Optimal Prediction Accuracy Research Articles

Probabilistic Tide Calculation for Multi-Wind Farm Distribution Network Based on CATTSTS Model

Traditional probabilistic tide calculation methods often fall short in capturing the dynamic nature of these systems, leading to discrepancies between calculated and actual data. To address this, a dynamic probabilistic tide calculation model is proposed in this study, incorporating a wind farm probabilistic model enhanced by the CATTSTS model. This integration aims to optimize prediction accuracy by improving feature focusing and data fitting, thereby reducing prediction errors and providing more reliable data for power system adjustments and optimizations. Experimental results demonstrate that the model's predictions for wind speed series in multi-wind farm distribution networks closely match actual conditions, underscoring its practical applicability and reliability in real-world settings. Furthermore, the study highlights the impact of spatial and temporal correlations on model performance. This research contributes to advancing the management of renewable energy integration in power systems, offering insights for more informed decision-making and operational efficiency.

Analysis and Box Office Prediction of Chinese High Box Office Films- Empirical Analysis Based on the Top250 Movies on Maoyan.com

This project focuses on analyzing and predicting box-office performance using data from Maoyan.com. Several key factors, including release year and month, ticket price, movie duration, and the proportion of positive reviews, are examined for their influence on box-office revenue. We begin by employing simple linear regression to investigate the relationship between Maoyan.com ratings for the top 250 movies and their box-office performance. Additionally, we construct an Autoregressive Distributed Lag (ARDL) model to assess the impact of these factors over time. The model parameters are estimated using the least squares regression method, and the Mean Absolute Percentage Error (MAPE) is minimized to achieve optimal prediction accuracy. Extensive data cleaning and preprocessing ensure the reliability of our analysis. The findings from this study provide valuable insights into the determinants of box-office success and offer a robust predictive model for future revenue estimation.

Predicting 24-hour intraocular pressure peaks and averages with machine learning.

Predicting 24-hour peak and average intraocular pressure (IOP) is essential for the diagnosis and management of glaucoma. This study aimed to develop and assess a machine learning model for predicting 24-hour peak and average IOP, leveraging advanced techniques to enhance prediction accuracy. We also aimed to identify relevant features and provide insights into the prediction results to better inform clinical practice. In this retrospective study, electronic medical records from January 2014 to May 2024 were analyzed, incorporating 24-hour IOP monitoring data and patient characteristics. Predictive models based on five machine learning algorithms were trained and evaluated. Five time points (10:00 AM, 12:00 PM, 2:00 PM, 4:00 PM, and 6:00 PM) were tested to optimize prediction accuracy using their combinations. The model with the highest performance was selected, and feature importance was assessed using Shapley Additive Explanations. This study included data from 517 patients (1,034 eyes). For predicting 24-hour peak IOP, the Random Forest Regression (RFR) model utilizing IOP values at 10:00 AM, 12:00 PM, 2:00 PM, and 4:00 PM achieved optimal performance: MSE 5.248, RMSE 2.291, MAE 1.694, and R2 0.823. For predicting 24-hour average IOP, the RFR model using IOP values at 10:00 AM, 12:00 PM, 4:00 PM, and 6:00 PM performed best: MSE 1.374, RMSE 1.172, MAE 0.869, and R2 0.918. The study developed machine learning models that predict 24-hour peak and average IOP. Specific time point combinations and the RFR algorithm were identified, which improved the accuracy of predicting 24-hour peak and average intraocular pressure. These findings provide the potential for more effective management and treatment strategies for glaucoma patients.

Machine Learning Models Can Define Clinically Relevant Bone Density Subgroups based on Patient Specific Calibrated CT Scans in Patients Undergoing Reverse Shoulder Arthroplasty

