Gradient Boosting Decision Tree Algorithm Research Articles

Predicting and detecting fires on multispectral images using machine learning methods

In today's world, fire forecasting and early detection play a critical role in preventing disasters and minimizing damage to the environment and human settlements. The main goal of the study is the development and testing of machine learning algorithms for automated detection of the initial stages of fires based on the analysis of multispectral images. Within the framework of this study, the capabilities of three popular machine learning methods: extreme gradient boosting, logistic regression, and vanilla convolutional neural network (vanilla CNN), are considered in the task of processing and interpreting multispectral images to predict and detect fires. XGBoost, as a gradient-boosted decision tree algorithm, provides high processing speed and accuracy, logistic regression stands out for its simplicity and interpretability, while vanilla CNN uses the power of deep learning to analyze spatial and spectral data. The results of the study show that the integration of these methods into monitoring systems can significantly improve the efficiency of early fire detection, as well as help in predicting potential fires.

Immediate postnatal prediction of death or bronchopulmonary dysplasia among very preterm and very low birth weight infants based on gradient boosting decision trees algorithm: A nationwide database study in Japan.

Bronchopulmonary dysplasia (BPD) poses a substantial global health burden. Individualized treatment strategies based on early prediction of the development of BPD can mitigate preterm birth complications; however, previously suggested predictive models lack early postnatal applicability. We aimed to develop predictive models for BPD and mortality based on immediate postnatal clinical data. Clinical information on very preterm and very low birth weight infants born between 2008 and 2018 was extracted from a nationwide Japanese database. The gradient boosting decision trees (GBDT) algorithm was adopted to predict BPD and mortality, using predictors within the first 6 h postpartum. We assessed the temporal validity and evaluated model adequacy using Shapley additive explanations (SHAP) values. We developed three predictive models using data from 39,488, 39,096, and 40,291 infants to predict "death or BPD," "death or severe BPD," and "death before discharge," respectively. These well-calibrated models achieved areas under the receiver operating characteristic curve of 0.828 (95% CI: 0.828-0.828), 0.873 (0.873-0.873), and 0.887 (0.887-0.888), respectively, outperforming the multivariable logistic regression models. SHAP value analysis identified predictors of BPD, including gestational age, size at birth, male sex, and persistent pulmonary hypertension. In SHAP value-based case clustering, the "death or BPD" prediction model stratified infants by gestational age and persistent pulmonary hypertension, whereas the other models for "death or severe BPD" and "death before discharge" commonly formed clusters of low mortality, extreme prematurity, low Apgar scores, and persistent pulmonary hypertension of the newborn. GBDT models for predicting BPD and mortality, designed for use within 6 h postpartum, demonstrated superior prognostic performance. SHAP value-based clustering, a data-driven approach, formed clusters of clinical relevance. These findings suggest the efficacy of a GBDT algorithm for the early postnatal prediction of BPD.

Development of interpretable machine learning models for prediction of acute kidney injury after noncardiac surgery: a retrospective cohort study.

Early identification of patients at high risk of postoperative acute kidney injury (AKI) can facilitate the development of preventive approaches. This study aimed to develop prediction models for postoperative AKI in noncardiac surgery using machine learning algorithms. We also evaluated the predictive performance of models that included only preoperative variables or only important predictors. Adult patients undergoing noncardiac surgery were retrospectively included in the study (76,457 patients in the discovery cohort and 11,910 patients in the validation cohort). AKI was determined using the KDIGO criteria. The prediction model was developed using 87 variables (56 preoperative variables and 31 intraoperative variables). A variety of machine learning algorithms were employed to develop the model, including logistic regression, random forest, extreme gradient boosting, and gradient boosting decision trees (GBDT). The performance of different models was compared using the area under the receiver operating characteristic curve (AUROC). Shapley Additive Explanations (SHAP) analysis was employed for model interpretation. The patients in the discovery cohort had a median age of 52 years (IQR: 42-61y), and 1179 patients (1.5%) developed AKI after surgery. The GBDT algorithm showed the best predictive performance using all available variables, or only preoperative variables. The AUROCs were 0.849 (95% CI, 0.835-0.863) and 0.828 (95% CI, 0.813-0.843), respectively. The SHAP analysis showed that age, surgical duration, preoperative serum creatinine and gamma-glutamyltransferase, as well as American Society of Anesthesiologists physical status III were the most important five features. When gradually reducing the features, the AUROCs decreased from 0.852 (including the top 40 features) to 0.839 (including the top 10 features). In the validation cohort, we observed a similar pattern regarding the models' predictive performance. The machine learning models we developed had satisfactory predictive performance for identifying high-risk postoperative AKI patients. Further, we found that model performance was only slightly affected when only preoperative variables or only the most important predictive features were included.

