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tree based regression methods for gap reconstruction of motion capture sequences

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 88卷

作者： Skurowski, Przemyslaw Pawlyta, Magdalena Silesian Tech Univ Fac Automat Control Elect & Comp Sci Dept Graph Comp Vis & Digital Syst Ul Akad 16 PL-44100 Gliwice Poland Polish Japanese Acad Informat Technol Koszykowa 86 PL-02008 Warsaw Poland

In the article, we study application of tree based regression methods for reconstruction of gaps in marker trajectories in optical motion capture systems. Having perfect ground truth sequences, we artificially introduced gaps of lengths between 0.1 and 2 s, then reconstructed them using various tree based regression methods - CART, LS boost, bagging, random forests, M5P (model tree). We also tested several methods known from the literature - neural networks (NN), interpolation, and matrix completion. We found out that in general, tree based methods perform poorly, but M5P model tree performs very well, especially for longer gaps, with results on average on par or better than neural networks. Finally, we compared the outcomes of M5P and NN, they appear to be uncorrelated, so they offer the possibility to create an ensemble predictor based on both methods.

关键词： Motion capture Reconstruction Gap filling Regression tree model tree

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Data-driven modelling framework for streamflow prediction in a physio-climatically heterogeneous river basin

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SOFT COMPUTING 2021年第8期25卷 5951-5978页

作者： Sharma, Priyank J. Patel, P. L. Jothiprakash, V. Sardar Vallabhbhai Natl Inst Technol SVNIT Dept Civil Engn Surat 395007 Gujarat India Florida Atlantic Univ Dept Civil Environm & Geomat Engn Boca Raton FL 33432 USA Indian Inst Technol Dept Civil Engn Mumbai 400076 Maharashtra India

The prediction of streamflows is essential for efficient water resources management at basin scale. The present study examines the performance of model tree (MT) data-driven technique in predicting streamflows for an intermittent and a perennial river in a physio-climatically heterogeneous river basin. The correlation and mutual information analyses of predictor (hydrometeorological) variables are performed to determine the model input structure. Overall, seventy-two model configurations are formulated for each stream gauging station based on the combination of input variables, MT variants and variable lengths of calibration and validation datasets. The model simulation results are analysed by estimating a suite of statistical performance indices for each model configuration. The influence of parameter sensitivity on model performance is also assessed. The results indicate that selection of input variables play a governing role in capturing the interplay of hydrological processes in a catchment. The model outputs displayed more sensitivity to pruning than smoothing in MT, and minimal sensitivity towards data portioning, since the datasets were homoscedastic. The study also proposes a procedure for model evaluation considering multiple criteria, such as forecasting error, efficiency, predictability and false alarms, and enabling multi-model comparisons for better decision making. The proposed procedure was successfully applied for selection of best-fit model to predict one-day ahead streamflows at each stream gauging station.

关键词： Base flow separation model evaluation model tree Sensitivity analysis Streamflow prediction Tapi basin

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Prediction of current-induced scour depth around pile groups using MARS, CART, and ANN approaches

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MARINE GEORESOURCES & GEOTECHNOLOGY 2021年第5期39卷 577-588页

作者： Samadi, Mehrshad Afshar, Mohammad Hadi Jabbari, Ebrahim Sarkardeh, Hamed IUST Sch Civil Engn POB 16765-163 Tehran Iran Hakim Sabzevari Univ Fac Engn Dept Civil Engn Sabzevar Iran

Scour around piles is considered as one of the most important phenomena which could undermine the stability of piles. Data-driven methods are now being used more and more to predict complex phenomena such as scour around of pile groups. In this study, three data-driven methods including multivariate adaptive regression spline (MARS), classification and regression tree (CART) and artificial neural network (ANN), are used for determining current-induced scour depth around pile groups in clear water conditions. Obtained results are compared with those of M5 model tree (M5MT) which shows the higher precision of them. The calculated statistical indices revealed the higher accuracy of MARS in prediction of scour depth (correlation coefficient [CC] = 0.872 and root mean square error [RMSE] = 0.233) in comparison with ANN (CC = 0.843 and RMSE = 0.256), CART (CC = 0.734 and RMSE = 0.323) and M5MT (CC = 0.761 and RMSE = 0.404) for testing data. Furthermore, the developed discrepancy ratio (DDR) index confirms the outperformance of MARS in comparison with other approaches for prediction of scour depth. Finally, the parametric investigations conducted through MARS show that the parameter of has the highest effect on the scour depth. This finding is in agreement with the results of CART.

