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Seasonal reservoir inflow forecasting with low-frequency climatic indices: a comparison of data-driven methods

HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES 2007年第3期52卷 508-522页

作者： Muluye, Getnet V. Coulibaly, Paulin McMaster Univ Dept Civil Engn Hamilton ON L8S 4L7 Canada McMaster Univ Dept Civil Engn Hamilton ON L8S 4L7 Canada McMaster Univ Sch Geog & Earth Sci Hamilton ON L8S 4L7 Canada

This paper investigates the potential of using data-driven methods, namely Bayesian neural networks (BNN), recurrent multi-layer perceptrons (RMLP), time-lagged feed-forward networks (TLFN), and conventional multi-layer perceptrons (MLP) to forecast seasonal reservoir inflows of the Churchill Falls watershed in northeastern Canada. A climate variability indicator (the El Nino-Southern Oscillation, ENSO) is used as additional information to historical inflow time series in order to predict seasonal reservoir inflows. The prediction results showed that the Bayesian neural network model was best able to capture the additional information provided by the ENSO series, and provided improved predictions in spring and summer seasons relative to the same model using only reservoir inflows. Similarly, time-lagged feed-forward networks and recurrent multi-layer perceptrons showed some improved forecast skill in spring when the ENSO index series are used but generally provided superior performance overall. The conventional multi-layer perceptron appears unable to capture relevant information from the ENSO series regardless of the season. However, when only historical flow series are used, all the selected data-driven methods provide very competitive forecast performances. Key words data-driven methods;Bayesian neural networks;recurrent networks;time-lagged networks;Churchill Falls basin;ENSO;seasonal forecast;reservoir inflows

关键词： data-driven methods Bayesian neural networks recurrent networks time-lagged networks Churchill Falls basin ENSO seasonal forecast reservoir inflows

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Mapping landslide susceptibility using data-driven methods

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SCIENCE OF THE TOTAL ENVIRONMENT 2017年 589卷 250-267页

作者： Zezere, J. L. Pereira, S. Melo, R. Oliveira, S. C. Garcia, R. A. C. Univ Lisbon Inst Geog & Spatial Planning Lisbon Portugal

Most epistemic uncertainty within data-driven landslide susceptibility assessment results from errors in landslide inventories, difficulty in identifying and mapping landslide causes and decisions related with the modelling procedure. In this work we evaluate and discuss differences observed on landslide susceptibility maps resulting from: (i) the selection of the statistical method;(ii) the selection of the terrain mapping unit;and (iii) the selection of the feature type to represent landslides in the model (polygon versus point). The work is performed in a single study area (Silveira Basin-18.2 km(2) - Lisbon Region, Portugal) using a unique database of geo-environmental landslide predisposing factors and an inventory of 82 shallow translational slides. The logistic regression, the discriminant analysis and two versions of the information value were used and we conclude that multivariate statistical methods perform better when computed over heterogeneous terrain units and should be selected to assess landslide susceptibility based on slope terrain units, geo-hydrological terrain units or census terrain units. However, evidence was found that the chosen terrain mapping unit can produce greater differences on final susceptibility results than those resulting from the chosen statistical method for modelling. The landslide susceptibility should be assessed over grid cell terrain units whenever the spatial accuracy of landslide inventory is good. In addition, a single point per landslide proved to be efficient to generate accurate landslide susceptibility maps, providing the landslides are of small size, thus minimizing the possible existence of heterogeneities of predisposing factors within the landslide boundary. Although during last years the ROC curves have been preferred to evaluate the susceptibility model's performance, evidence was found that the model with the highest AUC ROC is not necessarily the best landslide susceptibility model, namely when terrain mappi

关键词： Landslide susceptibility data-driven methods Terrain mapping units Validation Uncertainty

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Syllabification rules versus data-driven methods in a language with low syllabic complexity: The case of Italian

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COMPUTER SPEECH AND LANGUAGE 2009年第4期23卷 444-463页