Residual facial erythema in atopic dermatitis patients treated with dupilumab stratified by machine learning.

Persistent facial erythema represents a significant complication in atopic dermatitis (AD) patients undergoing treatment with dupilumab. Stratifying patients based on the erythema course is crucial for elucidating heterogeneous phenotypes and facilitating advanced drug efficacy predictions. This study aimed to identify factors associated with facial erythema severity in dupilumab-treated AD patients and to establish a prediction model for drug response based on the identified factors. Data from a retrospective study conducted between July 2018 and July 2021 were collected and analysed. Patients were categorized into three groups via hierarchical clustering based on the course of facial erythema: early remission, low remission and persistent residual. LightGBM, a supervised gradient boosting decision tree algorithm, was employed to discern group differences and construct a prediction model. The model incorporated patient demographic and clinical profiles, including pre- and post-treatment examinations. The model's performance was evaluated using accuracy and the area under the receiver operating characteristic curve (AUC). The binary classification model demonstrated an accuracy of 89.10% and an AUC of 0.862 when distinguishing between early remission and persistent residual patients. The eight prominent factors associated with facial erythema severity included age, sex, lactate dehydrogenase (LDH), immunoglobulin E (IgE), eosinophil count, white blood cell count, Alnus allergy and cedar allergy. This study has two main significances: first, three clusters were identified through unsupervised learning; second, a classification model was constructed that proved more accurate than random prediction. The stratification and identification of crucial factors associated with residual facial erythema in dupilumab-treated AD patients lay the foundation for AI-powered prognostic models. This groundwork provides a substantial basis for enhancing future medical AI support in AD treatment selection, potentially improving personalized treatment approaches and outcomes.

Power Losses Reduction of Stacked Multicell Converter Fed Wind Energy Conversion System (WECS) Using Hybrid Methodology

This paper proposes a hybrid method for reduction of power losses of multicell converter in wind energy conversion system (WECS). The proposed methodology is the Artificial Intelligence (AI) based phase disposition pulse width modulation (PD-PWM). The Artificial Intelligence (AI) is the Gradient Boosting Decision Tree algorithm (GBDT). Hence it is named as GBDT-PD-PWM. Gradient boosting is a machine learning system that creates a prediction model in the group of weak prediction models, often called decision trees. The multicell converter is employed as the grid side converter (GSC) of WECS to attenuate harmonics created by nonlinear load. For stacked multicell converters (SMCs), a method of capacitor voltage balance based on PD-PWM approach is proposed.

Prediction and Diagnosis of Electric Vehicle Battery Fault Based on Abnormal Voltage: Using Decision Tree Algorithm Theories and Isolated Forest