关键词： Artificial neural network classification and regression tree multivariate adaptive regression splines model tree pile groups scour depth

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Comparative analysis of data driven rainfall-runoff models in the Kolar river basin

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RESULTS IN ENGINEERING 2024年 23卷

作者： Tiwari, Deepak Kumar Kumar, Vijendra Goyal, Anuj Khedher, Khaled Mohamed Salem, Mohamed Abdelaziz GLA Univ Dept Civil Engn Matura 281406 Uttar Pradesh India Dr Vishwanath Karad MIT World Peace Univ Dept Civil Engn Pune 411038 Maharashtra India King Khalid Univ Coll Engn Dept Civil Engn Abha 61421 Saudi Arabia King Khalid Univ Coll Engn Dept Mech Engn Abha 61421 Saudi Arabia

To effectively tackle the challenges posed by climate change, it is crucial to enhance the accuracy of rainfall- runoff models to ensure reliability amidst changing climatic conditions. Neural networks, renowned for their ability to capture complex patterns and relationships within uncertain input and output data, offer valuable tools in this pursuit. This study aims to evaluate the efficacy of two neural network (NN) models: the Radial Basis Function Neural Network (RBFNN) and the model tree M5 Neural Network (MTM5NN). These models are assessed both individually and in combination with the Wavelet (WT) data processing technique for rainfall- runoff modeling in the Kolar River watershed located in Madhya Pradesh, India. Fifteen models were developed employing four algorithms: RBFNN models, WRBFNN (RBF model integrating wavelet components of rainfall as inputs), MTM5NN, and WMTM5NN (MT model incorporating wavelet components of rainfall as inputs). Initially, rainfall and runoff data underwent normalization and were applied to RBFNN and MTM5NN networks. Subsequently, time series data for both rainfall and runoff were decomposed using wavelet transforms, resulting in various sub-time series signals such as approximations and decompositions. These derived signals were then utilized as input data for RBFNN and MTM5NN, specifically designated as WRBFNN and WMTM5NN. The most effective model identified was model 8 of WMTM5NN, which demonstrated R2 2 values close to 0.97, outperforming the other models. These results underscore the superior performance of the WMTM5NN model, highlighting its effectiveness in achieving heightened accuracy in predicting runoff for this specific watershed.

关键词： RBF Rainfall-runoff modeling ANN Wavelet decompositions model tree

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Frequency-based performance measure for hydrologic model evaluation

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JOURNAL OF HYDROLOGY 2022年 608卷 127583-127583页

作者： Teegavarapu, Ramesh S., V Sharma, Priyank J. Patel, Prem Lal Florida Atlantic Univ Dept Civil Environm & Geomat Engn Boca Raton FL 33431 USA Indian Inst Technol Indore Dept Civil Engn Indore 453552 Madhya Pradesh India Sardar Vallabhbhai Natl Inst Technol Dept Civil Engn Surat 395007 Gujarat India

Scale-dependent and -independent error and performance measures are used to evaluate hydrologic forecasting and simulation models. However, these single-valued measures may not always provide a comprehensive assessment of model performance developed for a specific hydrologic process and application. A new frequency-based performance measure (FBPM) that can incorporate application-specific information for model evaluation is proposed to address the limitations of existing measures. FBPM is derived using a data classification scheme to partition the observed data into several classes and evaluate frequencies of each class's chronologically paired observed and forecasted values. A variant of FBPM, composite performance measure (CPM), is also developed to include additional indices that evaluate error, variance, and other statistical characteristics of the observed and forecasted series. Several univariate and multivariate data classification schemes are initially evaluated, and the best one is selected for use in the measures. FBPM and CPM are used to assess the performance of daily streamflow forecasting models developed for the Tapi River, India, using a data-driven model tree (MT) approach. The measures are also evaluated using two synthetic datasets representing different model forecast scenarios. A comprehensive evaluation of streamflow forecasts using FBPM and CPM with the best data classification scheme (i.e., geometric interval scheme in this work) indicates a better and more robust assessment of the forecasting model performance than that from existing error and performance measures. The traditional measures such as coefficient of determination, index of agreement, Nash Sutcliffe, and Kling Gupta efficiency have overestimated model performances compared to FBPM and CPM on an average by at least 100%. The traditional measures have also failed to identify inferior models mainly due to their inflated numerical values compared to those from FBPM and CPM. The measu