Reduced bone density is recognized as a predictor for potential complications in reverse shoulder arthroplasty (RSA). While humeral and glenoid planning based on preoperative computed tomography (CT) scans assist in implant selection and position, reproducible methods for quantifying the patients' bone density are currently not available. The purpose of this study was to perform bone density analyses including patient specific calibration in an RSA cohort based on preoperative CT imaging. It was hypothesized that preoperative CT bone density measures would provide objective quantification of the patients' humeral bone quality. This study consisted of three parts, (1) analysis of a patient-specific calibration method in cadaveric CT scans, (2) retrospective application in a clinical RSA cohort, and (3) clustering and classification with machine learning models. Forty cadaveric shoulders were scanned in a clinical CT and compared regarding calibration with density phantoms, air muscle, and fat (patient-specific) or standard Hounsfield unit. Post-scan patient-specific calibration was used to improve the extraction of three-dimensional regions of interest for retrospective bone density analysis in a clinical RSA cohort (n=345). Machine learning models were used to improve the clustering (Hierarchical Ward) and classification (Support Vector Machine (SVM)) of low bone densities in the respective patients. The patient-specific calibration method demonstrated improved accuracy with excellent intraclass correlation coefficients (ICC) for cylindrical cancellous bone densities (ICC>0.75). Clustering partitioned the training data set into a high-density subgroup consisting of 96 patients and a low-density subgroup consisting of 146 patients, showing significant differences between these groups. The SVM showed optimized prediction accuracy of low and high bone densities compared to conventional statistics in the training (accuracy=91.2%; AUC=0.967) and testing (accuracy=90.5 %; AUC=0.958) data set. Preoperative CT scans can be used to quantify the proximal humeral bone quality in patients undergoing RSA. The use of machine learning models and patient-specific calibration on bone mineral density demonstrated that multiple 3D bone density scores improved the accuracy of objective preoperative bone quality assessment. The trained model could provide preoperative information to surgeons treating patients with potentially poor bone quality.

Predicting Particle Size and Soil Organic Carbon of Soil Profiles Using VIS-NIR-SWIR Hyperspectral Imaging and Machine Learning Models

Modeling spectral reflectance data using machine learning algorithms presents a promising approach for estimating soil attributes. Nevertheless, a comprehensive investigation of the most effective models, parameters, wavelengths, and data acquisition techniques is essential to ensure optimal predictive accuracy. This work aimed to (a) explore the potential of the soil spectral signature obtained in different spectral bands (VIS-NIR, SWIR, and VIS-NIR-SWIR) and, by using hyperspectral imaging and non-imaging sensors, in the predictive modeling of soil attributes; and (b) analyze the accuracy of different ML models in predicting particle size and soil organic carbon (SOC) applied to the spectral signature of different spectral bands. Six soil monoliths, located in the central north region of Parana, Brazil, were collected and scanned via hyperspectral cameras (VIS-NIR camera and SWIR camera) and spectroradiometer (VIS-NIR-SWIR) in the laboratory. The spectral signature of the soils was analyzed and subsequently applied to ML models to predict particle size and SOC. Each set of data obtained by the different sensors was evaluated separately. The algorithms used were k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), linear regression (LR), artificial neural network (NN), and partial least square regression (PLSR). The most promising predictive performance was observed for the complete VIS-NIR-SWIR spectrum, followed by SWIR and VIS-NIR. Meanwhile, KNN, RF, and NN models were the most promising algorithms in estimating soil attributes for the dataset obtained from both sensors. The general mean R2 (determination coefficient) values obtained using these models, considering the different spectral bands evaluated, were around 0.99, 0.98, and 0.97 for sand prediction, and around 0.99, 0.98, and 0.96 for clay prediction. The lower performances, obtained for the datasets from both sensors, were observed for silt and SOC, with R2 results between 0.40 and 0.59 for these models. KNN demonstrated the best predictive performance. Integrating effective ML models with robust sample databases, obtained by advanced hyperspectral imaging and spectroradiometers, can enhance the accuracy and efficiency of soil attribute prediction.

Leveraging machine learning techniques and in-situ measurements for precise predicting the energy performance of regenerative counter-flow indirect evaporative cooler in a semi-arid climate building

