as well as their new life expectancy after extended use as a 'market product'. While remanufacturing holds immense promise, its full potential can only be realized through concerted efforts towards resolving the inherent complexities and obstacles that impede its operations. Machine learning (ML) and data-driven models emerge as transformative tools to mitigate numerous challenges encountered by manufacturing industry. Recently, the integration of cutting-edge technologies, such as sensor-based product data acquisition and storage, data analytics, machine health management, artificial intelligence (AI)-driven scheduling, and human-robot collaboration (HRC), in remanufacturing procedures has received significant attention from remanufacturers and the circular economy community. These advanced computational technologies help remanufacturers to implement flexible operation scheduling, enhance quality control, and streamline workflows for EoU products. This study embarks on a comprehensive review and in-depth analysis of state-of-the-art algorithms across various facets of remanufacturing processes and operations. Additionally, it identifies key challenges to advancing remanufacturing practices through data-driven and ML methods and uncovers research opportunities in synergy with smart manufacturing techniques. The study aims to offer guidelines for stakeholders and to reinforce the industry's pivotal role in circular economy initiatives.

关键词： remanufacturing circular economy machine learning data-driven models sustainability

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