Assessment for Thermal Conductivity of Frozen Soil Based on Nonlinear Regression and Support Vector Regression Methods

The comprehensive understanding of the variation law of soil thermal conductivity is the prerequisite of design and construction of engineering applications in permafrost regions. Compared with the unfrozen soil, the specimen preparation and experimental procedures of frozen soil thermal conductivity testing are more complex and challengeable. In this work, considering for essentially multiphase and porous structural characteristic information reflection of unfrozen soil thermal conductivity, prediction models of frozen soil thermal conductivity using nonlinear regression and Support Vector Regression (SVR) methods have been developed. Thermal conductivity of multiple types of soil samples which are sampled from the Qinghai-Tibet Engineering Corridor (QTEC) are tested by the transient plane source (TPS) method. Correlations of thermal conductivity between unfrozen and frozen soil has been analyzed and recognized. Based on the measurement data of unfrozen soil thermal conductivity, the prediction models of frozen soil thermal conductivity for 7 typical soils in the QTEC are proposed. To further facilitate engineering applications, the prediction models of two soil categories (coarse and fine-grained soil) have also been proposed. The results demonstrate that, compared with nonideal prediction accuracy of using water content and dry density as the fitting parameter, the ternary fitting model has a higher thermal conductivity prediction accuracy for 7 types of frozen soils (more than 98% of the soil specimens’ relative error are within 20%). The SVR model can further improve the frozen soil thermal conductivity prediction accuracy and more than 98% of the soil specimens’ relative error are within 15%. For coarse and fine-grained soil categories, the above two models still have reliable prediction accuracy and determine coefficient (R2) ranges from 0.8 to 0.91, which validates the applicability for small sample soils. This study provides feasible prediction models for frozen soil thermal conductivity and guidelines of the thermal design and freeze-thaw damage prevention for engineering structures in cold regions.

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Parsimonious statistical learning models for low-flow estimation

Hydrology and Earth System Sciences ◽

10.5194/hess-26-129-2022 ◽

2022 ◽

Vol 26 (1) ◽

pp. 129-148

Author(s):

Johannes Laimighofer ◽

Michael Melcher ◽

Gregor Laaha

Keyword(s):

Support Vector Regression ◽

Statistical Learning ◽

Nonlinear Model ◽

Prediction Accuracy ◽

Prediction Models ◽

Low Flow ◽

Support Vector ◽

Learning Models ◽

Model Based ◽

Linear And Nonlinear

Abstract. Statistical learning methods offer a promising approach for low-flow regionalization. We examine seven statistical learning models (Lasso, linear, and nonlinear-model-based boosting, sparse partial least squares, principal component regression, random forest, and support vector regression) for the prediction of winter and summer low flow based on a hydrologically diverse dataset of 260 catchments in Austria. In order to produce sparse models, we adapt the recursive feature elimination for variable preselection and propose using three different variable ranking methods (conditional forest, Lasso, and linear model-based boosting) for each of the prediction models. Results are evaluated for the low-flow characteristic Q95 (Pr(Q>Q95)=0.95) standardized by catchment area using a repeated nested cross-validation scheme. We found a generally high prediction accuracy for winter (RCV2 of 0.66 to 0.7) and summer (RCV2 of 0.83 to 0.86). The models perform similarly to or slightly better than a top-kriging model that constitutes the current benchmark for the study area. The best-performing models are support vector regression (winter) and nonlinear model-based boosting (summer), but linear models exhibit similar prediction accuracy. The use of variable preselection can significantly reduce the complexity of all the models with only a small loss of performance. The so-obtained learning models are more parsimonious and thus easier to interpret and more robust when predicting at ungauged sites. A direct comparison of linear and nonlinear models reveals that nonlinear processes can be sufficiently captured by linear learning models, so there is no need to use more complex models or to add nonlinear effects. When performing low-flow regionalization in a seasonal climate, the temporal stratification into summer and winter low flows was shown to increase the predictive performance of all learning models, offering an alternative to catchment grouping that is recommended otherwise.