This study explores the performance augmentation of a regenerative counterflow indirect evaporative cooler (RCFIEC) both experimentally and numerically. A counter flow heat/mass exchanger between the dry channel and the wet channel is effectively designed using a silica gel wet material. The RCFIECS is examined under different operational scenarios and thoroughly assessed in terms of wet-point effectiveness, and dew-point effectiveness, energy efficiency ratio, and cooling capacity. More so, leveraging machine learning models including Categorical Boosting Regressor (CatBR), Extra Tree Regressor (ETR), Random Forest Regressor (RFR), Voting Hybrid Regressor (VHR), Support Vector Regressor (SVR), and SVR coupled with Particle Swarm Optimization (PSO) are also developed to predict the performance parameters of the RCFIECS. Furthermore, the robustness of the six models is assessed using eight different statistical measures. The experimental findings indicated that the wet-bulb effectiveness of the proposed RCFIEC varied from 65 % to 110 %, while the dew-point effectiveness varied from 45 % to 80 %, at inlet air velocity varied from 0.15 to 0.90 m/s, respectively. Additionally, the system maintained improved cooling performance in which the averaged values of dew effectiveness, energy efficiency ratio, cooling capacity, and average cold air temperature are 0.60, 0.788, 31.6 W, and 25.0 °C, respectively. Furthermore, the statistical analyses prove that the optimal prediction accuracy achieved by ETR, followed by SVR-PSO, CatBR, VHR, and RFR, respectively, while the least accuracy yielded by the standalone SVR method. Specifically, the deterministic coefficient and RMSE of the predictive wet Effectiveness are 0.9978 and 0.0103 for ETR, and 0.9977 and 0.0128 for SVR-PSO, and 0.5515 and 0.1509 for typical SVR, respectively. Conclusively, the ETR approach can be deemed a potent optimization tool not only for forecasting the energetic performance of building cooling systems but is also valuable for various real-time applications.

Form Uncertainty to Sustainable Decision-Making: A Novel MIDAS–AM–DeepAR-Based Prediction Model for E-Commerce Industry Development

Global efforts to build sustainable e-commerce ecosystems through various prediction tools have suffered due to uncertainty in politics, the economy, and the environment. This paper proposes a new integrative prediction model to track the sustainable development of e-commerce. Using US e-commerce data, this study explores the prediction accuracy of the mixed data sampling (MIDAS) model in combination with the attention mechanism (AM) approach, analyzing the performance differences between the model’s before and after improvements. More so, the paper evaluates the performance of the new prediction approach against other competing models using the prediction accuracy metric, the probability interval test, and the Diebold and Mariann (DM) test methods. The results indicate that the introduction of the MIDAS and the AM approaches allows the prediction model to fully utilize the effective information of the mixed-frequency data while simultaneously capturing the differences in the importance of the variables in terms of their time series and the non-linear relationship of the learning variables, thereby positively influencing the economic prediction of the e-commerce industry. Second, the proposed prediction model combines the ability of long-term and short-term high-precision prediction and performs multi-step probability prediction on the development of the e-commerce industry. It can better track abnormal changes in macroeconomic indicators and fit their fluctuation trends. Third, based on the results of the three evaluation indicators, the MIDAS–AM–Deep autoregressive recurrent neural network (DeepAR) model achieves optimal prediction accuracy, allowing it to provide more timely, accurate, and comprehensive predictions for e-commerce management decisions when macroeconomic conditions are undergoing significant transformations.

Prediction of photovoltaic power generation based on a hybrid model

In order to fully exploit the relationship between temporal features in photovoltaic power generation data and improve the prediction accuracy of photovoltaic power generation, a photovoltaic power generation forecasting method is proposed based on a hybrid model of the convolutional neural network (CNN) and extreme gradient boost (XGBoost). Taking the historical data of China’s photovoltaic power plants as a sample, the high-dimensional mapping relationship of photovoltaic power generation variables is extracted based on the convolutional layer and pooling layer of the CNN network to construct a high-dimensional time-series feature vector, which is an input for the XGBoost. A photovoltaic power generation prediction model is established based on CNN-XGBoost by training CNN and XGBoost parameters. Since it is difficult for a single model to achieve optimal prediction accuracy under different weather conditions, the k-means clustering algorithm is used to group the power datasets and train independent models to improve prediction accuracy. Through the actual data verification of photovoltaic power plants, the proposed photovoltaic power generation prediction model can accurately predict the power, which shows high prediction accuracy and generalization ability compared with other methods.

Prediction of martensite start temperature of steel combined with expert experience and machine learning

ABSTRACT The martensite start temperature (MS) plays a pivotal role in formulating heat treatment regimes for steel. This paper, through the compilation of experimental data from literature and the incorporation of expert knowledge to construct features, employs machine learning algorithms to predict the MS of steel. The study highlights that the ETR algorithm attains optimal prediction accuracy, and the inclusion of atomic features enhances the model’s performance. Feature selection is accomplished by evaluating linear and nonlinear relationships between data using the Pearson correlation coefficient (PCC), variance inflation factor (VIF), and maximum information coefficient (MIC). Subsequently, the performance of machine learning models on unknown data is compared to validate the model’s generalization ability. The introduction of SHAP values for model interpretability analysis unveils the influencing mechanisms between features and the target variable. Finally, utilizing a specific steel type as an illustration, the paper underscores the practical value of the model.

