Modelling of soil moisture retention curve using machine learning techniques: Artificial and deep neural networks vs support vector regression models

Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform

Evaluation of data driven models for river suspended sediment concentration modeling

The rod ejection accident (REA) is the design-basis reactivity initiated event and an important aspect for a pressurized water reactor (PWR). The consequence of REA is that it introduces a large positive reactivity insertion in a core, which leads to a fast large power excursion and other parameters changing. Thus, it is important to understand the uncertainty in the parameters of reactor core when REA happens. This paper applies support vector regression (SVR) to analyze accident scenarios with control rod ejection. SVR is an approach based on machine learning and soft computing. SVR, by definition, is an application of support vector machine (SVM) to nonlinear regression problem. Furthermore, the objective of this paper is to train SVR model to identify both safe and potentially unsafe power plant conditions based on real time plant data. The data is obtained from computer generated accident scenarios and is divided into two datasets, training datasets and test datasets. The training dataset are used to train the SVR model and the test dataset are used to test the validation of this model. And then the results obtained by SVR model are compared with that of artificial neural network (ANN) model. The comparison results show that SVR model has superior performance over ANN model and agree well with the general understanding. Because the proposed methodology achieve accurate results, it is likely to be suitable for other data processing of nuclear engineering.

The absorption of capillary water is one of the most crucial factors in the flow of groundwater in rocks (CWA). Although meticulous experimental studies are needed to determine a rock’s CWA, predictive techniques might cut down on the expense and effort. There are various data mining methods for this purpose, but the considered algorithms in this study were not proposed so far for predicting the CWA. Different rock samples were taken for this purpose from various locations, yielding diverse rocks. For the prediction procedures, four support vector regression (SVR) models were created: a traditional SVR, two ensembled models, and a hybrid SVR model using the whale optimization technique (WOA - SVR). Results show that all models have acceptable performance in predicting the CWA with R2 larger than 0.797 and 0.806 for the training and testing data, respectively, representing the acceptable correlation between observed and predicted values. Regarding developed models, the conventional SVR model has the worst performance of all models. All statistical evaluation criteria were improved by assembling models, which present the ability of additive regression and bagging predictions in improving prediction processes. The hybrid WOA - SVR model has the best performance considering all indices. This hybrid model could also gain the lowest values of error indices between all SVR models, which leads to outperforming the WOA - SVR model compared to other methods.

Anthropogenic activities in coastal regions are endangering marine ecosystems. Coastal waters classified as case-II waters are especially complex due to the presence of different constituents. Recent advances in remote sensing technology have enabled to capture the spatiotemporal variability of the constituents in coastal waters. The present study evaluates the potential of remote sensing using machine learning techniques, for improving water quality estimation over the coastal waters of Hong Kong. Concentrations of suspended solids (SS), chlorophyll-a (Chl-a), and turbidity were estimated with several machine learning techniques including Artificial Neural Network (ANN), Random Forest (RF), Cubist regression (CB), and Support Vector Regression (SVR). Landsat (5,7,8) reflectance data were compared with in situ reflectance data to evaluate the performance of machine learning models. The highest accuracies of the water quality indicators were achieved by ANN for both, in situ reflectance data (89%-Chl-a, 93%-SS, and 82%-turbidity) and satellite data (91%-Chl-a, 92%-SS, and 85%-turbidity. The water quality parameters retrieved by the ANN model was further compared to those retrieved by “standard Case-2 Regional/Coast Colour” (C2RCC) processing chain model C2RCC-Nets. The root mean square errors (RMSEs) for estimating SS and Chl-a were 3.3 mg/L and 2.7 µg/L, respectively, using ANN, whereas RMSEs were 12.7 mg/L and 12.9 µg/L for suspended particulate matter (SPM) and Chl-a concentrations, respectively, when C2RCC was applied on Landsat-8 data. Relative variable importance was also conducted to investigate the consistency between in situ reflectance data and satellite data, and results show that both datasets are similar. The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters. The results indicate that the NN based machine learning approaches perform better and, thus, can be used for improved water quality monitoring with satellite data in optically complex coastal waters.

The evolution of machine learning (ML) algorithms provides researchers and engineers with state-of-the-art tools to dynamically model complex relationships. The design and operation of municipal solid waste (MSW) management systems require accurate estimation of generation rates. In this study, we applied rapid, non-linear and non-parametric data driven ML algorithms independently, multi-layer perceptron artificial neural network (MLP-ANN) and support vector regression (SVR) models to predict annual MSW generation rates in Bahrain. Models were trained and tested with MSW generation data for period of 1997-2019. The population, gross domestic product, annual tourist numbers, annual electricity consumption and total annual CO2 emissions were selected as explanatory variables and incorporated into developed models. The zero score normalization (ZSN) and minimum maximum normalization (MMN) methods were utilized to improve the quality of data and subsequently enhances the performance of ML algorithms. Statistical metrics were employed to discriminate performance of MLP-ANN and SVR models. The linear, polynomial, radial basis function (RBF) and sigmoid kernel functions were investigated to find the optimal SVR model. Results showed that RBF-SVR model with R2 value of 0.97% and 4.82% and absolute forecasting error (AFE) for the period of 2008 and 2019 exhibits superior prediction and robustness in comparison to MLP-ANN. The efficacy of MLP-ANN model was also reasonably successful with R2 value of 0.94. It was shown that MMN pre-processing generated optimal MLP-ANN model while ZSN pre-processing produced optimal RBF-SVR model. This work also highlights the importance of application of ML modelling approaches to plan and implement their roadmap for waste management systems by policymakers.