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Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽

10.3390/pr9071166 ◽

2021 ◽

Vol 9 (7) ◽

pp. 1166

Author(s):

Bashir Musa ◽

Nasser Yimen ◽

Sani Isah Abba ◽

Humphrey Hugh Adun ◽

Mustafa Dagbasi

Keyword(s):

Support Vector Regression ◽

Mean Square Error ◽

Prediction Accuracy ◽

Demand Forecasting ◽

Load Forecasting ◽

Coefficient Of Determination ◽

Percentage Error ◽

Support Vector ◽

Mean Square ◽

Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

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Predicting Corporate Financial Sustainability Using Novel Business Analytics

Sustainability ◽

10.3390/su11010064 ◽

2018 ◽

Vol 11 (1) ◽

pp. 64 ◽

Cited By ~ 5

Author(s):

Kyoung-jae Kim ◽

Kichun Lee ◽

Hyunchul Ahn

Keyword(s):

Financial Distress ◽

Prediction Accuracy ◽

Prediction Models ◽

Support Vector ◽

Model Parameters ◽

Financial Sustainability ◽

Business Analytics ◽

Financial Distress Prediction ◽

Proposed Model ◽

Distress Prediction

Measuring and managing the financial sustainability of the borrowers is crucial to financial institutions for their risk management. As a result, building an effective corporate financial distress prediction model has been an important research topic for a long time. Recently, researchers are exerting themselves to improve the accuracy of financial distress prediction models by applying various business analytics approaches including statistical and artificial intelligence methods. Among them, support vector machines (SVMs) are becoming popular. SVMs require only small training samples and have little possibility of overfitting if model parameters are properly tuned. Nonetheless, SVMs generally show high prediction accuracy since it can deal with complex nonlinear patterns. Despite of these advantages, SVMs are often criticized because their architectural factors are determined by heuristics, such as the parameters of a kernel function and the subsets of appropriate features and instances. In this study, we propose globally optimized SVMs, denoted by GOSVM, a novel hybrid SVM model designed to optimize feature selection, instance selection, and kernel parameters altogether. This study introduces genetic algorithm (GA) in order to simultaneously optimize multiple heterogeneous design factors of SVMs. Our study applies the proposed model to the real-world case for predicting financial distress. Experiments show that the proposed model significantly improves the prediction accuracy of conventional SVMs.

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A max-margin training of RNA secondary structure prediction integrated with the thermodynamic model

10.1101/205047 ◽

2017 ◽

Cited By ~ 1

Author(s):

Manato Akiyama ◽

Kengo Sato ◽

Yasubumi Sakakibara

Keyword(s):

Machine Learning ◽

Secondary Structure ◽

Structure Prediction ◽

Rna Secondary Structure ◽

Prediction Accuracy ◽

Secondary Structure Prediction ◽

Training Data ◽

Support Vector ◽

Rna Secondary Structure Prediction ◽

Fine Grained

AbstractMotivation: A popular approach for predicting RNA secondary structure is the thermodynamic nearest neighbor model that finds a thermodynamically most stable secondary structure with the minimum free energy (MFE). For further improvement, an alternative approach that is based on machine learning techniques has been developed. The machine learning based approach can employ a fine-grained model that includes much richer feature representations with the ability to fit the training data. Although a machine learning based fine-grained model achieved extremely high performance in prediction accuracy, a possibility of the risk of overfitting for such model has been reported.Results: In this paper, we propose a novel algorithm for RNA secondary structure prediction that integrates the thermodynamic approach and the machine learning based weighted approach. Ourfine-grained model combines the experimentally determined thermodynamic parameters with a large number of scoring parameters for detailed contexts of features that are trained by the structured support vector machine (SSVM) with the ℓ1 regularization to avoid overfitting. Our benchmark shows that our algorithm achieves the best prediction accuracy compared with existing methods, and heavy overfitting cannot be observed.Availability: The implementation of our algorithm is available at https://github.com/keio-bioinformatics/mxfold.Contact:[email protected]

