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Miniaturized near-infrared (NIR) spectrometers are revolutionizing the agri-food industry thanks to their compact size and ultra-fast analysis capabilities. This work compares the analytical performance of a handheld NIR spectrometer and a benchtop FT-NIR for the determination of several parameters, namely, pH, electrical conductivity (EC25), C/N ratio, and organic matter as LOI (loss-on-ignition) in compost. Samples were collected at different stages of maturity from a full-scale facility that processes olive mill semi-solid residue together with olive tree pruning residue and animal manure. Using an FT-NIR spectrometer, satisfactory predictions (RPD > 2.0) were obtained with both partial least squares (PLS) and support vector machine (SVM) regression, SVM clearly being superior in the case of pH (RMSEP = 0.26; RPD = 3.8). The superior performance of the FT-NIR spectrometer in comparison with the handheld spectrometer was essentially due to the extended spectral range, especially for pH. In general, when analyzing intact samples with the miniaturized spectrometer, sample rotation decreased RMSEP values (~20%). Nevertheless, a fast and simple assessment of compost quality with reasonable prediction performance can also be achieved on intact samples by averaging static measurements acquired at different sample positions. [ABSTRACT FROM AUTHOR]

Copyright of LHB: Hydroscience Journal is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To reduce the disasters caused by slope instability, this paper proposes a new machine learning (ML) model for slope stability prediction. This improved SVR model uses support vector machine regression (SVR) as the basic prediction tool and the grid search method with 5-fold cross-validation to optimize the hyperparameters to improve the prediction performance. Six features, namely, unit weight, cohesion, friction angle, slope angle, slope height, and pore pressure ratio, were taken as the input of the model, and the factor of safety was taken as the model output. Four statistical indicators, namely, the coefficient of determination (R2), mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE), were introduced to assess the generalization performance of the model. Finally, the feature importance score of the features was clarified by calculating the importance of the six features and visualizing them. The results show that the model can well describe the nonlinear relationship between features and the factor of safety. The R2, MAPE, MAE, and RMSE of the testing dataset were 0.901, 7.41%, 0.082, and 0.133, respectively. Compared with other ML models, the improved SVR model had a better effect. The most sensitive feature was unit weight. [ABSTRACT FROM AUTHOR]

This paper provides a thorough comparative review of deception detection techniques employed for a sample of 400 convicted offenders. It focuses on the utilization of polygraph sensor data as input variables for predicting deception, which are assessed against manual scoring by experts. Three advanced machine learning models, namely Random Forest Regression (RFR), Support Vector Machine (SVM) Regression, and Neural Network Regression (NNR), were employed with the purpose of analysing their predictive efficacy in identifying deception based on physiological responses captured by polygraph sensors. The obtained results indicate that all three algorithms exhibited varying degrees of effectiveness in predicting deceptive behavior. The Random Forest Regression algorithm achieved a Mean Squared Error (MSE) of 0.893 and a coefficient of determination (R²) of 0.091, which highlights its ability to discern key physiological indicators related to deceptive behavior. The Support Vector Machine Regression algorithm showed a competitive performance with a MSE of 0.98 and a R² value of 0.159, which underscores its capability to model non-linear relationships in the context of high-dimensional data. However, the Neural Network Regression algorithm proved to be the best model, with a MSE of 0.894 and a significantly higher R² value of 0.113. This model’s capacity to capture the complex relationships in the context of physiological data allowed it to surpass both RFR and SVM, which indicates its potential for a precise and reliable deception detection. This study provides valuable insights into the advancement of forensic applications with regard to deception detection technologies. Its findings suggest that the Neural Network Regression algorithm, due to its ability to learn complex patterns and relationships related to physiological data, stands out as an optimal choice for accurately identifying deceptive behavior. [ABSTRACT FROM AUTHOR]

Performance assessment of existing building structures, especially concrete compressive strength assessment, is a crucial aspect of engineering construction for most industrialized countries. Non-destructive testing (NDT) techniques are commonly employed to assess the compressive strength of concrete structures. However, existing methods for predicting concrete compressive strength using NDT techniques and machine learning methods do not take into account the concrete mix proportion design. This study proposes an effective method to predict concrete compressive strength by combining NDT tests with different mix proportion designs and curing ages. Specifically, support vector machine regression (SVR) and back propagation neural network (BPNN) models are established. Furthermore, various machine learning evaluation indexes are utilized to assess the model performance. To construct and validate the prediction models, a total of 180 datasets containing concrete specimens with different mix proportion designs and curing ages are collected from the existing research literature. The prediction results show that the coefficients of determination (R2) of the SVR and the BPNN prediction models for the test set of concrete compressive strength are 86.0 % and 86.7 % without considering the concrete mix proportion design. The R2 of the prediction results of the SVR model is higher than 95 % when considering the effects of concrete mix proportion design and curing age. The R2 of the BPNN prediction model ranged between 92 % and 97 %. All the evaluation indexes of the SVR model for predicting the compressive strength of concrete are better than those of the BPNN model. Consequently, the SVR model can be utilized to accurately evaluate concrete compressive strength during the structural performance assessment of existing buildings.

