scholarly journals Pile-Raft Settlements Prediction under Coupled Static-Dynamic Loads Using Four Heuristic Regression Approaches

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Mosbeh R. Kaloop ◽  
Jong Wan Hu ◽  
Emad Elbeltagi
Keyword(s):  
Prediction Models ◽  
Field Tests ◽  
Multivariate Adaptive Regression Splines ◽  
Dynamic Loads ◽  
Support Vector ◽  
M5 Model Tree ◽  
Regression Methods ◽  
Generalized Regression Neural Networks ◽  
Testing Stage ◽  
Heavy Loads

One of the main driving factors for structures’ evaluation is the foundation settlement. Measuring structures’ settlement in field is costly especially when heavy loads are applied. Settlement prediction models can be used to avoid the high cost of settlement field tests. Four advanced heuristic regression methods are developed and applied in this study to estimate raft foundations’ settlement, namely, multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), generalized regression neural networks (GRNN), and support vector regression (SVR) techniques. Simulation of raft pile foundations is utilized to calculate the settlements of piles under the effect of static and dynamic loads. Previous studies are compared with the newly developed models. The results show that the four models can be used to accurately predict foundations’ settlements in the training stage. Also, the results reveal that the MARS and SVR models performed slightly better than the M5Tree and GRNN models in the testing stage and accordingly can be used to predict foundations’ settlement. The SVR model outperformed other models when few numbers of measurements are available.

scholarly journals A Comparative Study of VMD-Based Hybrid Forecasting Model for Nonstationary Daily Streamflow Time Series

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Hui Hu ◽  
Jianfeng Zhang ◽  
Tao Li
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Data Driven ◽  
Support Vector ◽  
Efficiency Coefficient ◽  
Streamflow Forecasting ◽  
Daily Streamflow ◽  
Mode Decomposition ◽  
Testing Stage ◽  
Bpnn Model

Data-driven methods are very useful for streamflow forecasting when the underlying physical relationships are not entirely clear. However, obtaining an accurate data-driven model that is sufficiently performant for streamflow forecasting remains often challenging. This study proposes a new data-driven model that combined the variational mode decomposition (VMD) and the prediction models for daily streamflow forecasting. The prediction models include the autoregressive moving average (ARMA), the gradient boosting regression tree (GBRT), the support vector regression (SVR), and the backpropagation neural network (BPNN). The latest decomposition model, the VMD algorithm, was first applied to extract the multiscale features from the entire time series and to decompose them into several subseries, which were predicted after that using forecast models. The ensemble forecast was finally reconstructed by summing. Historical daily streamflow series recorded at the Wushan and Weijiabao hydrologic stations from 1 January 2001 to 31 December 2014 in China were investigated using the proposed VMD-based models. Three quantitative evaluation indexes, including the Nash–Sutcliffe efficiency coefficient (NSE), the root mean square error (RMSE), and the mean absolute error (MAE), were used to evaluate and compare the predicted results of the proposed VMD-based models with two other models such as nondecomposition method (BPNN) and BPNN based on ensemble empirical mode decomposition (EEMD-BPNN). Furthermore, a comparative analysis of the performance of the VMD-BPNN model under different forecast periods (1, 3, 5, and 7 days) was performed. The results evidenced that the proposed VMD-based models could always achieve good performance in the testing stage and had relatively good stability and representativeness. Specifically, the VMD-BPNN model considered both the prediction accuracy and computation efficiency. The results show that the reliability of the forecasting decreased as the foresight period increased. The model performed satisfactorily up to 7-d lead time. The VMD-BPNN model could be applied as a promising, reliable, and robust prediction tool for short-term streamflow forecasting modelling.

scholarly journals Enhancing Monthly Lake Levels Forecasting Using Heuristic Regression Techniques with Periodicity Data Component: Application of Lake Michigan

2021 ◽  
Author(s):  
Vahdettin DEMIR
Keyword(s):  
Lake Michigan ◽  
Absolute Error ◽  
Least Square ◽  
Multivariate Adaptive Regression Splines ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Lake Levels ◽  
M5 Model Tree ◽  
Comparison Results ◽  
Periodicity Component

