scholarly journals Prediction of Wave Transmission Characteristics of Low-Crested Structures with Comprehensive Analysis of Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8192
Author(s):  
Taeyoon Kim ◽  
Soonchul Kwon ◽  
Yongju Kwon
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Prediction Accuracy ◽  
Wave Attenuation ◽  
Linear Regression Analysis ◽  
Comprehensive Analysis ◽  
Wave Transmission ◽  
Dominant Factor ◽  
Gradient Boosting ◽  
Empirical Formulas

The adoption of low-crested and submerged structures (LCS) reduces the wave behind a structure, depending on the changes in the freeboard, and induces stable waves in the offshore. We aimed to estimate the wave transmission coefficient behind LCS structures to determine the feasible characteristics of wave mitigation. In addition, various empirical formulas based on regression analysis were proposed to quantitatively predict wave attenuation characteristics for field applications. However, inherent variability of wave attenuation causes the limitation of linear statistical approaches, such as linear regression analysis. Herein, to develop an optimization model for the hydrodynamic behavior of the LCS, we performed a comprehensive analysis of 10 types of machine learning models, which were compared and reviewed on the prediction accuracy with existing empirical formulas. We found that, among the 10 models, the gradient boosting model showed the highest prediction accuracy with MSE of 1.0 × 10−3, an index of agreement of 0.996, a scatter index of 0.065, and a correlation coefficient of 0.983, which indicates a performance improvement over the existing empirical formulas. In addition, based on a variable importance analysis using explainable artificial intelligence, we determined the significant importance of the input variable for the relative freeboard (RC/H0) and the relative freeboard to water depth ratio (RC/h), which confirms that the relative freeboard was the most dominant factor for influencing wave attenuation in the hydraulic behavior around the LCS. Thus, we concluded that the performance prediction method using a machine learning model can be applied to various predictive studies in the field of coastal engineering, deviating from existing empirical-based research.

scholarly journals Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model

2018 ◽  
Vol 7 (11) ◽  
pp. 428 ◽  
Author(s):  
Hyung-Chul Lee ◽  
Soo Yoon ◽  
Seong-Mi Yang ◽  
Won Kim ◽  
Ho-Geol Ryu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Transplantation ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
Gradient Boosting Machine

Acute kidney injury (AKI) after liver transplantation has been reported to be associated with increased mortality. Recently, machine learning approaches were reported to have better predictive ability than the classic statistical analysis. We compared the performance of machine learning approaches with that of logistic regression analysis to predict AKI after liver transplantation. We reviewed 1211 patients and preoperative and intraoperative anesthesia and surgery-related variables were obtained. The primary outcome was postoperative AKI defined by acute kidney injury network criteria. The following machine learning techniques were used: decision tree, random forest, gradient boosting machine, support vector machine, naïve Bayes, multilayer perceptron, and deep belief networks. These techniques were compared with logistic regression analysis regarding the area under the receiver-operating characteristic curve (AUROC). AKI developed in 365 patients (30.1%). The performance in terms of AUROC was best in gradient boosting machine among all analyses to predict AKI of all stages (0.90, 95% confidence interval [CI] 0.86–0.93) or stage 2 or 3 AKI. The AUROC of logistic regression analysis was 0.61 (95% CI 0.56–0.66). Decision tree and random forest techniques showed moderate performance (AUROC 0.86 and 0.85, respectively). The AUROC of support the vector machine, naïve Bayes, neural network, and deep belief network was smaller than that of the other models. In our comparison of seven machine learning approaches with logistic regression analysis, the gradient boosting machine showed the best performance with the highest AUROC. An internet-based risk estimator was developed based on our model of gradient boosting. However, prospective studies are required to validate our results.

scholarly journals Comparing Machine Learning Models and Hybrid Geostatistical Methods Using Environmental and Soil Covariates for Soil pH Prediction