In order to understand the hydrological changes of groundwater in karst areas, the Xiangxi River Basin in the western Hubei karst area was selected for water sample collection and monitoring. A carbon flux estimation model was established by combining support vector machine regression and sparrow search algorithm to achieve groundwater carbon flux estimation. The results show that the average range of dissolved oxygen in karst groundwater is 9.65~10.79mg · L−1, with an overall high content. The anions in groundwater are mainly HCO3−, and the cations are mainly Ca2+. The pH value of the water sample is mainly between 7.28 and 8.16, showing weak alkalinity. The mineralization value of the water sample is distributed between 81.36~323.41mg/L, and the groundwater hydrochemical types are mainly HCO3 Ca type and HCO3 Ca · Mg type. The carbon flux estimation model for karst groundwater has reduced the regression error value by 43.6% compared to the unimproved support vector machine. This indicates that improving the model can improve the accuracy of carbon flux estimation. By analyzing the hydrochemical characteristics, the resource characteristics, environmental effects, and ecological significance of karst groundwater can be understood, providing scientific basis for management and decision-making in related fields..

Abstract Solar photovoltaic (PV) power generation is susceptible to environmental factors, and redundant features can disrupt prediction accuracy. To achieve rapid and accurate online prediction, we propose a method that combines Principal Component Analysis (PCA) with a multi-strategy improved Squirrel Search Algorithm (SSA) to optimize Support Vector Machine (MISSA-SVM) for prediction. Initially, to mitigate the impact of redundant features on prediction accuracy, KPCA is employed for feature dimensionality reduction. Subsequently, SVM is suggested as the foundational algorithm for constructing the prediction model. Furthermore, to address the influence of hyperparameter selection on model performance, SSA is introduced for optimizing SVM hyperparameters, with the aim of establishing the optimal prediction model. Moreover, to enhance solution efficiency and accuracy, a multi-strategy approach termed MISSA is proposed, which integrates Population Initialization based on the Tent map, Nonlinear Predator Presence Probability, Chaotic-based Dynamic Opposition-based Learning, and Selection Strategy, to refine SSA. Finally, through case studies, the performance of MISSA optimization is assessed using challenging CEC2021 test functions, demonstrating its high optimization performance, stability, and significance. Subsequently, the performance of the prediction model is validated using two datasets, showcasing that the proposed prediction method achieves high accuracy and robust prediction stability.

This study proposes a deep-learning driven methodology for the analysis of mushroom moisture content (MC) datasets acquired using a novel portable hyperspectral imaging (HSI) system. One-dimensional convolutional neural network (1D-CNN) was developed and validated to process the raw HSI data of white button mushrooms (Agaricus bisporus) for MC prediction. For comparison purposes, state-of-the-art machine learning algorithms, i.e., support vector machine regression (SVMR) and partial least squares regression (PLSR) were also investigated for the model development based on five spectra pre-processed methods using two different lighting systems i.e., enhanced light-emitting diode (LED) and tungsten halogen (TH). Overall, the predictive models based on the HSI data acquired using the LED lights (Rp2 of 0.977, RMSEP of 4.27 %, and RPDp of 6.89) exhibited better performances on the prediction of mushroom MC than those models developed using the TH-HSI data (Rp2 of 0.868, RMSEP of 10.69 %, and RPDp of 2.75). Specifically, the 1D-CNN model based on the raw LED-HSI data (Rp2 of 0.972, RMSEP of 4.70 % and RPDp of 6.29) and the SVMR model based on multiplicative scatter correction (MSC) pretreated LED-HSI data (Rp2 of 0.977, RMSEP of 4.27 %, and RPDp of 6.89) achieved exceptional predictive accuracy for mushroom MC. This finding highlights the effectiveness of the 1D-CNN model in the analysis of HSI data, which performed similarly to the SVMR model without requiring complex data preprocessing steps. In addition, the feasibility of employing a novel LED illumination system in conjunction with a portable HSI camera for the precise MC monitoring of button mushrooms was demonstrated in the present work.