作者： Adsett, Connie R. Marchand, Yannick Keselj, Vlado Natl Res Council Canada Inst Biodiagnost Atlantic Halifax NS B3H 3A7 Canada Dalhousie Univ Fac Comp Sci Halifax NS B3H 1W5 Canada

Linguistic rules have been assumed to be the best technique for determining the syllabification of unknown words. This has recently been challenged for the English language where data-driven algorithms have been shown to outperform rule-based methods. It may be possible, however, that data-driven methods are only better for languages with complex syllable structures. In this study, three rule-based automatic syllabification systems and two data-driven automatic syllabification systems (Syllabification by Analogy and the Look-Up Procedure) are compared on a language with lower syllabic complexity - Italian. Comparing the performance using a lexicon containing 44,720 words, the best data-driven algorithm (Syllabification by Analogy) achieved 97.70% word accuracy while the best rule set correctly syllabified 89.77% words. These results show that data-driven methods can also outperform rule-based methods on Italian syllabification, a language of low syllabic complexity. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Syllabification Italian language Rule-based systems data-driven methods Analogy

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A Review of data-driven methods in Building Retrofit and Performance Optimization: From the Perspective of Carbon Emission Reductions

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ENERGIES 2024年第18期17卷 4641页

作者： Luo, Shu-Long Shi, Xing Yang, Feng Tongji Univ Coll Architecture & Urban Planning Shanghai 200092 Peoples R China Minist Educ Key Lab Ecol & Energy Saving Study Dense Habitat Shanghai 200092 Peoples R China

In order to reduce the contribution of the building sector to global greenhouse gas emissions and climate change, it is important to improve the building performance through retrofits from the perspective of carbon emission reductions. data-driven methods are now widely used in building retrofit research. To better apply data-driven techniques in low-carbon building retrofits, a better understanding is needed of the connections and interactions in optimization objectives and parameters, as well as optimization methods and tools. This paper provides a bibliometric analysis of selected 45 studies, summarizes current research hotspots in the field, discusses gaps to be filled, and proposes potential directions for future work. The results show that (1) the building-performance optimization (BPO) process established through physical simulation methods combines the site, retrofit variables, and carbon-related objectives, and the generated datasets are either directly processed using multi-objective optimization (MOO) algorithms or trained as a surrogate model and iteratively optimized using MOO methods. When a sufficient amount of data is available, data-driven methods can be used to develop mathematical models and use MOO methods for performance optimization from the perspective of building carbon emission reductions. (2) The benefits of retrofits are maximized by holistically taking environmental, economic, and social factors into account;from the perspectives of carbon emissions, costs, thermal comfort, and more, widely adopted strategies include improving the thermal performance of building envelopes, regulating HVAC systems, and utilizing renewable energy. (3) The optimization process based on data-driven methods, such as optimization algorithms and machine learning, apply mathematical models and methods for automatic iterative calculations and screen out the optimal solutions with computer assistance with high efficiency while ensuring accuracy. (4) Only 2.2% and 6

关键词： carbon emissions reduction building retrofit building-performance optimization data-driven methods bibliometric analysis

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Fault detection of wind turbine system based on data-driven methods: a comparative study

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NEURAL COMPUTING & APPLICATIONS 2024年第17期36卷 10279-10296页

作者： Elshenawy, Lamiaa M. Gafar, Ahmed A. Awad, Hamdi A. Abouomar, Mahmoud S. Menoufia Univ Fac Elect Engn Ind Elect & Control Engn Menoufia 32952 Egypt AlRyada Univ Sci & Technol Fac Comp & Artificial Intelligence Sadat City 32897 Egypt

Fault detection plays a crucial role in ensuring the safety, availability, and reliability of modern industrial processes. This study focuses on data-driven fault detection methods, which have gained significant attention across various industrial sectors due to the rapid development of industrial automation technologies and the availability of extensive datasets. The objectives of this paper are to comprehensively review and present the theoretical foundations of widely used data-driven fault detection approaches. Specifically, these approaches are applied to fault detection in wind turbine systems, with performance evaluation conducted using multiple statistical measures. The data utilized in this study were collected from a simulated benchmark of a wind turbine system. The data-driven methods are tested under the assumption that the wind turbine operates in a steady-state region. Additionally, a comparative study is conducted to identify and discuss the primary challenges associated with the practical application of these methods in real-world scenarios. Simulation results show the effectiveness and efficacy of data-driven approaches concerning the sensitivity and robustness of wind turbine sensor faults as applied in practical industrial environments.

