High-precision transient fuel consumption model based on support vector regression

Wind energy is a promising alternativefor renewable source of energy pursued world-wide to reduce carbon emissions for a green future. The prediction of wind speed is a challenging subject and plays an instrumental role in development of wind power systems (particularly grid connected renewable energy systems where predicting wind speed facilitates manipulation of the load on the grid). Modern machine learning techniques including neural networks have been widely utilized for this purpose. Literature indicates availability of several models for estimation of the wind speed one hour ahead and the hourly wind speed data profile one day ahead. This paper considers the prediction of wind energy as a univariate time series (UVT) prediction problem and employs major prediction algorithms including the K-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Regression (SVR), Holt-Winter and ARIMA method. Forecasting a univariate time series depends only on past wind speed data values, rather than use of external data attributes like wind direction or weather forecast for prediction algorithm. In the present study (as a case-study), 13 years of hourly average wind speed data (of the period 1970-1982) of Yanbu, Saudi Arabia has been utilized to evaluate the performance of selected algorithms. Yanbu is an industrial city that plays a major role in the economy of Saudi Arabia. The findings showed that SVR, RF and ARIMA methods exhibit a better forecastingperformance in relation to four evaluation parameters of Mean Absolute Percentage Error(MAPE),Symmetric Mean Absolute Percentage Error (sMAPE),Mean Absolute Error (MAE) and Mean Absolute Scaled Error (MASE).

Experiments have proven that traditional prediction research methods have limitations in practice. Proposing countermeasures for environmental changes is the key to optimal control of the cold chain environment and reducing the lag of control effects. In this paper, a cold chain transportation environment prediction method, combining k-means++ and a long short-term memory (LSTM) neural network, is proposed according to the characteristics of the cold chain transportation environment of agricultural products. The proposed prediction model can predict the trend of cold chain environment changes in the next ten minutes, which allows cold chain vehicle managers to issue control instructions to the environmental control equipment in advance. The fusion process for temperature and humidity data measured by multiple data sensors is performed with the k-means++ algorithm, and then the fused data are fed into an LSTM neural network for prediction based on time series. The prediction error of the prediction model proposed in this paper is very satisfactory, with a root-mean-square error (RMSE), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE) and R-squared of 0.5707, 0.2484, 0.3258, 0.0312 and 0.9660, respectively, for temperature prediction, and with an RMSE, MAE, MSE, mean absolute percentage error and R-squared of 1.6015, 1.1770, 2.5648, 0.2736 and 0.9702, respectively, for humidity prediction. Finally, the LSTM neural network and back propagation (BP) neural network are compared in order to enhance the reliability of the results. In terms of the prediction effect of the temperature and humidity in cold chain vehicles transporting agricultural products, the proposed model has a higher prediction accuracy than that of existing models and can provide strategic support for the fine management and regulation of the cold chain transportation environment.

The growth and sustainability of human development rely on water availability. Proper management of Groundwater as the main source of one-third of freshwater resources is compulsory because of its renewable sources and to safeguard food sanctuary and water security. The steps ahead modeling of GRACE Terrestrial Water Storage (TWS) was employed in this study using the GRACE TWS data from 2007 to 2017 and TWS spherical harmonic solution obtained from the University of Texas (UT) center for space research.  The data was used to create the artificial intelligence-based models viz: Elman neural network (ENN) and Support vector regression (SVR) based on several input variables, including t-12, t-24, t-36, t-48, and TWS as the output variable. The models were evaluated using mean absolute error (MAE), root means square error (RMSE), mean absolute percentage error (MAPE), correlation coefficient (CC), and Nash–Sutcliffe efficiency (NSE). The estimation outcomes depicted that only SVR-M1 (NSE=0.993, MAE0.0346) generated promising results, with ENN-M3 (NSE=0.6586, MAE=0.6895) as the second-best model. All other models’ combinations were within the range of good to marginal accuracies, which are unreliable for decision-making. 