Monitoring crop height during different growth stages provides farmers with valuable information important for managing and improving expected yields. The use of synthetic aperture radar Sentinel-1 (S-1) and Optical Sentinel-2 (S-2) satellites provides useful datasets that can assist in monitoring crop development. However, studies exploring synergetic use of SAR S-1 and optical S-2 satellite data for monitoring crop biophysical parameters are limited. We utilized a time-series of monthly S-1 satellite data independently and then used S-1 and S-2 satellite data synergistically to model wheat-crop height in this study. The polarization backscatter bands, S-1 polarization indices, and S-2 spectral indices were computed from the datasets. Optimized Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), Decision Tree Regression (DTR), and Neural Network Regression (NNR) machine-learning algorithms were applied. The findings show that RFR (R2 = 0.56, RMSE = 21.01 cm) and SVM (R2 = 0.58, RMSE = 20.41 cm) produce a low modeling accuracy for crop height estimation with S-1 SAR data. The S-1 and S-2 satellite data fusion experiment had an improvement in accuracy with the RFR (R2 = 0.93 and RMSE = 8.53 cm) model outperforming the SVM (R2 = 0.91 and RMSE = 9.20 cm) and other models. Normalized polarization (Pol) and the radar vegetation index (RVI_S1) were important predictor variables for crop height retrieval compared to other variables with S-1 and S-2 data fusion as input features. The SAR ratio index (SAR RI 2) had a strong positive and significant correlation (r = 0.94; p < 0.05) with crop height amongst the predictor variables. The spatial distribution maps generated in this study show the viability of data fusion to produce accurate crop height variability maps with machine-learning algorithms. These results demonstrate that both RFR and SVM can be used to quantify crop height during the growing stages. Furthermore, findings show that data fusion improves model performance significantly. The framework from this study can be used as a tool to retrieve other wheat biophysical variables and support decision making for different crops. [ABSTRACT FROM AUTHOR]

Monitoring crop height during different growth stages provides farmers with valuable information important for managing and improving expected yields. The use of synthetic aperture radar Sentinel-1 (S-1) and Optical Sentinel-2 (S-2) satellites provides useful datasets that can assist in monitoring crop development. However, studies exploring synergetic use of SAR S-1 and optical S-2 satellite data for monitoring crop biophysical parameters are limited. We utilized a time-series of monthly S-1 satellite data independently and then used S-1 and S-2 satellite data synergistically to model wheat-crop height in this study. The polarization backscatter bands, S-1 polarization indices, and S-2 spectral indices were computed from the datasets. Optimized Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), Decision Tree Regression (DTR), and Neural Network Regression (NNR) machine-learning algorithms were applied. The findings show that RFR (R2 = 0.56, RMSE = 21.01 cm) and SVM (R2 = 0.58, RMSE = 20.41 cm) produce a low modeling accuracy for crop height estimation with S-1 SAR data. The S-1 and S-2 satellite data fusion experiment had an improvement in accuracy with the RFR (R2 = 0.93 and RMSE = 8.53 cm) model outperforming the SVM (R2 = 0.91 and RMSE = 9.20 cm) and other models. Normalized polarization (Pol) and the radar vegetation index (RVI_S1) were important predictor variables for crop height retrieval compared to other variables with S-1 and S-2 data fusion as input features. The SAR ratio index (SAR RI 2) had a strong positive and significant correlation (r = 0.94; p < 0.05) with crop height amongst the predictor variables. The spatial distribution maps generated in this study show the viability of data fusion to produce accurate crop height variability maps with machine-learning algorithms. These results demonstrate that both RFR and SVM can be used to quantify crop height during the growing stages. Furthermore, findings show that data fusion improves model performance significantly. The framework from this study can be used as a tool to retrieve other wheat biophysical variables and support decision making for different crops.

Based on SBAS-InSAR technology and machine learning knowledge, the monitoring and prediction of surface settlement in high-fill areas have important guiding significance for construction, maintenance, and operation of engineering projects. This study takes the Chongqing Donggang Container Terminal as the research object, and utilizes 31 scenes of Sentinel-1A data from 2018 to 2019. The surface subsidence data of the area is obtained by SBAS-InSAR technology, and the internal and external accuracy is evaluated. The topography characteristics of the prone areas of surface subsidence were analyzed through an information quantity model to select prediction points. Grey Relational Analysis （GRA） was used to calculate the grey correlation degree between dynamic influencing factors and subsidence. Principal component analysis was used to extract principal components from influencing factors, and training and testing sets were constructed. PSO-SVR prediction model was used to predict the testing set data. To verify the reliability and superiority of the model in subsidence prediction in high-fill areas, the ARIMA model was used as a comparative model, and the prediction results of the PSO-SVR model and the ARIMA model were compared with the testing set. The results show that the prediction accuracy of the PSO-SVR model is better than that of the ARIMA model, and it has better practicality in predicting surface subsidence in high-fill areas.

Miniaturized near-infrared (NIR) spectrometers are revolutionizing the agri-food industry thanks to their compact size and ultra-fast analysis capabilities. This work compares the analytical performance of a handheld NIR spectrometer and a benchtop FT-NIR for the determination of several parameters, namely, pH, electrical conductivity (EC25), C/N ratio, and organic matter as LOI (loss-on-ignition) in compost. Samples were collected at different stages of maturity from a full-scale facility that processes olive mill semi-solid residue together with olive tree pruning residue and animal manure. Using an FT-NIR spectrometer, satisfactory predictions (RPD > 2.0) were obtained with both partial least squares (PLS) and support vector machine (SVM) regression, SVM clearly being superior in the case of pH (RMSEP = 0.26; RPD = 3.8). The superior performance of the FT-NIR spectrometer in comparison with the handheld spectrometer was essentially due to the extended spectral range, especially for pH. In general, when analyzing intact samples with the miniaturized spectrometer, sample rotation decreased RMSEP values (~20%). Nevertheless, a fast and simple assessment of compost quality with reasonable prediction performance can also be achieved on intact samples by averaging static measurements acquired at different sample positions.