关键词： Fault detection Statistical process monitoring data-driven methods Wind turbine system.

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Stability Analysis for a Class of Systems: From Model-Based methods to data-driven methods

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2014年第11期61卷 6463-6471页

作者： Wang, Zhuo Liu, Derong Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2V4 Canada Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA

This paper presents some new methods to analyze the stability of the equilibrium point, and the bounded-input/ boundedstate (BIBS) and the bounded-input/ bounded-output (BIBO) stabilities for linear systems and a class of nonlinear systems. We study these systems in both continuous-time and discrete-time forms, using approaches from model-based methods to datadriven methods. For linear systems, our work about the BIBS and the BIBO stabilities does not concern the state controllability or the state observability. The data-driven methods can analyze the stabilities by doing some simple experiments and observations, while not identifying the corresponding parameter matrices. Our methods are easier

关键词： Bounded-input/bounded-state (BIBS) and bounded-input/bounded-output BIBO) stabilities continuoustime and discrete-time systems data-driven methods model-based methods stability of the equilibrium point

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The Application of data-driven methods and Physics-Based Learning for Improving Battery Safety

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JOULE 2021年第2期5卷 316-329页

作者： Finegan, Donal P. Zhu, Juner Feng, Xuning Keyser, Matt Ulmefors, Marcus Li, Wei Bazant, Martin Z. Cooper, Samuel J. Natl Renewable Energy Lab Golden CO 80401 USA MIT Dept Mech Engn 77 Massachusetts Ave Cambridge MA 02139 USA MIT Dept Chem Engn 77 Massachusetts Ave Cambridge MA 02139 USA Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Northvolt AB S-11120 Stockholm Sweden Imperial Coll London Dyson Sch Design Engn London England MIT Dept Math 77 Massachusetts Ave Cambridge MA 02139 USA

Enabling accurate prediction of battery failure will lead to safer battery systems, as well as accelerating cell design and manufacturing processes for increased consistency and reliability. data-driven prediction methods have shown promise for accurately predicting cell behaviors with low computational cost, but they are expensive to train. Furthermore, given that the risk of battery failure is already very low, gathering enough relevant data to facilitate data-driven predictions is extremely challenging. Here, a perspective for designing experiments to facilitate a relatively low number of tests, handling the data, applying data-driven methods, and improving our understanding of behavior-dictating physics is outlined. This perspective starts with effective strategies for experimentally replicating rare failure scenarios and thus reducing the number of experiments, and proceeds to describe means to acquire high-quality datasets, apply data-driven prediction techniques, and to extract physical insights into the events that lead to failure by incorporating physics into data-driven approaches.

关键词： lithium-ion batteries data-driven methods machine learning modeling diagnostics safety battery failure

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Modeling debris flow initiation and run-out in recently burned areas using data-driven methods

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NATURAL HAZARDS 2017年第3期88卷 1373-1407页

作者： Melo, Raquel Zezere, Jose Luis Univ Lisbon Inst Geog & Spatial Planning Ctr Geog Studies Edificio IGOTRua Branca Edmec Marques P-1600276 Lisbon Portugal