Unmixing urban hyperspectral imagery using probability distributions to represent endmember variability

Tomato is one of the most important vegetables due to its high production and nutritional value. With the development of digital agriculture, the tomato breeding and processing industries have seen a rapid increase in the need for simple, low-labor, and inexpensive methods for analyzing tomato composition. This study proposes a digital method to predict four minerals (calcium, potassium, phosphorus, and magnesium) in beef-type tomato using machine learning models, including k-nearest neighbors (kNN), artificial neural networks (ANNs), and Support Vector Regression (SVR). The models were discriminated using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The kNN model showed the best performance for estimation of quantity of calcium, potassium, phosphorus, and magnesium. The results demonstrate that kNN consistently outperforms ANNs and SVR across all target nutrients, achieving the highest R2 and the lowest error metrics (RMSE, MAE, and MAPE). Notably, kNN achieved an exceptional R2 of 0.8723 and a remarkably low MAPE of 3.95% in predicting phosphorus. This study highlights how machine learning can provide a versatile, accurate, and efficient solution for tomato mineral analysis in digital agriculture.

Compressive strength is an important mechanical property of high-strength concrete (HSC), but testing methods are usually uneconomical, time-consuming, and labor-intensive. To this end, in this paper, a long short-term memory (LSTM) model was proposed to predict the HSC compressive strength using 324 data sets with five input independent variables, namely water, cement, fine aggregate, coarse aggregate, and superplasticizer. The prediction results were compared with those of the conventional support vector regression (SVR) model using four metrics, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient (R2). The results showed that the prediction accuracy and reliability of LSTM were higher with R2 = 0.997, RMSE = 0.508, MAE = 0.08, and MAPE = 0.653 compared to the evaluation metrics R2 = 0.973, RMSE = 1.595, MAE = 0.312, MAPE = 2.469 of the SVR model. The LSTM model is recommended for the pre-estimation of HSC compressive strength under a given mix ratio before the laboratory compression test. Additionally, the Shapley additive explanations (SHAP)-based approach was performed to analyze the relative importance and contribution of the input variables to the output compressive strength.

The current work aims to develop computational models for the thermal characteristics of turbulent CH4 flames for varying burner dimensions. This study develops a platform for data-driven analysis of temperature prediction of turbulent non-premixed flames, in which the influence of flow and geometric parameters, including burner head diameter (D), half cone angles (α), and co-flow air velocity (Ucf), have been considered. The algorithms used were ridge regressor (RR), linear regressor (LR), and three variations of support vector regression (SVR): SVR with a linear kernel (SVR-LR), SVR with a radial basis function (SVR-RBF), and SVR with a polynomial kernel (SVR-Poly). The performance of each computational model was evaluated and contrasted based on several metrics: mean absolute error, regression coefficient (R2), mean absolute percentage error, and mean Poisson deviance. From the modeling of the output data, it was observed that the SVR-RBF predictions were more accurate compared to those from the other algorithms, as it achieved the highest training value of 0.955. The testing predictions of RR, SVR-LR, SVR-RBF, and SVR-Poly algorithms were also robust, with R2 values ranging between 0.91 and 0.94. It is, therefore, established that these computational models are effectively suited for predicting sensitive turbulent CH4 flame characteristics based on varying input factors.

<p>Accurate water level (WL) forecasting is important for water resources management and planning purposes in the Great Lakes. The objectives of this research are two-fold.  The first objective is to apply machine learning (ML) (i.e., random forest (RF) and support vector regression (SVR)) and hybrid convolutional neural network(CNN)-long-short term memory (LSTM) deep learning (DL) models for multi-step (i.e., one-, two- and three-monthly step ahead) WL forecasting in the Great Lakes (Michigan and Ontario). The second objective is to integrate the boundary corrected (BC) maximal overlap discrete wavelet transform (MODWT) with SVR, RF, and CNN-LSTM models to improve the performance of the individual models. By employing a BC-wavelet decomposition method, the ‘future data’ issue (i.e., data from the future that is not available), often overlooked in the literature and a major barrier to achieving realistic forecasting performance is overcome. </p><p>For Lakes Michigan and Ontario, 1212 monthly WL (m) records (spanning Jan 1918–Dec 2018) were used to develop the models. For the non-wavelet-based models (SVR, RF, and CNN-LSTM), candidate model inputs included the WL recorded over the previous 12 months.  For the BC-MODWT-based models (BC-MODWT-SVR, BC-MODWT-RF, and BC-MODWT-CNN-LSTM), the lagged input time series were decomposed into BC-wavelet and scaling coefficients by using different mother wavelets (Haar, Daubechies, Symlets, Fejer-Korovkin and Coiflets), filter lengths (from two up to 12) and decomposition levels (from one up to seven).  For each method (SVR, RF, and CNN-LSTM), mother wavelet, and decomposition level a model was generated.  For both wavelet- and non-wavelet-based models, the particle swarm optimization (PSO) method was used to select the most appropriate inputs to include in the proposed multi-step WL forecasting models.</p><p>The datasets were partitioned into calibration and validation subsets. After calibrating the models, various performance evaluation metrics, e.g., coefficient of determination (R<sup>2</sup>), root mean square error (RMSE), mean absolute error (MAE), root mean square percentage error (RMSPE), mean absolute percentage error (MAPE) and the Nash-Sutcliffe efficiency coefficient (NSC) were used to assess model accuracy.</p><p>Of the ML models, the SVR outperformed RF while the DL models outperformed the ML models for each forecast lead time (one-, two-, and three-step(s) ahead). Results from this case study indicate that not all wavelet families and decomposition levels perform equally and in some cases, the wavelet-based models do not improve performance over the non-wavelet-based models. However, the BC-MODWT-CNN-LSTM using suitable mother wavelets (e.g., Haar) outperforms the individual ML and BC-MODWT-ML-based models. More accurate forecasts were obtained for Lake Michigan although the performance in both Great Lakes was accurate. The outcomes of this research indicate that the BC-MODWT-CNN-LSTM model is a promising tool for generating accurate WL forecasts.</p>