关键词： Frequency-based performance measure Data classification Error and performance measures model evaluation Synthetic datasets model tree Composite performance measure

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A tree-based Clear Sky model for DNI Forecasting 30

A Tree-based Clear Sky Model for DNI Forecasting

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30th Chinese Control and Decision Conference (CCDC)

作者： Shen, Yu Wei, Haikun Zhao, Xin Zhu, Tingling Zhang, Kanjian Southeast Univ Sch Automat Minist Educ Key Lab Measurement & Control CSE Nanjing 210096 Peoples R China

ISBN: (纸本)9781538612446

Solar irradiance is a renewable energy that has been utilized by power plants. Direct normal irradiance (DNI) forecasting is a crucial approach to reduce the output uncertainty of solar power plants, and enhance reliability of power grids. According to statistics, the solar radiation of clear sky accounts for more than half of the total amount of radiation in a year. Hence, clear sky DNI forecasting models play a significant role in solar power systems. The existing clear sky models assume that the atmosphere is at rest and ignore the variation of aerosol optical depth in a day, which may reduce the accuracy of DNI forecasting, In this paper, a novel tree-based piecewise clear sky model is proposed to capture aerosol extinction coefficient varying with time in a day and forecast DNI when clouds are absent. Firstly, a linear model is deduced from a physical equation to represent the relationship of air mass and DNI approximately. Then, model trees with this linear mode at each leaf node are create. The database of the National Renewable Energy Laboratory (NREL) is used for experiments to evaluate the performance of the proposed model, compared with other clear sky models. The results of the experiments show that the proposed model provides more accurate forecast of DNI than other clear sky models.

关键词： Direct Normal Irradiance Clear Sky model model tree Piecewise Linear model

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New formulae for capacity energy-based assessment of liquefaction triggering

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MARINE GEORESOURCES & GEOTECHNOLOGY 2020年第2期38卷 214-222页

作者： Jafariavval, Yasaman Derakhshani, Ali Shahed Univ Fac Engn Dept Civil Engn Tehran Iran

Liquefaction of loose saturated soil deposits is a hazardous type of ground failure occurring under earthquake excitations. Therefore, an accurate estimation of liquefaction potential is extremely important in geotechnical engineering. In the current study, a new model is proposed which estimates the level of strain energy needed for liquefaction initiation. A compiled database containing cyclic tests gathered from previously published works was used to develop new models to predict liquefaction potential. M5 ' algorithm was used to find the best correlation between parameters. It was shown that not only the derived formulas are acceptably accurate but also they feature a very simple structure in comparison with available formulas in the literature. The proposed equations are accurate, physically sound and uncomplicated. Furthermore, safety factors were given for different levels of risk, which can be useful for engineering practice. In addition, the influence of different predictors on the liquefaction potential was evaluated and also the significance of input variables was assessed via sensitivity analysis. Finally, a new model was introduced for preliminary estimation of liquefaction potential.