Abstract This paper investigates the accuracy of three different techniques with periodicity component for estimation of monthly lake levels. The compared methods are Least Square Support Vector Regression (LSSVR) Multivariate Adaptive Regression Splines (MARS) and M5 Model Tree (M5-Tree). Data from Lake Michigan, located in the USA, is used in the analysis. In the first stage of the study, three different techniques were applied to forecast monthly lake-levels variations up to 8- mount ahead of time intervals. In the second stage, the influence of the periodicity component was applied (month number of the year, e.g., 1, 2, 3, …12) as an external sub-set in modeling monthly lake levels. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2) were utilized are used for evaluating the accuracy of models. In both stages, the comparison results indicate that the MARS model generally performs superior to the LSSVR, and M5-Tree models. Furthermore, it has been discovered that including periodicity as an input to the models improves their accuracy in projecting monthly lake levels.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

scholarly journals Implementation of Machine Learning Algorithms in Spectral Analysis of Surface Waves (SASW) Inversion

2021 ◽  
Vol 11 (6) ◽  
pp. 2557
Author(s):  
Sadia Mannan Mitu ◽  
Norinah Abd. Rahman ◽  
Khairul Anuar Mohd Nayan ◽  
Mohd Asyraf Zulkifley ◽  
Sri Atmaja P. Rosyidi
Keyword(s):  
Spectral Analysis ◽  
Surface Waves ◽  
Soil Profile ◽  
Field Tests ◽  
Iteration Process ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Complex Processes ◽  
Inversion Procedure ◽  
Inversion Analysis

One of the complex processes in spectral analysis of surface waves (SASW) data analysis is the inversion procedure. An initial soil profile needs to be assumed at the beginning of the inversion analysis, which involves calculating the theoretical dispersion curve. If the assumption of the starting soil profile model is not reasonably close, the iteration process might lead to nonconvergence or take too long to be converged. Automating the inversion procedure will allow us to evaluate the soil stiffness properties conveniently and rapidly by means of the SASW method. Multilayer perceptron (MLP), random forest (RF), support vector regression (SVR), and linear regression (LR) algorithms were implemented in order to automate the inversion. For this purpose, the dispersion curves obtained from 50 field tests were used as input data for all of the algorithms. The results illustrated that SVR algorithms could potentially be used to estimate the shear wave velocity of soil.

scholarly journals Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)

2021 ◽  
Vol 10 (4) ◽  
pp. 199
Author(s):  
Francisco M. Bellas Aláez ◽  
Jesus M. Torres Palenzuela ◽  
Evangelos Spyrakos ◽  
Luis González Vilas
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Prediction Models ◽  
Support Vector ◽  
False Alarms ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Rías Baixas ◽  
New Algorithms

This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). The models predict Pseudo-nitzschia spp. blooms in the Galician Rias Baixas. This work builds on a previous study by the authors (doi.org/10.1016/j.pocean.2014.03.003) but uses an extended database (from 2002 to 2012) and new algorithms. Our results show that RF and AdaBoost provide better prediction results compared to SVMs and NNs, as they show improved performance metrics and a better balance between sensitivity and specificity. Classical machine learning approaches show higher sensitivities, but at a cost of lower specificity and higher percentages of false alarms (lower precision). These results seem to indicate a greater adaptation of new algorithms (RF and AdaBoost) to unbalanced datasets. Our models could be operationally implemented to establish a short-term prediction system.

scholarly journals Development of Machine Learning Models for Prediction of Smoking Cessation Outcome

2021 ◽  
Vol 18 (5) ◽  
pp. 2584
Author(s):  
Cheng-Chien Lai ◽  
Wei-Hsin Huang ◽  
Betty Chia-Chen Chang ◽  
Lee-Ching Hwang
Keyword(s):  
Machine Learning ◽  
Smoking Cessation ◽  
Success Rate ◽  
Prediction Models ◽  
Smoking Status ◽  
Medical Center ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Smoking Cessation Outcome

Predictors for success in smoking cessation have been studied, but a prediction model capable of providing a success rate for each patient attempting to quit smoking is still lacking. The aim of this study is to develop prediction models using machine learning algorithms to predict the outcome of smoking cessation. Data was acquired from patients underwent smoking cessation program at one medical center in Northern Taiwan. A total of 4875 enrollments fulfilled our inclusion criteria. Models with artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LoR), k-nearest neighbor (KNN), classification and regression tree (CART), and naïve Bayes (NB) were trained to predict the final smoking status of the patients in a six-month period. Sensitivity, specificity, accuracy, and area under receiver operating characteristic (ROC) curve (AUC or ROC value) were used to determine the performance of the models. We adopted the ANN model which reached a slightly better performance, with a sensitivity of 0.704, a specificity of 0.567, an accuracy of 0.640, and an ROC value of 0.660 (95% confidence interval (CI): 0.617–0.702) for prediction in smoking cessation outcome. A predictive model for smoking cessation was constructed. The model could aid in providing the predicted success rate for all smokers. It also had the potential to achieve personalized and precision medicine for treatment of smoking cessation.

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