. This study introduces support vector regression (SVR) approach to model the relationship between the glass transition temperature (Tg) and multipole moments for polymers. SVR was trained and tested via 60 samples by using two quantum chemical descriptors including the molecular traceless quadrupole moment and the molecular average hexadecapole moment Φ. The prediction performance of SVR was compared with that of reported quantitative structure property relationship (QSPR) model. The results show that the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE) of training samples and test samples achieved by SVR model, are smaller than those achieved by the QSPR model, respectively. This investigation reveals that SVR-based modeling is a practically useful tool in prediction of the glass transition temperature of polymers.

In recent times, gold has been one of the prioritized commodities in terms of long term as well as short term investments since the investors consider gold as a hedgerow against the unforeseen events leading to chaos in the market. Consequentially, the price of gold in the market plays an important role. In this research work, time-series gold price prediction models have been developed using the support vector regression and anfis models for the prediction of daily gold prices. The support vector model was designed using epsilon support vector regression method while the adaptive neural fuzzy inference systems have been developed using grid partition and subtractive clustering methods. The gold prices obtained for the training and testing were obtained from Perth Mint of Australia. The evaluation criteria for the comparison of the models are Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Nash-Sutcliffe model efficiency coefficient (E) and Mean absolute percentage error (MAPE). It was observed that the models obtained using support vector regression outperformed the ANFIS models. In the ANFIS models, it was observed that ANFIS-GP performed slightly better than the ANFIS-SC model.

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.

Background Leaf area is a crucial indicator of plant growth and physiology, with direct measurements being destructive to the plant. This study aimed to develop and compare machine learning models [support vector regression (SVR), adaptive neuro-fuzzy inference system (ANFIS), and deep multilayer perceptron (DMLP)] and linear regression (LRM) for the nondestructive prediction of leaf area in five colored cotton cultivars. A total of 1 334 leaves were sampled, and their length (L), width (W), and leaf area (LA) were determined via digitized images. The models were developed using 70% of the data for training and 30% for validation. Their performance was evaluated using the coefficient of determination ( R 2 ), root mean square error, mean absolute error, mean absolute percentage error, and Willmott's index of agreement. Results The results showed that the machine learning models, notably the ANFIS (triangular membership function), the DMLP (2–16-16–1 configuration), and the SVR [radial basis function (RBF) kernel], significantly outperformed the linear regression models in leaf area estimation accuracy. The ANFIS and DMLP models achieved the highest R 2 (0.979 3, test), followed by the SVR model ( R 2 = 0.979 0, test), all with minimal errors. Among the linear models, the LRM (using the L × W product) was the most effective ( R 2 = 0.978 3). Conclusions On the basis of the performance criteria of the models, the machine learning models are more accurate for the nondestructive estimation of leaf area in colored cotton. The best-performing model (SVR with RBF kernel) was made available in an interactive web application, aiming to optimize crop management with accurate and nondestructive data.