关键词： Liquefaction potential soft computing strain energy probabilistic design model tree

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Accurate prediction of thermal conductivity of ethylene glycol-based hybrid nanofluids using artificial intelligence techniques

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2020年 116卷 104624-104624页

作者： Jamei, Mehdi Pourrajab, Rashid Ahmadianfar, Iman Noghrehabadi, Aminreza Shohadaye Hoveizeh Univ Technol Fac Engn Dasht E Azadegan Susangerd Iran Behbahan Khatam Alanbia Univ Technol Dept Civil Engn Behbahan Iran Shahid Chamran Univ Ahvaz Dept Mech Engn Ahvaz Iran

Accurate prediction of thermal conductivity of hybrid nanofluids is very important for industries such as microelectronics and cooling applications that heavily rely on the heat transfer. Many experimental investigations are conducted aiming at developing correlations to predict the relative thermal conductivity of hybrid nanofluids. However, the proposed correlations are limited to specific types of hybrid nanofluids. In this research, for the first time three soft computing techniques namely, Genetic programming (GP), model tree (MT) and Multi linear regression (MLR) models, were developed and used to accurately predict the thermal conductivity of various ethylene glycol (EG)-based hybrid nanofluids. A total of 275 datasets from literature were collected and divided into the testing and training groups. The results obtained from the proposed approaches were compared with a number of performance metrics and empirical correlations. The performance criteria indicated that the GP model for the test dataset (R = 0.950, RMSE = 0.0225) had the best prediction performance for the relative thermal conductivity of hybrid nanofluids in comparison to MT (R = 0.928, RMSE =0.0301) and MLR (R = 0.787, RMSE =0.050), respectively. Sensitivity analysis showed that the nanoparticle volume fraction (R = 0.445, SI = 0.0667) was the most influential factor among all model input parameters.

关键词： Hybrid nanofluid Thermal conductivity Volume fraction Genetic programming model tree

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Cracking the Black Box: Distilling Deep Sports Analytics 20

Cracking the Black Box: Distilling Deep Sports Analytics

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26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Sun, Xiangyu Davis, Jack Schulte, Oliver Liu, Guiliang Simon Fraser Univ 8888 Univ Dr Burnaby BC Canada

ISBN: (纸本)9781450379984

This paper addresses the trade-off between Accuracy and Transparency for deep learning applied to sports analytics. Neural nets achieve great predictive accuracy through deep learning, and are popular in sports analytics [6, 11, 17, 25]. But it is hard to interpret a neural net model and harder still to extract actionable insights from the knowledge implicit in it. Therefore, we built a simple and transparent model that mimics the output of the original deep learning model and represents the learned knowledge in an explicit interpretable way. Our mimic model is a linear model tree, which combines a collection of linear models with a regression-tree structure. The tree version of a neural network achieves high fidelity, explains itself, and produces insights for expert stakeholders such as athletes and coaches. We propose and compare several scalable model tree learning heuristics to address the computational challenge from datasets with millions of data points.

关键词： interpretability neural networks model tree mimic learning sports analytics

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Some structural patterns of the stand of Scotch pine (Pinussylvestris L.) in the conditions of Zavolzhsky - ObshchySyrt province

Some structural patterns of the stand of Scotch pine (Pinuss...

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Universitas-Riau International Conference on Science and Environment (URICSE)

作者： Rjabuchina, M., V Ryabinina, Z. N. Kalyakina, R. G. Gerasimenko, V. V. Maiski, R. A. Russian Acad Sci Fed Sci Ctr Biol Syst & Agr Technol 9th January St29 Orenburg 460014 Russia FSBEI HE Orenburg State Agr Univ Chelyuskintsevst 18 Orenburg 460014 Russia FSBEI HE Ufa State Petr Tech Univ Kosmonavtov Str 1 Ufa 450062 Russia

Under the structure of stands is understood the order of combination and the nature of the relationship of the trees that make them up according to the main taxation characteristics, both in statics and *** the stand is an unified system where the growth and development of trees are closely interconnected, the study of the structure should be carried out for the entire set of trees without separating any of its constituent parts, as indicated by N.V. Tretyakov.A.A. Makarenko proposed a methodology for studying the structure of stands, based on the construction of distribution curves for 10 classes within the range of variation of taxation *** the present work, it was found that the distribution of actual values within 9 or 11 steps does not significantly affect the statistics of the distribution series calculated on the same trial plots for 10 classes, and with an increase or decrease in the classes, the difference between these statistics will increase accordingly.

关键词： stand structure growth course Scotch pine model tree pure culture

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