Battery voltage is a pivotal parameter for evaluating battery health and safety. The precise prediction of battery voltage and the implementation of anomaly detection are imperative for ensuring the secure and dependable operation of battery systems. Nevertheless, during the actual operation of electric vehicles, battery performance is subject to the influence of the vehicle's operational state and battery characteristic parameters, introducing challenges to safety alerts. In order to address these challenges and achieve precise battery voltage prediction, this paper comprehensively considers the battery characteristics and driving behavior of electric vehicles in both charging and operational states. Mathematical processing, including averaging and variance calculation, is applied to the battery characteristic parameter data and driving behavior data. By integrating historical voltage data and employing a modified gradient boosting decision tree algorithm (GBDT), a fast and accurate online voltage prediction method is proposed. Hyperparameter optimization is employed to minimize prediction voltage errors. The accuracy and timeliness of the predictions are validated through a comprehensive evaluation and comparison of the forecasted voltages. To diagnose anomalies in battery voltage, the paper proposes a fault diagnosis method that combines the Isolation Forest and Boxplot techniques. Finally, utilizing authentic electric vehicle data for validation, the research underscores the capability of the proposed method to achieve accurate voltage predictions six minutes in advance and provide effective fault diagnosis. This investigation carries substantial practical implications for fortifying battery management and optimizing the performance of electric vehicles.

Application of Gradient Boosting Decision Tree Algorithm in Operation Quality Evaluation of Electric Energy Metering Device in Electric Power Company

Application of Gradient Boosting Decision Tree Algorithm in Operation Quality Evaluation of Electric Energy Metering Device in Electric Power Company

Real‐time prediction and ponding process early warning method at urban flood points based on different deep learning methods

AbstractAccurate prediction of urban floods is regarded as one of the critical means to prevent urban floods and reduce the losses caused by floods. In this study, a refined prediction and early warning method system for urban flood and waterlogging processes based on deep learning methods is proposed. The spatial autocorrelation of rain and ponding points is analyzed by Moran's I (a common used statistic for spatial autocorrelation). For each ponding point, the relationship model between the rainfall process and ponding process is constructed based on different deep learning methods, and the results are analyzed and verified by mean absolute error (MAE), root mean square error (RMSE), Nash efficiency coefficient (NSE) and correlation coefficient (CC). The results show that the gradient boosting decision tree algorithm has the highest accuracy and efficiency (with a 0.001 m RMSE of the predicted and measured ponding depth) for ponding process prediction and is regarded as the most suitable method for ponding process prediction. Finally, the real‐time prediction and early warning of urban floods and waterlogging processes driven by rainfall forecast data are realized, and the results are verified by the measured data. The research results can provide theoretical support for urban flood prevention and control.

The use of machine learning to predict somatic cell count status in dairy cows post-calving.

Udder health remains a priority for the global dairy industry to reduce pain, economic losses, and antibiotic usage. The dry period is a critical time for the prevention of new intra-mammary infections and it provides a point for curing existing intra-mammary infections. Given the wealth of udder health data commonly generated through routine milk recording and the importance of udder health to the productivity and longevity of individual cows, an opportunity exists to extract greater value from cow-level data to undertake risk-based decision-making. The aim of this research was to construct a machine learning model, using routinely collected farm data, to make probabilistic predictions at drying off for an individual cow's risk of a raised somatic cell count (hence intra-mammary infection) post-calving. Anonymized data were obtained as a large convenience sample from 108 UK dairy herds that undertook regular milk recording. The outcome measure evaluated was the presence of a raised somatic cell count in the 30 days post-calving in this observational study. Using a 56-farm training dataset, machine learning analysis was performed using the extreme gradient boosting decision tree algorithm, XGBoost. External validation was undertaken on a separate 28-farm test dataset. Statistical assessment to evaluate model performance using the external dataset returned calibration plots, a Scaled Brier Score of 0.095, and a Mean Absolute Calibration Error of 0.009. Test dataset model calibration performance indicated that the probability of a raised somatic cell count post-calving was well differentiated across probabilities to allow an end user to apply group-level risk decisions. Herd-level new intra-mammary infection rate during the dry period was a key driver of the probability that a cow had a raised SCC post-calving, highlighting the importance of optimizing environmental hygiene conditions. In conclusion, this research has determined that probabilistic classification of the risk of a raised SCC in the 30 days post-calving is achievable with a high degree of certainty, using routinely collected data. These predicted probabilities provide the opportunity for farmers to undertake risk decision-making by grouping cows based on their probabilities and optimizing management strategies for individual cows immediately after calving, according to their likelihood of intra-mammary infection.