In the framework of the landslide susceptibility assessment, the maps produced should include not only the landslide initiation areas, but also those areas potentially affected by the traveling mobilized material. To achieve this purpose, the susceptibility analysis must be separated in two distinct components: (1) The first one, which is also the most discussed in the literature, deals with the susceptibility to failure, and (2) the second component refers to the run-out modeling using the initiation areas as an input. Therefore, in this research we present a debris flow susceptibility assessment in a recently burned area in a mountain zone in central Portugal. The modeling of debris flow initiation areas is performed using two statistical methods: a bivariate (information value) and a multivariate (logistic regression). The independent validation of the results generated areas under the receiver operating characteristic curves between 0.91 and 0.98. The slope angle, plan curvature, soil thickness and lithology proved to be the most relevant predisposing factors for the debris flow initiation in recently burned areas. The run-out is simulated by applying two different methods: the empirical model Flow Path Assessment of Gravitational Hazards at a Regional Scale (Flow-R) and the hydrological algorithm D-infinity downslope influence (DI). The run-out modeling of the 36 initiation areas included in the debris flow inventory delivered a true positive rate of 83.5% for Flow-R and 80.5% for DI, reflecting a good performance of both models. Finally, the susceptibility map for the entire basin including both the initiation and the run-out areas in a scenario of a recent wildfire was produced by combining the four models mentioned above.

关键词： Debris flow Initiation areas Run-out data-driven methods Burned areas

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A comparative study of two data-driven methods in detection of vuggy zones: a case study from a carbonate reservoir, west of Iran

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CARBONATES AND EVAPORITES 2015年第2期30卷 181-185页

作者： Mollajan, Amir Asgarinezhad, Yousef Tokhmechi, Behzad Sherkati, Shahram Univ Tehran Coll Engn Sch Min Engn Tehran Iran Univ Shahrood Sch Min Petr & Geophys Shahrood Iran Natl Iranian Oil Co Explorat Directorate Tehran Iran

Vuggy porosity is a key pore space type associated with the characterization of any carbonate reservoir. This porosity has significant influence on many parameters in reservoirs such as permeability, oil recovery, pressure drop and production. In this paper, two data-driven methods (i.e., Bayesian and Parzen classifiers) are used to identify vuggy porosity zones from well logs. Four well logs namely gamma ray (GR), neutron porosity (NPHI), bulk density (RHOB) and sonic (DT) from three oil wells drilled in Sarvak Formation, west of Iran, were integrated to examine the capability of the methods. The results show that the Parzen classifier gives better results than Bayesian in terms of classification accuracy. The Parzen technique classifies the given depth with an average accuracy of 77.7 % in single-well analysis and average accuracy of 67.3 % in generalization step.

关键词： data-driven methods Bayesian classifier Parzen classifier Sarvak Formation Iran

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PREDICTION OF FLOOD IN KARKHEH BASIN USING data-driven methods 6

PREDICTION OF FLOOD IN KARKHEH BASIN USING DATA-DRIVEN METHO...

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ISPRS GeoSpatial Conference / Joint 6th Sensors and Models in Photogrammetry and Remote Sensing Conference (SMPR) / 4th Geospatial Information Research (GIResearch) Conference

作者： Kamali, S. Saedi, F. Asghari, K. Isfahan Univ Technol Dept Civil Engn Esfahan Iran

Flood causes several threats with outcomes which include peril to human and animal life, damage to property, and adversity to agricultural fields. Hence, flood prediction is highly significant for the mitigating municipal and environmental damage. The aim of this study was assessing the performance of different machine learning methods in predicting flood in Karkheh basin. To aim this, we used Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM), Feed Forward Back Propagation Neural Network (FFBPNN), and Radial Basis Function Neural Network (RBFNN) to simulate monthly streamflow in the study area. Furthermore, the performance of models was compared in predicting flood. All four models indicated good performance in simulating streamflow. However, LSSVM model had the highest accuracy compared with other models with R-2 and RMSE of 85.89% and 30.02 m(3)/s during testing periods, respectively. Similarly, LSSVM model performed better in predicting annual maximum streamflow in comparison with other machine learning models.

关键词： Flood data-driven methods Annual Maximum Streamflow Precipitation Modelling Karkheh Basin

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