Grasslands play a vital role in the global ecosystem. Efficient and reproducible methods for estimating the grassland aboveground biomass (AGB) are crucial for understanding grassland growth, promoting sustainable development, and assessing the carbon cycle. Currently, the available methods are limited by their computational inefficiency, model transfer, and sampling scale. Therefore, in this study, the estimation of grassland AGB over a large area was achieved by coupling the PROSAIL model with the support vector machine regression (SVR) method. The ill-posed inverse problem of the PROSAIL model was mitigated through kernel-based regularization using the SVR model. The Zoigê Plateau was used as the case study area, and the results demonstrated that the estimated biomass accurately reproduced the reference AGB map generated by zooming in on on-site measurements (R2 = 0.64, RMSE = 43.52 g/m2, RRMSE = 15.13%). The estimated AGB map also maintained a high fitting accuracy with field sampling data (R2 = 0.69, RMSE = 44.07 g/m2, RRMSE = 14.21%). Further, the generated time-series profiles of grass AGB for 2022 were consistent with the trends in local grass growth dynamics. The proposed method combines the advantages of the PROSAIL model and the regression algorithm, reduces the dependence on field sampling data, improves the universality and repeatability of grassland AGB estimation, and provides an efficient approach for grassland ecosystem construction and planning. [ABSTRACT FROM AUTHOR]

For the problem of geometric error prediction of CNC machine tools, an improved hybrid grey wolf optimization (IHGWO) algorithm is proposed to optimize the geometric error modeling scheme of the support vector regression machine (SVR). The predicted and measured values of the geometric error are combined to construct the fitness function. In IHGWO, principles of particle swarm optimization (PSO) algorithm and dimension learning-based hunting (DLH) search strategies are introduced while retaining the excellent grey wolf position of the basic grey wolf optimization (GWO) algorithm. IHGWO algorithm uses Euclidean distance to construct the neighborhood of individual grey wolves, which enhances the ability to communicate between individual grey wolves and improves the convergence speed and accuracy of the algorithm. Predictive performance of SVR models using sum squared residual to quantify geometric error. Based on the screw theory, space models of geometric errors of CNC machine tools are established and combined with SVR models of geometric errors for compensation. Empirical evidence proves that the proposed method surpasses current error modeling methods in terms of precision and efficiency, as evidenced by a minimum reduction of 9% in circular trajectory error and a reduction to two overruns in S-shaped test pieces after error compensation. This research contributes to the field of CNC machine tool error modeling and has practical implications for manufacturing industries. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Geological Hazard & Control is the property of China Institute of Geological Environmental Monitoring (CIGEM) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Seawalls are critical defence infrastructures in coastal zones that protect hinterland areas from storm surges, wave overtopping and soil erosion hazards. Scouring at the toe of sea defences, caused by wave-induced accretion and erosion of bed material imposes a significant threat to the structural integrity of coastal infrastructures. Accurate prediction of scour depths is essential for appropriate and efficient design and maintenance of coastal structures, which serve to mitigate risks of structural failure through toe scouring. However, limited guidance and predictive tools are available for estimating toe scouring at sloping structures. In recent years, Artificial Intelligence and Machine Learning (ML) algorithms have gained interest, and although they underpin robust predictive models for many coastal engineering applications, such models have yet to be applied to scour prediction. Here we develop and present ML-based models for predicting toe scour depths at sloping seawall. Four ML algorithms, namely, Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Artificial Neural Networks (ANNs), and Support Vector Machine Regression (SVMR) are utilised. Comprehensive physical modelling measurement data is utilised to develop and validate the predictive models. A Novel framework for feature selection, feature importance, and hyperparameter tuning algorithms are adopted for pre- and post-processing steps of ML-based models. In-depth statistical analyses are proposed to evaluate the predictive performance of the proposed models. The results indicate a minimum of 80% prediction accuracy across all the algorithms tested in this study and overall, the SVMR produced the most accurate predictions with a Coefficient of Determination (r2) of 0.74 and a Mean Absolute Error (MAE) value of 0.17. The SVMR algorithm also offered most computationally efficient performance among the algorithms tested. The methodological framework proposed in this study can be applied to scouring datasets for rapid assessment of scour at coastal defence structures, facilitating model-informed decision-making. [ABSTRACT FROM AUTHOR]

Forecasting stock market movements is a challenging task from the practitioners’ point of view. We explore how model selection via the least absolute shrinkage and selection operator (LASSO) approach can be better used to forecast stock closing prices using real-world datasets of daily stock closing prices of three major international airlines. Combining the LASSO method with multiple external data sources in our model leads to a robust and efficient method to predict stock behavior. We also compare our approach with ridge, tree, and support vector machine regressions, as well as neural network approaches to model the data. We include lags of each external variable and response variable in the model, resulting in a total of 870 predictor variables. The empirical results indicate that the LASSO-fitted model is the most effective when compared to other approaches we consider. The results show that the closing price of an airline stock is affected by its closing price for the previous days and those of other types of airlines and is significantly correlated with the Shanghai Composite Index for the previous day and 3 days prior. Other influencing factors include the positive impact of the Shanghai Composite Index daily share volume, the negative impact of loan interest rates, the amount of highway passenger and railway freight turnover, etc.