A novel method of fuel consumption prediction for wing-diesel hybrid ships based on high-dimensional feature selection and improved blending ensemble learning method

Establishing an accurate fuel consumption prediction model is essential to optimize energy efficiency in wing-diesel hybrid ships. This study proposes an improved Blending ensemble learning method for predicting fuel consumption. Initially, the original data is cleaned using artificial experience and Interquartile Range (IQR) method. A high-dimensional feature selection method, variance analysis- Pearson correlation-mutual information-permutation importance (VA-PC-MI-PI), is then proposed to identify 16 key features from the initial 111 features. Furthermore, 9 algorithms are employed for prediction analysis under different temporal resolution data conditions. Based on the results, optimal time step and algorithm models are determined to construct the Blending ensemble model (BEM). Subsequently, the feature data of “sailing speed” and “bow draft”, which account for the highest permutation importance, are imported into the metadata set to alleviate insufficient BEM data utilization. Additionally, Extra Trees (ET) model is chosen as the meta-learner for adaptation and Bayesian method is employed for global optimization. The resulting improved Blending ensemble model (IBEM) exhibits optimal prediction accuracy and generalization ability with an RMSE of 66.04 kg/h, representing a reduction of 16.99% and 13.86% compared to the Support Vector Machine (SVM) model and BEM, respectively. Additionally, the IBEM exhibits significantly smaller residuals and absolute residuals, demonstrating superior predictive stability and consistency. This study provides an important reference and basis for energy efficiency prediction, control, and optimization of wing-diesel hybrid ships.

Comparison of deep learning models to traditional Cox regression in predicting survival of colon cancer: Based on the SEER database.

In this study, a deep learning algorithm was used to predict the survival rate of colon cancer (CC) patients, and compared its performance with traditional Cox regression. In this population-based cohort study, we used the characteristics of patients diagnosed with CC between 2010 and 2015 from the Surveillance, Epidemiology and End Results (SEER) database. The population was randomized into a training set (n=10596, 70%) and a test set (n=4536, 30%). Brier scores, area under the (AUC) receiver operating characteristic curve and calibration curves were used to compare the performance of the three most popular deep learning models, namely, artificial neural networks (ANN), deep neural networks (DNN), and long-short term memory (LSTM) neural networks with Cox proportional hazard (CPH) model. In the independent test set, the Brier values of ANN, DNN, LSTM and CPH were 0.155, 0.149, 0.148, and 0.170, respectively. The AUC values were 0.906 (95% confidence interval [CI] 0.897-0.916), 0.908 (95% CI 0.899-0.918), 0.910 (95% CI 0.901-0.919), and 0.793 (95% CI 0.769-0.816), respectively. Deep learning showed superior promising results than CPH in predicting CC specific survival. Deep learning showed potential advantages over traditional CPH models in terms of prognostic assessment and treatment recommendations. LSTM exhibited optimal predictive accuracy and has the ability to provide reliable information on individual survival and treatment recommendations for CC patients.

Wind Shear and Aircraft Aborted Landings: A Deep Learning Perspective for Prediction and Analysis

In civil aviation, severe weather conditions such as strong wind shear, crosswinds, and thunderstorms near airport runways often compel pilots to abort landings to ensure flight safety. While aborted landings due to wind shear are not common, they occur under specific environmental and situational circumstances. This research aims to accurately predict aircraft aborted landings using three advanced deep learning techniques: the conventional deep neural network (DNN), the deep and cross network (DCN), and the wide and deep network (WDN). These models are supplemented by various data augmentation methods, including the Synthetic Minority Over-Sampling Technique (SMOTE), KMeans-SMOTE, and Borderline-SMOTE, to correct the imbalance in pilot report data. Bayesian optimization was utilized to fine-tune the models for optimal predictive accuracy. The effectiveness of these models was assessed through metrics including sensitivity, precision, F1-score, and the Matthew Correlation Coefficient. The Shapley Additive Explanations (SHAP) algorithm was then applied to the most effective models to interpret their results and identify key factors, revealing that the intensity of wind shear, specific runways like 07R, and the vertical distance of wind shear from the runway (within 700 feet above runway level) were significant factors. The results of this research provide valuable insights to civil aviation experts, potentially revolutionizing safety protocols for managing aborted landings under adverse weather conditions, thereby improving overall airport efficiency and safety.