2020 ◽  
Vol 9 (4) ◽  
pp. 276
Author(s):  
Panagiotis Tziachris ◽  
Vassilis Aschonitis ◽  
Theocharis Chatzistathis ◽  
Maria Papadopoulou ◽  
Ioannis (John) D. Doukas
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Random Forests ◽  
Soil Ph ◽  
Prediction Accuracy ◽  
Gradient Boosting ◽  
Soil Parameters ◽  
Geostatistical Methods ◽  
Significant Difference ◽  
Hyperparameter Selection

In the current paper we assess different machine learning (ML) models and hybrid geostatistical methods in the prediction of soil pH using digital elevation model derivates (environmental covariates) and co-located soil parameters (soil covariates). The study was located in the area of Grevena, Greece, where 266 disturbed soil samples were collected from randomly selected locations and analyzed in the laboratory of the Soil and Water Resources Institute. The different models that were assessed were random forests (RF), random forests kriging (RFK), gradient boosting (GB), gradient boosting kriging (GBK), neural networks (NN), and neural networks kriging (NNK) and finally, multiple linear regression (MLR), ordinary kriging (OK), and regression kriging (RK) that although they are not ML models, they were used for comparison reasons. Both the GB and RF models presented the best results in the study, with NN a close second. The introduction of OK to the ML models’ residuals did not have a major impact. Classical geostatistical or hybrid geostatistical methods without ML (OK, MLR, and RK) exhibited worse prediction accuracy compared to the models that included ML. Furthermore, different implementations (methods and packages) of the same ML models were also assessed. Regarding RF and GB, the different implementations that were applied (ranger-ranger, randomForest-rf, xgboost-xgbTree, xgboost-xgbDART) led to similar results, whereas in NN, the differences between the implementations used (nnet-nnet and nnet-avNNet) were more distinct. Finally, ML models tuned through a random search optimization method were compared with the same ML models with their default values. The results showed that the predictions were improved by the optimization process only where the ML algorithms demanded a large number of hyperparameters that needed tuning and there was a significant difference between the default values and the optimized ones, like in the case of GB and NN, but not in RF. In general, the current study concluded that although RF and GB presented approximately the same prediction accuracy, RF had more consistent results, regardless of different packages, different hyperparameter selection methods, or even the inclusion of OK in the ML models’ residuals.

scholarly journals Development of a Probabilistic Seismic Performance Assessment Model of Slope Using Machine Learning Methods

2020 ◽  
Vol 12 (8) ◽  
pp. 3269
Author(s):  
Shinyoung Kwag ◽  
Daegi Hahm ◽  
Minkyu Kim ◽  
Seunghyun Eem
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Linear Regression ◽  
Seismic Performance ◽  
Prediction Models ◽  
Linear Regression Analysis ◽  
Assessment Model ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods

The objective of this study is to propose a model that can predict the seismic performance of slope relatively accurately and efficiently by using machine learning methods. Probabilistic seismic fragility analyses of the slope had been carried out in other studies, and a closed-form equation for slope seismic performance was proposed through a multiple linear regression analysis. However, the traditional statistical linear regression analysis showed a limit that could not accurately represent such nonlinear slope seismic performances. To overcome this limit, in this study, we used three machine learning methods (i.e., support vector machine (SVM), artificial neural network (ANN), Gaussian process regression (GPR)) to generate prediction models of the slope seismic performance. The models obtained through the machine learning methods basically showed better performance compared to the models of the traditional statistical methods. The results of the SVM showed no significant performance difference compared with the results of the nonlinear regression analysis method, but the results based on the ANN and GPR showed a remarkable improvement in the prediction performance over the other models. Furthermore, this study confirmed that the GPR-based model predicted relatively accurate seismic performance values compared with the model through the ANN.