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Prediction of Commuter’s Daily Time Allocation

PROMET - Traffic&Transportation ◽

10.7307/ptt.v25i5.1190 ◽

2013 ◽

Vol 25 (5) ◽

pp. 445-455 ◽

Cited By ~ 7

Author(s):

Fang Zong ◽

Jia Hongfei ◽

Pan Xiang ◽

Wu Yang

Keyword(s):

Support Vector Regression ◽

Travel Time ◽

Time Allocation ◽

Prediction Accuracy ◽

Travel Demand ◽

Model System ◽

Support Vector ◽

Activity Duration ◽

Wide Range ◽

Duration Prediction

This paper presents a model system to predict the time allocation in commuters’ daily activity-travel pattern. The departure time and the arrival time are estimated with Ordered Probit model and Support Vector Regression is introduced for travel time and activity duration prediction. Applied in a real-world time allocation prediction experiment, the model system shows a satisfactory level of prediction accuracy. This study provides useful insights into commuters’ activity-travel time allocation decision by identifying the important influences, and the results are readily applied to a wide range of transportation practice, such as travel information system, by providing reliable forecast for variations in travel demand over time. By introducing the Support Vector Regression, it also makes a methodological contribution in enhancing prediction accuracy of travel time and activity duration prediction.

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A New Method for Predicting the Permeability of Sandstone in Deep Reservoirs

Geofluids ◽

10.1155/2020/8844464 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16

Author(s):

Feisheng Feng ◽

Pan Wang ◽

Zhen Wei ◽

Guanghui Jiang ◽

Dongjing Xu ◽

...

Keyword(s):

Support Vector Regression ◽

Capillary Pressure ◽

Prediction Models ◽

Reservoir Rock ◽

Support Vector ◽

Pressure Curve ◽

Pore Throat ◽

Capillary Pressure Curve ◽

Mercury Injection ◽

Mercury Injection Capillary Pressure

Capillary pressure curve data measured through the mercury injection method can accurately reflect the pore throat characteristics of reservoir rock; in this study, a new methodology is proposed to solve the aforementioned problem by virtue of the support vector regression tool and two improved models according to Swanson and capillary parachor parameters. Based on previous research data on the mercury injection capillary pressure (MICP) for two groups of core plugs excised, several permeability prediction models, including Swanson, improved Swanson, capillary parachor, improved capillary parachor, and support vector regression (SVR) models, are established to estimate the permeability. The results show that the SVR models are applicable in both high and relatively low porosity-permeability sandstone reservoirs; it can provide a higher degree of precision, and it is recognized as a helpful tool aimed at estimating the permeability in sandstone formations, particularly in situations where it is crucial to obtain a precise estimation value.

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Spatiotemporal Traffic Flow Prediction with KNN and LSTM

Journal of Advanced Transportation ◽

10.1155/2019/4145353 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10 ◽

Cited By ~ 37

Author(s):

Xianglong Luo ◽

Danyang Li ◽

Yu Yang ◽

Shengrui Zhang

Keyword(s):

Support Vector Regression ◽

Traffic Flow ◽

Prediction Models ◽

Short Term Memory ◽

Transportation Systems ◽

Support Vector ◽

K Nearest Neighbor ◽

Traffic Flow Prediction ◽

Flow Prediction ◽

Proposed Model

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

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Regression Cloud Models and Their Applications in Energy Consumption of Data Center

Journal of Electrical and Computer Engineering ◽

10.1155/2015/143071 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Yanshuang Zhou ◽

Na Li ◽

Hong Li ◽

Yongqiang Zhang

Keyword(s):