Establishment of a differential diagnosis method and an online prediction platform for AOSD and sepsis based on gradient boosting decision trees algorithm

ObjectiveThe differential diagnosis between adult-onset Still’s disease (AOSD) and sepsis has always been a challenge. In this study, a machine learning model for differential diagnosis of AOSD and sepsis was developed and an online platform was developed to facilitate the clinical application of the model.MethodsAll data were collected from 42 AOSD patients and 50 sepsis patients admitted to Affiliated Hospital of Xuzhou Medical University from December 2018 to December 2021. In addition, 5 AOSD patients and 10 sepsis patients diagnosed in our hospital after March 2022 were collected for external validation. All models were built using the scikit-learn library (version 1.0.2) in Python (version 3.9.7), and feature selection was performed using the SHAP (Shapley Additive exPlanation) package developed in Python.ResultsThe results showed that the gradient boosting decision tree(GBDT) optimization model based on arthralgia, ferritin × lymphocyte count, white blood cell count, ferritin × platelet count, and α1-acid glycoprotein/creatine kinase could well identify AOSD and sepsis. The training set interaction test (AUC: 0.9916, ACC: 0.9457, Sens: 0.9556, Spec: 0.9578) and the external validation also achieved satisfactory results (AUC: 0.9800, ACC: 0.9333, Sens: 0.8000, Spec: 1.000). We named this discrimination method AIADSS (AI-assisted discrimination of Still’s disease and Sepsis) and created an online service platform for practical operation, the website is http://cppdd.cn/STILL1/.ConclusionWe created a method for the identification of AOSD and sepsis based on machine learning. This method can provide a reference for clinicians to formulate the next diagnosis and treatment plan.

Abstract 13652: Machine Learning Prediction of RV End-Diastolic Volume in Patients With Pulmonary Regurgitation

Introduction: RV end-diastolic volume (RVEDV) by cardiac MRI (cMRI) guides intervention in congenital heart disease. Guideline directed linear and area measurements from two-dimensional echocardiography (2DE) have poor univariable linear correlation with RVEDV. We hypothesized that non-linear modeling of 2DE RV measurements may predict cMRI volume. Aims: We sought to develop a prediction algorithm for RVEDV from guideline-directed RV 2DE measurements. Methods: Patients from 2009 to 2022 with > mild pulmonary regurgitation and cMRI +/- 3 months were included. 2DE measurements were: RV end-diastolic (RVED) apical 4-chamber area, basal RVED diameter (d), mid RVED d, tricuspid annulus d, RVED length; PSLAX proximal RVOT d; PSSAX proximal RVOT d, distal RVOT d. A gradient-boosting decision trees algorithm (XGBoost) regressed cMRI RVEDV (mL) using 2DE inputs (including missing data) with leave-one-out crossvalidation. Predicted RVEDV was BSA-indexed to examine performance at clinical threshold 150 mL/m 2 . Intraclass correlation coefficient (ICC) in 30 samples was measured. Results: Of 243 subjects (median age 21 [IQR 16-31] y, 58% tetralogy of Fallot, 22% congenital pulmonary stenosis s/p intervention), 19% had cMRI RVEDV > 150 mL/m 2 . Missing ranged from 5% (basal RVED d) to 17% (PSSA proximal RVOT), and ICC ranged from 0.46 (basal RVED d) to 0.78 (tricuspid annulus). Mean absolute error was 40 mL (27 mL/m 2 ). At the predicted threshold 150 mL/m 2 had 39% sensitivity, 87% specificity, 40% positive predictive value, and 86% negative predictive value for true RV dilation. Conclusion: Prediction of RVEDV from guideline recommended 2DE RV measurements is possible. Detection of clinically significant RV dilation has low sensitivity but may be helpful in ruling out cases. Sources of error include interrater variability and missing data due to poor visualization of the RV on 2DE. Future work to develop a consistent and informative feature set for modeling is required.