The extraction of early warning features for the predicting financial distress based on XGboost model and shap framework

2021 ◽  
pp. 2141004
Author(s):  
He Yang ◽  
Emma Li ◽  
Yi Fang Cai ◽  
Jiapei Li ◽  
George X. Yuan
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Financial Distress ◽  
Prediction Accuracy ◽  
Financial Risk ◽  
Learning Algorithm ◽  
Listed Companies ◽  
Gradient Boosting ◽  
Distress Risk ◽  
Extreme Gradient Boosting

The purpose of this paper is to establish a framework for the extraction of early warning risk features for the predicting financial distress based on XGBoost model and SHAP. It is well known that the way to construct early warning risk features to predict financial distress of companies is very important, and by comparing with the traditional statistical methods, though the data-driven machine learning for the financial early warning, modelling has a better performance in terms of prediction accuracy, but it also brings the difficulty such as the one the corresponding model may be not explained well. Recently, eXtreme Gradient Boosting (XGBoost), an ensemble learning algorithm based on extreme gradient boosting, has become a hot topic in the area of machine learning research field due to its strong nonlinear information recognition ability and high prediction accuracy in the practice. In this study, the XGBoost algorithm is used to extract early warning features for the predicting financial distress for listed companies, with 76 financial risk features from seven categories of aspects, and 14 non-financial risk features from four categories of aspects, which are collected to establish an early warning system for the predication of financial distress. With applications, we conduct the empirical testing respect to AUC, KS and Kappa, the numerical results show that by comparing with the Logistic model, our method based on XGBoost model established in this paper has much better ability to predict the financial distress risk of listed companies. Moreover, under the framework of SHAP (SHAPley Additive exPlanations), we are able to give a reasonable explanation for important risk features and influencing ways affecting the financial distress visibly. The results given by this paper show that the XGBoost approach to model early warning features for financial distress does not only preform a better prediction accuracy, but also is explainable, which is significant for the identification of early warning to the financial distress risk for listed companies in the practice.

Improving Site-Dependent Wind Turbine Performance Prediction Accuracy Using Machine Learning

2022 ◽  
Author(s):  
Sarah Barber ◽  
Florian Hammer ◽  
Adrian Tica
Keyword(s):  
Machine Learning ◽  
Wind Turbine ◽  
Prediction Accuracy ◽  
Wind Farm ◽  
Gradient Boosting ◽  
K Nearest Neighbors ◽  
Site Specific ◽  
Turbine Performance ◽  
Extreme Gradient Boosting ◽  
Out Of Plane

Abstract Data-driven wind turbine performance predictions, such as power and loads, are important for planning and operation. Current methods do not take site-specific conditions such as turbulence intensity and shear into account, which could result in errors of up to 10%. In this work, four different machine learning models (k-nearest neighbors regression, random forest regression, extreme gradient boosting regression and artificial neural networks (ANN) are trained and tested, firstly on a simulation dataset and then on a real dataset. It is found that machine learning methods that take site-specific conditions into account can improve prediction accuracy by a factor of two to three, depening on the error indicator chosen. Similar results are observed for multi-output ANNs for simulated in- and out-of-plane rotor blade tip deflection and root loads. Future work focuses on understanding transferability of results between different turbines within a wind farm and between different wind turbine types.

The Determinant Factors of Foreign Tourists’ Visit to Tourism Destination in North Sumatra

2019 ◽  
Vol 10 (1) ◽  
pp. 266
Author(s):  
Maria Magdalena BATE’E ◽  
Syah Abadi MENDROFA ◽  
Yamolala ZEGA ◽  
Syukur Kasieli HULU ◽  
LELYSO LELYSO ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Negative Influence ◽  
Dominant Factor ◽  
Tourism Destination ◽  
Factors Influencing ◽  
North Sumatra

This research aims to investigate the factors influencing the rate of foreign tourists’ visit to tourism destination in North Sumatra, Indonesia. The data were collected using observation, interview, documentation study and questionnaires. The data were analysed using multiple linear regression analysis. The findings of the study indicated that the ancillary facilities are the dominant factor influencing tourists’ visit to tourism destination in North Sumatera. The factors of service quality and facilities and infrastructures provide negative influence on tourists’ visit to tourism destination.