To reduce the disasters caused by slope instability, this paper proposes a new machine learning (ML) model for slope stability prediction. This improved SVR model uses support vector machine regression (SVR) as the basic prediction tool and the grid search method with 5-fold cross-validation to optimize the hyperparameters to improve the prediction performance. Six features, namely, unit weight, cohesion, friction angle, slope angle, slope height, and pore pressure ratio, were taken as the input of the model, and the factor of safety was taken as the model output. Four statistical indicators, namely, the coefficient of determination (R2), mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE), were introduced to assess the generalization performance of the model. Finally, the feature importance score of the features was clarified by calculating the importance of the six features and visualizing them. The results show that the model can well describe the nonlinear relationship between features and the factor of safety. The R2, MAPE, MAE, and RMSE of the testing dataset were 0.901, 7.41%, 0.082, and 0.133, respectively. Compared with other ML models, the improved SVR model had a better effect. The most sensitive feature was unit weight.

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Seawalls are critical defence infrastructures in coastal zones that protect hinterland areas from storm surges, wave overtopping and soil erosion hazards. Scouring at the toe of sea defences, caused by wave-induced accretion and erosion of bed material imposes a significant threat to the structural integrity of coastal infrastructures. Accurate prediction of scour depths is essential for appropriate and efficient design and maintenance of coastal structures, which serve to mitigate risks of structural failure through toe scouring. However, limited guidance and predictive tools are available for estimating toe scouring at sloping structures. In recent years, Artificial Intelligence and Machine Learning (ML) algorithms have gained interest, and although they underpin robust predictive models for many coastal engineering applications, such models have yet to be applied to scour prediction. Here we develop and present ML-based models for predicting toe scour depths at sloping seawall. Four ML algorithms, namely, Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Artificial Neural Networks (ANNs), and Support Vector Machine Regression (SVMR) are utilised. Comprehensive physical modelling measurement data is utilised to develop and validate the predictive models. A Novel framework for feature selection, feature importance, and hyperparameter tuning algorithms are adopted for pre- and post-processing steps of ML-based models. In-depth statistical analyses are proposed to evaluate the predictive performance of the proposed models. The results indicate a minimum of 80% prediction accuracy across all the algorithms tested in this study and overall, the SVMR produced the most accurate predictions with a Coefficient of Determination (r2) of 0.74 and a Mean Absolute Error (MAE) value of 0.17. The SVMR algorithm also offered most computationally efficient performance among the algorithms tested. The methodological framework proposed in this study can be applied to scouring datasets for rapid assessment of scour at coastal defence structures, facilitating model-informed decision-making.

Financial fraud detection is a challenging problem due to four primary reasons: the constantly changing fraudulent behavior, the lack of a mechanism to track fraud data, the specific limitations of available detection techniques (such as machine learning algorithms), and the highly dispersed financial fraud dataset. Thus, it can be declared that teaching algorithms are complex. The current study used machine learning techniques, including support vector machine regression and boosted regression tree, to detect financial fraud in the Iranian stock market. The findings indicated that the boosted regression tree machine model has the lowest RMSE. Furthermore, concerned with the sensitivity value of the models, the boosted regression tree model has the highest sensitivity in the sense that they had correctly detected the absence of financial fraud Tehran Stock Exchange market the Tehran Stock Exchange market. The boosted regression tree has the highest kappa coefficient indicating the appropriate performance of this model compared to other models used in the research. [ABSTRACT FROM AUTHOR]