Research on Detection and Restoration Methods of Basic Operation Data for Inter-Basin Water Diversion Projects

Inter-basin water diversion is an essential means to alleviate the contradiction between the supply and demand of water resources, and accurate hydraulic modelling guarantees smooth operation. However, due to the increasing complexity of water diversion methods, structures, water conservancy facilities and equipment, it is difficult to obtain accurate and effective measured data to establish a model. Therefore, based on a data-driven method, this paper diagnoses and restores the important parameters of the water diversion projects, including the elevation of pipeline and water level data, which can be used to establish the adaptive hydraulic transition model of the water diversion projects. Firstly, the abnormal data of the elevation of pipeline were identified using expert data annotation and support vector classification (SVC), with the identification accuracy of abnormal data being as high as 91%. Then, the single and continuous abnormal data were restored using an interpolation method and multiple linear regression algorithm (MLR), and the restored data were found to be consistent with the judgment of expert knowledge. Secondly, K-medoids was used to classify the complex multi-dimensional water level data, combined with the 3-sigma method to identify the outliers in each class. The gradient boosting decision tree algorithm (GBDT) trained on normal data restored outliers in a predictive manner, and the mean absolute percentage error (MAPE) was 0.003%, 0.025% and 0.091% in each class, respectively, showing the best accuracy compared with other models.

Intratumoral and peritumoral radiomics based on contrast-enhanced MRI for preoperatively predicting treatment response of transarterial chemoembolization in hepatocellular carcinoma

BackgroundNoninvasive and precise methods to estimate treatment response and identify hepatocellular carcinoma (HCC) patients who could benefit from transarterial chemoembolization (TACE) are urgently required. The present study aimed to investigate the ability of intratumoral and peritumoral radiomics based on contrast-enhanced magnetic resonance imaging (CE-MRI) to preoperatively predict tumor response to TACE in HCC patients.MethodsA total of 138 patients with HCC who received TACE were retrospectively included and randomly divided into training and validation cohorts at a ratio of 7:3. Total 1206 radiomics features were extracted from arterial, venous, and delayed phases images. The inter- and intraclass correlation coefficients, the spearman’s rank correlation test, and the gradient boosting decision tree algorithm were used for radiomics feature selection. Radiomics models on intratumoral region (TR) and peritumoral region (PTR) (3 mm, 5 mm, and 10 mm) were established using logistic regression. Three integrated radiomics models, including intratumoral and peritumoral region (T-PTR) (3 mm), T-PTR (5 mm), and T-PTR (10 mm) models, were constructed using TR and PTR radiomics scores. A clinical-radiological model and a combined model incorporating the optimal radiomics score and selected clinical-radiological predictors were constructed, and the combined model was presented as a nomogram. The discrimination, calibration, and clinical utilities were evaluated by receiver operating characteristic curve, calibration curve, and decision curve analysis, respectively.ResultsThe T-PTR radiomics models performed better than the TR and PTR models, and the T-PTR (3 mm) radiomics model demonstrated preferable performance with the AUCs of 0.884 (95%CI, 0.821–0.936) and 0.911 (95%CI, 0.825–0.975) in both training and validation cohorts. The T-PTR (3 mm) radiomics score, alkaline phosphatase, tumor size, and satellite nodule were fused to construct a combined nomogram. The combined nomogram [AUC: 0.910 (95%CI, 0.854–0.958) and 0.918 (95%CI, 0.831–0.986)] outperformed the clinical-radiological model [AUC: 0.789 (95%CI, 0.709–0.863) and 0.782 (95%CI, 0.660–0.902)] in the both cohorts and achieved good calibration capability and clinical utility.ConclusionsCE-MRI-based intratumoral and peritumoral radiomics approach can provide an effective tool for the precise and individualized estimation of treatment response for HCC patients treated with TACE.