The role of apparent diffusion coefficient in the grading of adult isocitrate dehydrogenase-mutant astrocytomas: relationship with the Ki-67 proliferation index.

The grading of adult isocitrate dehydrogenase (IDH)-mutant astrocytomas is a crucial prognostic factor. To investigate the value of conventional magnetic resonance imaging (MRI) features and apparent diffusion coefficient (ADC) in the grading of adult IDH-mutant astrocytomas, and to analyze the correlation between ADC and the Ki-67 proliferation index. The clinical and MRI data of 82 patients with adult IDH-mutant astrocytoma who underwent surgical resection and molecular genetic testing with IDH and 1p/19q were retrospectively analyzed. The conventional MRI features, ADCmin, ADCmean, and nADC of the tumors were compared using the Kruskal-Wallis single factor ANOVA and chi-square tests. Receiver operating characteristic (ROC) curves were drawn to evaluate conventional MRI and ADC accuracy in differentiating tumor grades. Pearson correlation analysis was performed to determine the correlation between ADC and the Ki-67 proliferation index. The difference in enhancement, ADCmin, ADCmean, and nADC among WHO grade 2, 3, and 4 tumors was statistically significant (all P <0.05). ADCmin showed the preferable diagnostic accuracy for grading WHO grade 2 and 3 tumors (AUC=0.724, sensitivity=63.4%, specificity=80%, positive predictive value (PPV)=62.0%; negative predictive value (NPV)=82.5%), and distinguishing grade 3 from grade 4 tumors (AUC=0.764, sensitivity=70%, specificity=76.2%, PPV=75.0%, NPV=71.4%). Enhancement + ADC model showed an optimal predictive accuracy (grade 2 vs. 3: AUC = 0.759; grade 3 vs. 4: AUC = 0.799). The Ki-67 proliferation index was negatively correlated with ADCmin, ADCmean, and nADC (all P <0.05), and positively correlated with tumor grade. Conventional MRI features and ADC are valuable to predict pathological grading of adult IDH-mutant astrocytomas.

Quantitative analysis of the illegal addition of Atenolol in Panax notoginseng based on NIR-MIR spectral data fusion and calibration transfer.

To address the issue of the common illegal addition of Atenolol in Panax notoginseng, we propose an approach that realizes multivariate calibration transfer between different particle sizes based on near-infrared (NIR) and mid-infrared (MIR) spectral data fusion. To achieve high prediction accuracy, we construct three data fusion schemes (full-spectrum fusion, feature-level fusion, and decision-level fusion) that combine NIR and MIR spectral data. Among three data fusion schemes, the feature-level fusion based on the UVE-SPA-PLS model for 120-mesh spectral data achieves optimal prediction accuracy. Here, a Piecewise Direct Standardization (PDS) algorithm has been applied to calibration transfer from 100-mesh and 80-mesh to 120-mesh to reduce the influence of particle size and improve the robustness of the model. The correlation coefficient (R2) of 100-mesh, and 80-mesh prediction sets can reach 0.9861 and 0.9823, respectively. The corresponding root mean square error (RMSE) are 0.1545 and 0.2045, respectively. This research provides a method for illegal additions in precious herbs and reduces the effect of particle size on spectral modeling, enabling high-precision quantitative detection. In addition, it has important application prospects in reducing experimental losses of precious medicinal materials and ensuring the safe use of Chinese and Western medicines, which provides an alternative method for non-destructive testing.

Estimation of Tire Side-Slip Angles Based on the Frequency Domain Lateral Acceleration Characteristics Inside Tires