Due to the influences of the storage environment, water content change, particle settlement, natural loss, and other factors, the distribution density of wheat and the volume of grain pile in the storage process are gradually changed so that the single weight calculation method cannot objectively evaluate the storage quantity of wheat and also causes difficulties to the regular inspection of the quantity of wheat stock. To meet the practical needs of wheat inventory monitoring, a wheat inventory monitoring method based on inventory measurement and the support vector machine regression (SVR) prediction model is proposed. By collecting the working papers for the physical inspection of wheat in grain warehouses in Shanxi province, Hebei province, Henan province, Jiangsu province, and other places, the storage time, storage weight, storage moisture content, measured moisture content, measured volume weight, measured net volume, and measured weight for inspection were selected as training samples for the SVR prediction model, and kernel function selection and parameter optimization were carried out. We developed an optimal prediction model for the amount of wheat in the grain depots. In the actual grain store measurement process, the net volume of wheat in the current grain store was obtained by a laser volumetric measuring apparatus, the actual bulk density of wheat was sampled, and the actual moisture content of wheat was measured by sampling. The three samples, their storage time, their storage moisture content, and their storage weight were fed into the trained SVR prediction model as new samples, and the predicted weight of the wheat in the current grain store was obtained from the output. The error rate calculation procedure was introduced to achieve an anomalous judgment error rate for grain depots. The experimental results showed that the SVR prediction model based on the linear kernel function had a very low mean squared error and high determination coefficient, and the average prediction accuracy of the grain stock error rate reached 93.2 percent, which can meet the requirements of wheat quantity monitoring in grain warehouses. [ABSTRACT FROM AUTHOR]

To improve the accuracy of soil moisture estimation in Vertisols, this study took the Vertisol in Xiping County, Henan Province, China, as its research object and conducted hyperspectral measurement in the laboratory by configuring soil samples with different moisture contents after implementing smoothing (SR), logarithm of the inverse[LOG (1/R) ], first-order differentiation (FD), multiple scattering correction (MSC), and continuum removal (CR) spectral transformation processes on the soil sample hyperspectral data. The best feature bands were identified by combining the successive projection algorithm (SPA) with the machine learning methods of partial least squares regression (PLSR) and support vector machine regression (SVR) and stacking (Stacking) integrated learning methods were used to construct the soil water content inversion model. The results showed that the information related to soil water content was most enhanced in the MSC-transformed spectra. The SPA algorithm was able to downscale and extract feature information from the water content spectral data of the Vertisol. The Stacking integrated model, which integrated PLSR and SVR after MSC transformation based on the reflection spectra, had the highest coefficient of determination (R²=0.963) and the lowest root mean square error (RMSE=1.7) . This study indicates that the Stacking integrated learning model is the best inversion model for Vertisol moisture content. It effectively improves the accuracy and generalization ability of the model. [ABSTRACT FROM AUTHOR]

Timely crop yield information is needed for agricultural land management and food security. We investigated using remote sensing data from the Earth observation mission Sentinel-2 to monitor the crop phenology and predict the crop yield of sunflowers at the field scale. Ten sunflower fields in Mezőhegyes, southeastern Hungary, were monitored in 2021, and the crop yield was measured by a combine harvester. Images from Sentinel-2 were collected throughout the monitoring period, and vegetation indices (VIs) were extracted to monitor the crop growth. Multiple linear regression and two different machine learning approaches were applied to predicting the crop yield, and the best-performing one was selected for further analysis. The results were as follows. The VIs showed the highest correlation with the crop yield (R > 0.6) during the inflorescence emergence stage. The most suitable time for predicting the crop yield was 86–116 days after sowing. Random forest regression (RFR) was the best machine learning approach for predicting field-scale variability of the crop yield (R2 ∼ 0.6 and RMSE 0.284–0.473 t/ha). Our results can be used to develop a timely and robust prediction method for sunflower crop yields at the field scale to support decision-making by policymakers regarding food security.

Brassica rapa, commonly known as the rapeseed plant, is globally recognized for its nutrient-rich composition and oil-packed seeds, earning its distinction as a substantial oil-seed crop. The seed quality, particularly the germination rate, is instrumental in guaranteeing a high-yield rapeseed crop. Given this, the accurate, quantitative determination and selection of germination rates in seed batches prior to sowing is of paramount importance. However, conventional germination tests, employed to determine the average germination rate of seed batches, are marred by substantial time and cost inefficiencies. This study proposes the use of near-infrared spectral analysis as a proficient, non-invasive approach for assessing germination rates in rapeseed seed batches. The research involved artificial aging of seeds procured from a variety of rapeseed strains, resulting in 228 batches with a broad germination rate spectrum of 15.73% to 99.13%. We recorded near-infrared diffuse reflectance spectra and applied a range of strategies for spectral data preprocessing and feature variable selection. Furthermore, we leveraged support vector regression (SVR) modeling to augment the detection methodology. SVR training and detection were conducted using MATLAB, with selected feature wavelengths undergoing rigorous scrutiny and discussion. The results indicated that employing Savitzky–Golay convolution smoothing for spectral preprocessing, along with Synergy interval Partial Least Squares (SiPLS) in conjunction with Random Frog (RF) for the selection of 50 feature wavelength points, yielded optimal germination rate prediction performance within the SVR model. The coefficients of determination (R2c) for the training set and (R2p) for the testing set were observed to be 0.8559 and 0.8386, respectively, while the Root Mean Square Errors of Calibration (RMSEC) and Prediction (RMSEP) were calculated to be 13.76% and 17.04%. The mechanism of detecting seed vigor through near-infrared spectroscopy was analyzed based on joint variable screening and sensitive variable traceability. Consequently, the SG–SiPLS–RF–SVR model demonstrates its effectiveness in predicting the average germination rate of seed batches, offering a rapid, non-invasive detection method that can be universally applied to various rapeseed strains, thus significantly improving seed production efficiency. [ABSTRACT FROM AUTHOR]