Linear Regression ◽

Regression Model ◽

Support Vector Regression ◽

Nonlinear Regression ◽

Regression Models ◽

Energy Model ◽

Support Vector ◽

Support Vector Regression Model ◽

Performance Counters ◽

System Utilization

As cloud data center consumes more and more energy, both researchers and engineers aim to minimize energy consumption while keeping its services available. A good energy model can reflect the relationships between running tasks and the energy consumed by hardware and can be further used to schedule tasks for saving energy. In this paper, we analyzed linear and nonlinear regression energy model based on performance counters and system utilization and proposed a support vector regression energy model. For performance counters, we gave a general linear regression framework and compared three linear regression models. For system utilization, we compared our support vector regression model with linear regression and three nonlinear regression models. The experiments show that linear regression model is good enough to model performance counters, nonlinear regression is better than linear regression model for modeling system utilization, and support vector regression model is better than polynomial and exponential regression models.

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Colorimetric detection on paper analytical device using machine learning

10.33774/chemrxiv-2021-0zbwm ◽

2021 ◽

Author(s):

Bidur Khanal ◽

Pravin Pokhrel ◽

Bishesh Khanal ◽

Basant Giri

Keyword(s):

Machine Learning ◽

Enzyme Inhibition ◽

Prediction Accuracy ◽

Low Cost ◽

Colorimetric Detection ◽

Field Testing ◽

Support Vector ◽

Ann Model ◽

Fine Grained ◽

Imaging Conditions

Paper-based analytical devices (PADs) employing colorimetric detection and smartphone images have gained wider acceptance in a variety of measurement applications. The PADs are primarily meant to be used in field settings where assay and imaging conditions greatly vary resulting in less accurate results. Recently, machine learning (ML) assisted models have been used in image analysis. We evaluated a combinations of four ML models - logistic regression, support vector machine, random forest, and artificial neural network, and three image color spaces - RGB, HSV, and LAB for their ability to accurately predict analyte concentrations. We used images of PADs taken at varying lighting conditions, with different cameras, and users for food color and enzyme inhibition assays to create training and test datasets. Prediction accuracy was higher for food color than enzyme inhibition assays in most of the ML model and colorspace combinations. All models better predicted coarse level classification than fine grained concentration labels. ML models using sample color along with a reference color increased the models’ ability in predicting the result in which the reference color may have partially factored out the variation in ambient assay and imaging conditions. The best concentration label prediction accuracy obtained for food color was 0.966 when using ANN model and LAB colorspace. The accuracy for enzyme inhibition assay was 0.908 when using SVM model and LAB colorspace. Appropriate model and colorspace combinations can be useful to analyze large numbers of samples on PADs as a powerful low-cost quick field-testing tool.

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Application of support vector machine for the prediction of strip crown in hot strip rolling

10.21203/rs.3.rs-31633/v1 ◽

2020 ◽

Author(s):

Ya-feng Ji ◽

Le-Bao Song ◽

Hao Yuan ◽

Wen Peng ◽

Hua-Ying Li ◽

...

Keyword(s):

Support Vector Machine ◽

Prediction Accuracy ◽

Prediction Models ◽

Mean Squared Error ◽

Prediction Performance ◽

Support Vector ◽

Hot Strip Rolling ◽

Strip Rolling ◽

Hot Strip ◽

Strip Crown

Abstract In order to enhance the prediction accuracy of the strip crown and improve the quality of final product in the hot strip rolling, an optimized model based upon support vector machine (SVM) is proposed firstly. Meanwhile, for purposes of enriching data information and ensuring data quality, the actual data from a hot-rolled plant are collected to establish prediction model, as well as the prediction performance of models was evaluated by using multiple indicators. Besides, the traditional SVM model and the combined prediction models with the particle swarm optimization (PSO) and the cuckoo search (CS) optimization algorithm are also proposed. Furthermore, the prediction performance comparisons of the three different methods are discussed and validated. The results show that the CS-SVM has the highest prediction accuracy compared to the other two methods, and the root mean squared error (RMSE) of the proposed CS-SVM is 2.05µm, and 98.11% of prediction data have an absolute error below 4.5μm. In addition, the results also demonstrated that the CS-SVM not only with faster convergence speed and higher prediction accuracy but can be well applied to the actual hot strip rolling production.

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