The identification and control of tire side slip angle is the key to vehicle stability control. Intelligent tire technology based on the sensing of side-slip acceleration inside the tire provides a novel method for estimating the tire side-slip angle. This study proposed a method to estimate the tire side-slip angle by using the frequency domain lateral acceleration of the tire. First, an intelligent tire testing system was constructed by independently developing a special rim assembly and data collector. A three-axis accelerometer was placed on the right side of the tire, and the acceleration value was acquired by using a wired method with a sampling frequency of 50 kHz. Second, based on the constructed test system, a tire side deflection test was carried out on the Flat Trac bench. Through data analysis, it was found that the lateral acceleration was in the frequency domain of 400 Hz. As the side-slip angle increased from −4° to 4°, the vibration amplitude gradually decreased. Moreover, the vibration amplitude within 0.5~2 kHz was highly correlated with the side-slip angle. Subsequently, the vibration amplitude of the lateral acceleration within 2 kHz was extracted at an interval of 20 Hz as the feature point, and a frequency domain data set FDAy3 was established together with the vertical load and tire pressure. Finally, the support vector machine (SVM) algorithm was employed to make predictions on the data set. The grid search method was utilized to find the optimal parameter values of the model penalty factor c and radial basis kernel function coefficient g, which were 1.4142 and 0.0884, respectively. The results suggested that the root mean square error of the model prediction was 0.0806°, and the maximum estimated angle deviation of the prediction was 0.4587°. Meanwhile, the optimal prediction accuracy and real-time performance were achieved when the number of feature points and the feature frequency band were 25 and within 500 Hz, respectively. The findings of this study confirm that it is feasible to estimate the tire side-slip angle based on the frequency domain lateral acceleration of the tire, which provides a new method for tire side-slip angle estimation.

Advancing Traffic Simulation Precision and Scalability: A Data-Driven Approach Utilizing Deep Neural Networks

In traditional traffic simulation studies, vehicle behavior has typically been modeled using complex analytical frameworks, which often struggle to encompass the full range of variables affecting vehicle operations. Addressing this gap, our research introduces an innovative data-driven framework for traffic simulation that incorporates human driving data into its decision-making processes. This enables the modeling of diverse vehicle behaviors by taking into account both vehicle-specific characteristics and environmental factors. At the core of this framework are two advanced deep neural networks, convolutional long short-term memory and convolutional gated recurrent unit, which underpin our vehicle traffic simulation model. Utilizing datasets from the Next Generation Simulation project, specifically the I-80 and US-101 road sections, our study further evaluates the framework’s performance through single-step continuous prediction, as well as transferability tests, employing the TransMSEloss function to optimize prediction accuracy. Our findings reveal that the proposed data-driven model significantly outperforms traditional models, achieving an exceptional accuracy of 97.22% in training and 95.76% in testing. Notably, in continuous prediction, our model maintains an 89.57% accuracy up to the fifth step, exceeding the traditional framework’s 82.82% by 5% to 10% at each step. Time cost analysis indicates that while the data-driven framework’s advantages are more pronounced in large-scale simulations, it also demonstrates strong transferability, with a 93.48% accuracy on diverse datasets, showcasing its applicability across different traffic scenarios. This study not only highlights the potential of deep learning in traffic simulation, but also sets a new benchmark for accuracy and scalability in the field.

Improving the prediction performance of leaf water content by coupling multi-source data with machine learning in rice (Oryza sativa L.)

BackgroundLeaf water content (LWC) significantly affects rice growth and development. Real-time monitoring of rice leaf water status is essential to obtain high yield and water use efficiency of rice plants with precise irrigation regimes in rice fields. Hyperspectral remote sensing technology is widely used in monitoring crop water status because of its rapid, nondestructive, and real-time characteristics. Recently, multi-source data have been attempted to integrate into a monitored model of crop water status based on spectral indices. However, there are fewer studies using spectral index model coupled with multi-source data for monitoring LWC in rice plants. Therefore, 2-year field experiments were conducted with three irrigation regimes using four rice cultivars in this study. The multi-source data, including canopy ecological factors and physiological parameters, were incorporated into the vegetation index to accurately predict LWC in rice plants.ResultsThe results presented that the model accuracy of rice LWC estimation after combining data from multiple sources improved by 6–44% compared to the accuracy of a single spectral index normalized difference index (ND). Additionally, the optimal prediction accuracy of rice LWC was produced using a machine algorithm of gradient boosted decision tree (GBDT) based on the combination of ND(1287,1673) and crop water stress index (CWSI) (R2 = 0.86, RMSE = 0.01).ConclusionsThe machine learning estimation model constructed based on multi-source data fully utilizes the spectral information and considers the environmental changes in the crop canopy after introducing multi-source data parameters, thus improving the performance of spectral technology for monitoring rice LWC. The findings may be helpful to the water status diagnosis and accurate irrigation management of rice plants.