At present, coal accounts for more than 56% of China's primary energy consumption and will continue to dominate for a long time in the future. With the continuous expansion of the mining intensity and scale of Jurassic coal resources in Northwestern China, the problem of mine roof water disasters is becoming increasingly serious. The degree of harm is related to the hydrogeological structure of the overlying strata of the coal seam. Reasonable and effective prediction and evaluation of the water abundance of the coal seam roof aquifer is conducive to making scientific decisions on the prevention and control of roof water disasters, so as to achieve safe mining. In order to solve the problem of water abundance evaluation in mining areas lacking hydrological holes, taking the Hongliulin coal mine in Shennan mining area as an example, four main control factors for water abundance were selected: sandstone thickness, core recovery ratio, brittle rock thickness ratio, and flushing fluid consumption. Combined with unit water inflow and multiple factor comprehensive analysis, a back propagation (BP) artificial neural network and support vector machine regression (SVR) were introduced into water abundance evaluation. The reciprocal variance method was used to predict the measured unit water inflow. Finally, according to the "Detailed Rules for Coal Mine Water Prevention and Control", the water abundance of aquifers was classified to verify the accuracy of the model and partition the water abundance of the study area. The results indicate that, based on the predicted results of unit water inflow, out of 37 borehole data, 22 weak water abundance holes and 15 medium water abundance holes were evaluated correctly, verifying their applicability. The study area was generally weak in water abundance, with two grades of medium and weak. The medium water abundance area was mainly located in the north and south of the study area, and the weak water abundance area was mainly located in the east and west. It can be seen that this evaluation model has certain applicability for evaluating the water abundance of coal seam roofs. It is of great significance, especially for the evaluation of water abundance in mining areas where hydrological holes are lacking. [ABSTRACT FROM AUTHOR]

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Water Conservancy Science & Techonlogy & Economy is the property of Water Conservancy Science & Technology & Economy Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Iranian Journal of Soil & Water Researches (IJSWR) is the property of University of Tehran and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Wave and water depth were measured with an instrumented tripod in the Yellow River Delta from 9 December 2014 to 29 April 2015. Concurrent wind data were also collected from a nearby wind station. A high‐precision model for predicting local significant wave height (Hs) with wind speed (vw) is constructed using an improved data‐driven approach. The proposed model realized high accuracy as it solves the problem that the Hs falls too fast during the wind‐decreasing periods. It was tackled by considering the remaining influence of historical vw on the present Hs via incorporating a memory curve of the past wind effect. This innovative approach significantly improves the prediction (R2 from 0.60 to 0.83). The winds in the past 24 hr still left an influence on the waves at the observation site although the influence decreases with time. Physically, it is an implicit but simpler consideration of wind fetch/duration. Further data modeling experiments indicated that the decisive factor for the Hs at the site is the wind speed. Wind directions slightly improve the prediction, indicating that waves are slightly affected by the underwater seabed slope along different wind directions, and northwest winds cause the strongest waves at the site. Adding atmospheric pressure or water depth even reduces the accuracy, which indicated that storm surges and wave deformations under different tide levels have a weak impact on Hs. The proposed local wave model can be easily constructed with available wind and wave data, making it expandable to other regions dominated by wind waves. Plain Language Summary: Wave conditions in the ocean are essential for engineering applications. Due to the influence of geographical environment, topography, and geomorphology, the wave conditions of a certain station in the ocean often have certain rules to follow. This paper attempts to establish a high‐precision prediction model for local significant wave height of wind waves through long‐term in situ observations and machine learning methods. The introduction of a "memory curve" into the conventional Support Vector Regression model significantly improved the prediction accuracy. The physical meaning of the "memory curve" corresponds to the residual impact of historical wind speed, which is the implicit expression of the effects of wind duration/fetch in wave development. The proposed method can be easily applied to other sea areas dominated by wind waves. Key Points: A high‐accuracy local wind wave height model is built based on wind speedA memory curve is incorporated into Support Vector Regression to improve the prediction significantlyInfluencing factors of wave height are explored through data modeling experiments [ABSTRACT FROM AUTHOR]