scholarly journals Toward Improving the Prediction Accuracy of Product Recommendation System Using Extreme Gradient Boosting and Encoding Approaches

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1566 ◽  
Author(s):  
Zeinab Shahbazi ◽  
Debapriya Hazra ◽  
Sejoon Park ◽  
Yung Cheol Byun
Keyword(s):  
Machine Learning ◽  
South Korea ◽  
Collaborative Filtering ◽  
Mean Square Error ◽  
Prediction Accuracy ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Gradient Boosting ◽  
Mean Square ◽  
Extreme Gradient Boosting

With the spread of COVID-19, the “untact” culture in South Korea is expanding and customers are increasingly seeking for online services. A recommendation system serves as a decision-making indicator that helps users by suggesting items to be purchased in the future by exploring the symmetry between multiple user activity characteristics. A plethora of approaches are employed by the scientific community to design recommendation systems, including collaborative filtering, stereotyping, and content-based filtering, etc. The current paradigm of recommendation systems favors collaborative filtering due to its significant potential to closely capture the interest of a user as compared to other approaches. The collaborative filtering harnesses features like user-profile details, visited pages, and click information to determine the interest of a user, thereby recommending the items that are related to the user’s interest. The existing collaborative filtering approaches exploit implicit and explicit features and report either good classification or prediction outcome. These systems fail to exhibit good results for both measures at the same time. We believe that avoiding the recommendation of those items that have already been purchased could contribute to overcoming the said issue. In this study, we present a collaborative filtering-based algorithm to tackle big data of user with symmetric purchasing order and repetitive purchased products. The proposed algorithm relies on combining extreme gradient boosting machine learning architecture with word2vec mechanism to explore the purchased products based on the click patterns of users. Our algorithm improves the accuracy of predicting the relevant products to be recommended to the customers that are likely to be bought. The results are evaluated on the dataset that contains click-based features of users from an online shopping mall in Jeju Island, South Korea. We have evaluated Mean Absolute Error, Mean Square Error, and Root Mean Square Error for our proposed methodology and also other machine learning algorithms. Our proposed model generated the least error rate and enhanced the prediction accuracy of the recommendation system compared to other traditional approaches.

scholarly journals Analisis Profitabilitas Usaha Peternakan Ayam Kampung Super di Kabupaten Jember

2017 ◽  
Vol 1 (1) ◽  
pp. 31-38
Author(s):  
Muhammad Ardi Wiranata ◽  
Joko Irsan Sanyoto ◽  
Hariadi Subagja
Keyword(s):  
Regression Analysis ◽  
Research Method ◽  
Cost Management ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Dominant Factor ◽  
Selling Price ◽  
Feed Cost ◽  
The Cost ◽  
Data Result

This study aimed to determine the cost of management, day old chick cost, production, selling price to profit level, R / C ratio and Return on Investmen (ROI) ayam kampung super in Jember. The research method used was survey with 18 respondents in Jember. The data result were analyzed using multiple linear regression analysis and the following equations were Y = -1.90 – 0.35X1 – 1.22X2 + 0.23X3 + 2.26X4 + 0.11X5. This equation means every level of profit level by X1 (management cost) equal to -0.35, X2 (feed cost) equal to -1.22, X3 (seed cost) equal to 0.23, X4 (chicken production) equal to 2.26, X5 (chicken selling price) of 0.11. R / C ratio was 1.18, and ROI was 16%. The result of research showed that the most dominant factor influences to the profit level were  feed cost, management cost and production. Profitability of this research 98% have been considered while factors outside this study 2%.