A Novel Approach for Predicting Anthropogenic CO2 Emissions Using Machine Learning Based on Clustering of the CO2 Concentration

The monitoring of anthropogenic CO2 emissions, which increase the atmospheric CO2 concentration, plays the most important role in the management of emission reduction and control. With the massive increase in satellite-based observation data related to carbon emissions, a data-driven machine learning method has great prospects for predicting anthropogenic CO2 emissions. Training samples, which are used to model predictions of anthropogenic CO2 emissions through machine learning algorithms, play a key role in obtaining accurate predictions for the spatial heterogeneity of anthropogenic CO2 emissions. We propose an approach for predicting anthropogenic CO2 emissions using the training datasets derived from the clustering of the atmospheric CO2 concentration and the segmentation of emissions to resolve the issue of the spatial heterogeneity of anthropogenic CO2 emissions in machine learning modeling. We assessed machine learning algorithms based on decision trees and gradient boosting (GBDT), including LightGBM, XGBoost, and CatBoost. We used multiple parameters related to anthropogenic CO2-emitting activities as predictor variables and emission inventory data from 2019 to 2021, and we compared and verified the accuracy and effectiveness of different prediction models based on the different sampling methods of training datasets combined with machine learning algorithms. As a result, the anthropogenic CO2 emissions predicted by CatBoost modeling from the training dataset derived from the clustering analysis and segmentation method demonstrated optimal prediction accuracy and performance for revealing anthropogenic CO2 emissions. Based on a machine learning algorithm using observation data, this approach for predicting anthropogenic CO2 emissions could help us quickly obtain up-to-date information on anthropogenic CO2 emissions as one of the emission monitoring tools.

A data-driven data-augmentation method based on Slim-generative Adversarial Imputation Networks for short-term ship-motion attitude prediction

The performance of data-driven complex ship-motion attitude prediction models not only depends on the structure and algorithm design of the model but is also affected by the quantity and quality of training samples. Insufficient available data, resulting from factors such as low sampling rates and high costs, could inhibit the achievement of optimal prediction accuracy. This limitation in turn increases the model’s expected generalization error of the model and causes overfitting in regression models. Nevertheless, by artificially expanding the training dataset, data-augmentation methods enhance the resistance of the model to overfitting, thereby improving the prediction performance. This study introduces a novel data-driven model for ship-motion prediction based on the principles of Slim Generative Adversarial Imputation Networks (SGAINs). The model not only increases the amount of data but also considers temporal correlation features in the enhancement process, thus uniquely addressing the issue of poor accuracy of prediction models caused by insufficient training datasets. The simulation results demonstrate the effectiveness and superiority of the proposed model for short-term ship-motion attitude data augmentation.

Exploration of Global and Specialized Near-Infrared Calibrations for the Quantification of Nutritional Content in Grapevine Organs, Berry Phenological Stages, and Shoot Lignification.

Current infrared spectroscopy applications in the field of viticulture are moving toward direct in-field measuring techniques. However, limited research is available on quantitative applications using direct measurement of fresh tissue. The few studies conducted have combined the spectral data from various cultivars, growing regions, grapevine organs, and phenological stages during model development. The spectral data from these heterogeneous samples are combined into a single data set and analyzed jointly during quantitative analysis. Combining the spectral information of these diverse samples into a global data set could be an unsuitable approach and could yield less accurate prediction results. Spectral differences among samples could be overlooked during model development and quantitative analysis. The development of specialized calibrations should be considered and could lead to more accurate quantitative analyses. This study explored a model optimization strategy attempting global and specialized calibrations. Global calibrations, containing data from multiple organs, berry phenological, and shoot lignification stages, were compared to specialized calibrations per organ or stage. The global calibration for organs contained data from shoots, leaves, and berries and produced moderately accurate prediction results for nitrogen, carbon, and hydrogen. The specialized calibrations per organ yielded more accurate calibrations with a coefficient of determination in validation (R2val) at 90.65% and a root mean square error of prediction (RMSEP) at 0.32% dry matter (DM) for the berries' carbon calibrations. The leaves and shoots carbon calibrations had R2val and RMSEP at 84.99%, 0.34% DM, and 90.06%, 0.37% DM, respectively. The specialized calibrations for nitrogen and hydrogen showed similar improvements in prediction accuracy per organ. Specialized calibrations per phenological and lignification stage were also explored. Not all stages showed improvement, however, most stages had comparable or improved results for the specialized calibrations compared to the global calibrations containing all phenological or lignification stages. The results indicated that both global and specialized calibrations should be considered during model development to optimize prediction accuracy.