The primary objective of this study is to evaluate the accuracy of different forecasting models for monthly wind farm electricity production. This study compares the effectiveness of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Support Vector Regression (SVR). This study utilizes data from two wind farms located in Poland—‘Gizałki’ and ‘Łęki Dukielskie’—to exclude the possibility of biased results due to specific characteristics of a single farm and to allow for a more comprehensive comparison of the effectiveness of both time series analysis methods. Model parameterization was optimized through a grid search based on the Mean Absolute Percentage Error (MAPE). The performance of the best models was evaluated using Mean Bias Error (MBE), MAPE, Mean Absolute Error (MAE), and R2Score. For the Gizałki farm, the ARIMA model outperformed SARIMA and SVR, while for the Łęki Dukielskie farm, SARIMA proved to be the most accurate, highlighting the importance of optimizing seasonal parameters. The SVR method demonstrated the lowest effectiveness for both datasets. The results indicate that the ARIMA and SARIMA models are effective for forecasting wind farm energy production. However, their performance is influenced by the specificity of the data and seasonal patterns. The study provides an in-depth analysis of the results and offers suggestions for future research, such as extending the data to include multidimensional time series. Our findings have practical implications for enhancing the accuracy of wind farm energy forecasts, which can significantly improve operational efficiency and planning.

. Based on two quantum chemical descriptors (the thermal energy Ethermal and the total energy of the whole system EHF) calculated from the structures of the repeat units of polyacrylamides by density functional theory (DFT), the support vector regression (SVR) approach combined with particle swarm optimization (PSO), is proposed to establish a model for prediction of the glass transition temperature (Tg) of polyacrylamides. The prediction performance of SVR was compared with that of multivariate linear regression (MLR). The results show that the mean absolute error (MAE=4.65K), mean absolute percentage error (MAPE=1.28%) and correlation coefficient (R2=0.9818) calculated by leave-one–out cross validation (LOOCV) via SVR models are superior to those achieved by QSPR (MAE=14.25K, MAPE=4.39% and R2=0.9211) and QSPR-LOO (MAE=17.01K, MAPE=5.66% and R2=0.8823) models for the identical samples, respectively. The prediction results strongly demonstrate that the modeling and generalization abilities of SVR model consistently surpass those of QSPR and QSPR-LOO models. It is revealed that the established SVR model is more suitable to be used for prediction of the Tg values for unknown polymers possessing similar structure than the conventional MLR approach. These suggest that SVR is a promising and practical methodology to predict the glass transition temperature of polyacrylamides.

Commodity markets, such as crude oil and precious metals, play a strategic role in the economic development of nations, with crude oil prices influencing geopolitical relations and the global economy. Moreover, gold and silver are argued to hedge the stock and cryptocurrency markets during market downsides. Therefore, accurate forecasting of crude oil and precious metals prices is critical. Nevertheless, due to the nonlinear nature, substantial fluctuations, and irregular cycles of crude oil and precious metals, predicting their prices is a challenging task. Our study contributes to the commodity market price forecasting literature by implementing and comparing advanced deep-learning models. We address this gap by including silver alongside gold in our analysis, offering a more comprehensive understanding of the precious metal markets. This research expands existing knowledge and provides valuable insights into predicting commodity prices. In this study, we implemented 16 deep- and machine-learning models to forecast the daily price of the West Texas Intermediate (WTI), Brent, gold, and silver markets. The employed deep-learning models are long short-term memory (LSTM), BiLSTM, gated recurrent unit (GRU), bidirectional gated recurrent units (BiGRU), T2V-BiLSTM, T2V-BiGRU, convolutional neural networks (CNN), CNN-BiLSTM, CNN-BiGRU, temporal convolutional network (TCN), TCN-BiLSTM, and TCN-BiGRU. We compared the forecasting performance of deep-learning models with the baseline random forest, LightGBM, support vector regression, and k-nearest neighborhood models using mean absolute error (MAE), mean absolute percentage error, and root mean squared error as evaluation criteria. By considering different sliding window lengths, we examine the forecasting performance of our models. Our results reveal that the TCN model outperforms the others for WTI, Brent, and silver, achieving the lowest MAE values of 1.444, 1.295, and 0.346, respectively. The BiGRU model performs best for gold, with an MAE of 15.188 using a 30-day input sequence. Furthermore, LightGBM exhibits comparable performance to TCN and is the best-performing machine-learning model overall. These findings are critical for investors, policymakers, mining companies, and governmental agencies to effectively anticipate market trends, mitigate risk, manage uncertainty, and make timely decisions and strategies regarding crude oil, gold, and silver markets.