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI PERAN PENJUAL KOMODITAS HORTIKULTURA OLEH KAUM IBU DALAM MENINGKATKAN EKONOMI KELUARGA DI PASAR WAMANGGU KOTA MERAUKE

2019 ◽  
Vol 1 (2) ◽  
pp. 53-59
Author(s):  
Nurdhaiyah Nurdhaiyah ◽  
David Oscar Simatupang ◽  
Untari Untari
Keyword(s):  
Regression Analysis ◽  
Descriptive Analysis ◽  
Linear Regression Analysis ◽  
Sampling Technique ◽  
Multiple Linear Regression Analysis ◽  
Dominant Factor ◽  
Quantitative Descriptive Analysis ◽  
Family Economy ◽  
The Family

This study aims to find out how much the factors of education, capital, and dependents influence the role of mothers in improving the family economy in Wamanggu Market and analyze the dominant factors that influence the role of mothers in improving the family economy in Wamanggu Market. This research was conducted at the Wamanggu Merauke Market, for four (4) months, namely from August to December 2018. The population in this study were all vegetable sellers in the Wamanggu Merauke Market. The sampling technique was random sampling with 173 samples and a total population of 63 sellers, while data analysis was carried out with quantitative descriptive analysis method using multiple linear regression analysis. The results of multiple regression analysis show that the magnitude of the effect of education on income (Y): based on education (X1) affects income (Y) insignificantly by 0.044 or 4.4%, based on capital (X2) affects income (Y) significantly by 0.919 or 91.9%, based on dependents (X3) affect income (Y) not significantly by 0.005 or 5%. The dominant factor that influences the decision of mothers in improving the family economy is the capital factor (X2) which is equal to 0.919 or 91.9%.   Keywords: Education, Capital, Dependents, Income.

scholarly journals Predicting Growth and Carcass Traits in Swine Using Metagenomic Data and Machine Learning Algorithms

2018 ◽  
Author(s):  
Christian Maltecca ◽  
Duc Lu ◽  
Costantino Schillebeeckx ◽  
Nathan P McNulty ◽  
Clint Schwab ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Carcass Traits ◽  
Live Weight ◽  
Carcass Composition ◽  
Machine Learning Algorithms ◽  
Metagenomic Data ◽  
Gradient Boosting ◽  
Phenotypic Traits ◽  
Microbiome Composition

ABSTRACTIn this paper, we evaluated the power of metagenome measures taken at three time points over the growth test period (weaning, 15 and 22 weeks) to foretell growth and carcass traits in 1039 individuals of a line of crossbred pigs. We measured prediction accuracy as the correlation between actual and predicted phenotypes in a five-fold cross-validation setting. Phenotypic traits measured included live weight measures and carcass composition obtained during the trial as well as at slaughter. We employed a null model excluding microbiome information as a baseline to assess the increase in prediction accuracy stemming from the inclusion of operational taxonomic units (OTU) as predictors. We further contrasted performance of models from the Bayesian alphabet (Bayesian Lasso) as well machine learning approaches (Random Forest and Gradient Boosting) and semi-parametric kernel models (Reproducing Kernel Hilbert space). In most cases, prediction accuracy increased significantly with the inclusion of microbiome data. Accuracy was more substantial with the inclusion of metagenomic information taken at week 15 and 22, with values ranging from approximately 0.30 for loin traits to more than 0.50 for back-fat. Conversely, microbiome composition at weaning resulted in most cases in marginal gains of prediction accuracy, suggesting that later measures might be more useful to include in predictive models. Model choice affected predictions marginally with no clear winner for any model/trait/time point. We, therefore, suggest average prediction across models as a robust strategy in fitting metagenomic information. In conclusion, microbiome composition can effectively be used as a predictor of growth and composition traits, particularly for fatness traits. The inclusion of OTU predictors could potentially be used to promote fast growth of individuals while limiting fat accumulation. Early microbiome measures might not be good predictors of growth and OTU information might be best collected at later life stages. Future research should focus on the inclusion of both microbiome as well as host genome information in predictions, as well as the interaction between the two. Furthermore, the influence of microbiome on feed efficiency as well as carcass and meat quality should be investigated.

Sign in / Sign up

Export Citation Format

Share Document