PurposeThis study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.Design/methodology/approachTwo sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated.FindingsThe findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends.Practical implicationsThe findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector.Originality/valueThis study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.

Evaluation of machine learning algorithms in the prediction of hydraulic conductivity and soil moisture at the Brazilian Savannah

Combined Terrestrial Evapotranspiration Index prediction using a hybrid artificial intelligence paradigm integrated with relief algorithm-based feature selection

ABSTRACTThis paper investigates the ability of five different data‐driven methods, artificial neural networks (ANNs), adaptive neuro‐fuzzy inference system (ANFIS) with grid partition (GP), ANFIS with subtractive clustering (SC), support vector regression (SVR) and gene expression programming (GEP), in predicting long‐term monthly temperatures by using data from 50 stations in Iran. The periodicity component (month of the year), station latitude, longitude and altitude values were used as inputs to the applied models to predict the long‐term monthly temperatures. The overall accuracy of the SVR model was found to be better than that of the other models. The GEP model gave the worst estimates. The maximum determination coefficient (R2) values were found to be 0.996, 0.999, 0.997 and 0.959 for the ANN, ANFIS‐GP, ANFIS‐SC and GEP models in Karaj and Qazvin stations, respectively. The highest R2 value (0.999) of SVR model was found for the Tabas station. The minimum R2 values were respectively found as 0.988, 0.946 and 0.985 for the ANFIS‐GP, ANFIS‐SC and SVR models in Bandar Abbas station while the ANN and GEP models gave the minimum R2 values of 0.982 and 0.886 in the Abadan and Kerman stations, respectively. The results indicated that the long‐term monthly temperatures of any site can be successfully estimated by data‐driven methods applied in this study using geographical inputs. The interpolated maps of temperatures were also obtained by using the optimal SVR model and evaluated in the study. The temperature maps showed that the highest temperatures were occurred in the southeastern and central parts of the Iran.

This chapter analyses efficiency of support vector regression (SVR), artificial neural networks (ANNs), and structural vector autoregressive (SVAR) models in terms of in-sample forecasting of portfolio inflows (PIs). Time series daily data sourced from Rand Merchant Bank (RMB) covering the period of 1st March 2004 to 1st February 2016 were used. Mean squared error, root mean squared error, mean absolute error, mean absolute squared error, and root mean scaled log error were used to evaluate model performance. The results showed that SVR has the best modelling performance when compared to others. In determining factors that affect allocation of PIs into South Africa based on SVAR, 69% of the variation was explained by pull factors while 9% was explained by push factor. Hence, SVR model is more accurate than ANNs. This chapter therefore recommends that banking sector particularly RMB should use machine learning technique in modelling PIs for a better financial solution.

Nonlinear QSAR models with high-dimensional descriptor selection and SVR improve toxicity prediction and evaluation of phenols on Photobacterium phosphoreum

The static Young's Modulus (Estatic) is an important parameter affecting the design of different aspects related to oil and gas producing wells. It is significantly changing based on the type of the formation, and hence, an accurate method of identifying Estatic is required. This study evaluates the performance of support vector regression (SVR) for prediction of the Estatic. The SVR model was learned to evaluate the Estatic from the well logs of the bulk formation density in addition to compressional and shear transit time. It was learned and tested on 592 training datasets of the inputs and their corresponding Estatic, these datasets were obtained from a sandstone formation in Well-A. The learned SVR model was then validated on 38 data points from Well-B, the performance of the optimized SVR on predicting the Estatic for the validation data was also compared with these of the early optimized artificial neural networks (ANN) and functional neural networks (FNN). As a result, all machine learning models showed high precision in predicting the Estatic for the validation data where Estatic was estimated with average absolute percentage errors of 3.80%, 2.54, and 2.03% and correlation coefficients of 0.991, 0.997, and 0.999 using the optimized ANN, FNN, and SVR models, respectively. This result shows the high accuracy of the SVR on predicting the Estatic.

Globally, buildings are responsible for an estimated 40% of energy consumption and 33% of CO2 emissions. In a bid to reduce CO2 emissions and hence, global warming, it has become necessary to ensure the energy efficient construction and operation of buildings. Understanding how a building utilises energy is a critical step to increase its efficiency. In this study, we leverage on an open-source data obtained from UCI data repository. Exploratory data analysis and feature engineering were used to eliminate non-contributing features while identifying key attributes of the data for model training. Linear Regression (LR) and Support Vector Regression (SVR) were employed as the machine learning techniques for the study. The models were trained using a repeated cross-validation technique. The models’ performance was evaluated on an independent data set segregated for testing. The LR model was trained with nine out of thirty-three features, while the Support Vector Regression (SVR) model used twenty-eight features for its training. The SVR model had a higher variance (0.48), accuracy (92.41%), and lower Mean Absolute Percentage Error (MAPE) of 7.59% compared to the LR model's variance of 0.26, accuracy of 91.87%, and MAPE of 8.13%. The SVR model was more accurate in predicting energy consumption, as it showed better accuracy on the test set with lower MAPE and higher R-squared value. Both models outperformed a relatively complex and computationally expensive model in a previous study. It also identified areas with high energy consumption which could be used to inform the building's energy management strategy.

A stacking ensemble model for predicting soil organic carbon content based on visible and near-infrared spectroscopy