Little work has been carried out to predict the comfort of aircraft seats, a component in close contact with the human body during travel. In order to more accurately predict the nonlinear and complex relationship between subjective and objective evaluations of comfort, this paper proposes a prediction method based on the Improved Particle Swarm Algorithm (IPSO) and optimized Support Vector Machine Regression (SVR). Focusing on the problems of the too-fast convergence and low accuracy of the traditional particle swarm algorithm (PSO), the improved particle swarm algorithm (IPSO) is obtained by linearly decreasing the dynamic adjustments of inertia weight ω , self-learning factor c 1 , and social factor c 2 ; then, the penalty parameter C and kernel function parameter σ of SVR are optimized by the IPSO algorithm, and the comfort prediction of IPSO-SVR is established. The prediction accuracy of IPSO-SVR was 94.00%, the root mean square error RMSE was 0.37, the mean absolute value error MAE was 0.32, and the goodness of fit R2 was 0.92. The results show that the optimized IPSO-SVR prediction model can more accurately predict seat comfort under different angles and backrest tilt angles and can provide reference and research value for related industries. The results show that the optimized nonlinear prediction model of IPSO-SVR has higher accuracy, and its prediction method is feasible and generalizable, meaning it can provide a reliable basis for the prediction of seat comfort under different angles and backrest inclinations, as well as providing reference and research value for related industries. [ABSTRACT FROM AUTHOR]

This paper introduces novel techniques based on Machine Learning (ML) algorithms for a Photovoltaic integrated Shunt Active Power Filter performance improvement. The first goal is to design an efficient maximum power point tracking MPPT strategy in order to harness the largest amount of energy possible. Thereby, a new hybrid Support Vector Machine Regression Perturb and Observe (SVM regression-P&O) algorithm is proposed. The SVM block improves the tracking speed by predicting an initial duty cycle, whereas a small fixed-step P&O algorithm ensures a high MPPT accuracy. The second purpose is to upgrade harmonics detection by exploiting the characteristics of intelligent learning of Adaline combined with ML algorithm. Therefore, a novel SVM regression-Adaline PQ strategy is designed. The SVM block generates the predicted initial weights of Adaline, thus ensuring fast identification of the DC active power component. In addition, the ability of this design to work with a small learning rate parameter allows an accurate harmonics extraction in contrast with the Adaptive Adaline technique where the performances are highly dependent on the chosen learning rate parameter. A comparative analysis of various ML models are carried out in order to get the best output prediction for each SVM regression block. Simulations have been performed to confirm the supremacy of the new strategies over intelligent and classical techniques. Finding exhibits a significant decrease of PV energy losses (up to 99%), a minor overshoot with an impressively decrease of the harmonics extraction’s response time (up to 98.8%), and a PVSAPF power quality enhancement under online intermittent weather conditions and variable nonlinear load.

Copyright of Chinese Journal of Inorganic Analytical Chemistry / Zhongguo Wuji Fenxi Huaxue is the property of Beijing Research Institute of Mining & Metallurgy Technology Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Grasslands play a vital role in the global ecosystem. Efficient and reproducible methods for estimating the grassland aboveground biomass (AGB) are crucial for understanding grassland growth, promoting sustainable development, and assessing the carbon cycle. Currently, the available methods are limited by their computational inefficiency, model transfer, and sampling scale. Therefore, in this study, the estimation of grassland AGB over a large area was achieved by coupling the PROSAIL model with the support vector machine regression (SVR) method. The ill-posed inverse problem of the PROSAIL model was mitigated through kernel-based regularization using the SVR model. The Zoigê Plateau was used as the case study area, and the results demonstrated that the estimated biomass accurately reproduced the reference AGB map generated by zooming in on on-site measurements (R2 = 0.64, RMSE = 43.52 g/m2, RRMSE = 15.13%). The estimated AGB map also maintained a high fitting accuracy with field sampling data (R2 = 0.69, RMSE = 44.07 g/m2, RRMSE = 14.21%). Further, the generated time-series profiles of grass AGB for 2022 were consistent with the trends in local grass growth dynamics. The proposed method combines the advantages of the PROSAIL model and the regression algorithm, reduces the dependence on field sampling data, improves the universality and repeatability of grassland AGB estimation, and provides an efficient approach for grassland ecosystem construction and planning.

In order to predict the risk level of tunnel collapse and reduce the disaster accidents caused by tunnel collapse, a tunnel collapse risk prediction model based on artificial bee colony(ABC) optimization support vector machine regression ( SVR) was established. Firstly, considering engineering geology, hydrometeorology, design factors and construction factors, 13 main influencing factors were selected to establish the tunnel collapse risk index system. Then, the artificial bee colony algorithm was introduced to optimize the kernel parameter C and penalty parameter g of SVR, the defect of low stability of traditional SVR was solved, and the accuracy of the model was improved. In order to verify the performance of the model, the evaluation parameters of correlation coefficient (R²), mean square error (MSE) and root mean square error (RMSE) were compared and analyzed. Finally, taking a water supply project in northern Xinjiang as the research object, the tunnel collapse risk test samples were predicted, and the ABC-SVR, PSO-SVR, GA-SVR and SVR models were compared and analyzed respectively. The results show that the prediction results of ABC-SVR are 100%, PSO-SVR are 83.3%, GA-SVR and SVR are 66.67% . The prediction results of ABC-SVR are more consistent with the actual engineering results, which can provide a scientific decision-making basis for tunnel collapse risk assessment. [ABSTRACT FROM AUTHOR]