Applied gradient boosting decision tree algorithms for accurate prediction of trihalomethanes: A case study in dual-sources drinking water distribution system in metropolitan

The complex hydraulic environment in dual-source drinking water distribution system (DWDS) in metropolitan puts water safety in an unknown situation, which requires systematic water quality monitoring and controlling to reduce the water risk during water distribution process. Given that, this study conducted a one-year sampling of trihalomethanes (THMs) and identified the hydraulic junction area in a real dual-source DWDS. For efficient prediction of THMs, several predictive frameworks were established via mechanistic algorithms and machine learning (ML). The results indicated that Gradient Boosting Decision Tree (GBDT) algorithm achieved higher interpretability and generalizability in describing the relationship between various of features with R2 reaching 0.903, 0.908, 0.945 and 0.959 for trichloromethane (TCM), bromodichloromethane (BDCM), dibromomonochloromethane (DBCM) and tribromomethane (TBM), respectively. Shapley additive explanations (SHAP) analysis of GBDT models further identified water temperature, residual chlorine, water supply distance, and pH were key features affecting THMs formation in practical DWDS. Furthermore, the spatio-temporal distribution of THMs was simulated by GBDT models and identified the region might be exposed to high risk of the THMs. The simulation results indicated that the DBCM and TBM were more likely to form in the region nearly to DWTP-X, whereas in the region nearly to DWTP-Y, the TCM and BDCM were the dominant THMs species due to differences in raw water characteristics. Undoubtedly, this study provided novel insights into utilizing ML models to predict THMs levels in dual-source DWDS in the metropolitan.

Estimation of Forest Height Using Google Earth Engine Machine Learning Combined with Single-Baseline TerraSAR-X/TanDEM-X and LiDAR

Forest height plays a crucial role in various fields, such as forest ecology, resource management, natural disaster management, and environmental protection. In order to obtain accurate and efficient measurements of forest height over large areas, in this study, Terra Synthetic Aperture Radar-X and the TerraSAR-X Add-on for Digital Elevation Measurement (TerraSAR-X/TanDEM-X), Sentinel-2A, and Shuttle Radar Topography Mission (SRTM) data were used, and various feature combinations were established in conjunction with measurements from Light Detection and Ranging (LiDAR). Classification and regression tree (CART), gradient-boosting decision tree (GBDT), random forest (RF), and support vector machine (SVM) algorithms were employed to estimate forest height in the study area. Independent validation on the basis of LiDAR forest height samples showed the following results: (1) Regarding feature combinations, the combination of coherence and decorrelation of volume scattering provided by TerraSAR-X/TanDEM-X data outperformed the combination of backscatter coefficient and local incidence angle, as well as the combination of coherence, decorrelation of volume scattering, backscatter coefficient, and local incidence angle. The best results (R2 = 0.67, RMSE = 2.89 m) were achieved with the combination of coherence and decorrelation of volume scattering using the GBDT and RF algorithms. (2) In terms of machine learning methods, the GBDT algorithm proved suitable for estimating forest height. The most effective approach for forest height mapping involved combining the GBDT algorithm with coherence, decorrelation of volume scattering, and a small amount of LiDAR forest height data, used as training data.

A Novel Algorithm for Professor Recommendation in Higher Education

This paper introduces a novel professor-recommendation system designed specifically for community college and university courses. Building upon an existing algorithm for one-on-one teacher recommendations, we leveraged insights from the literature on Massive Open Online Course (MOOC) recommender algorithms. By analysing various approaches, we combined and refined ideas to develop an optimised system. Our approach utilises a tri-module framework that incorporates supervised and unsupervised learning techniques. The first module employs a Gradient-boosted Decision Tree algorithm, augmented with multiple factors and student dropout rates as ground truth, to generate a ranking score. The second module applies Apriori Association and Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithms to analyse these factors and identify professors with similar characteristics. In the third module, item-based collaborative filtering is employed, incorporating user ratings and the cosine similarity algorithm. The outputs from these three modules are subsequently integrated through a weighted average. This addition enables the system to prioritise opportunities for new professors, thereby ensuring a balanced recommendation approach. The resulting combined ranking score provides accurate recommendations for course instructors. This approach can be integrated into university course selection software for the benefit of both students and educational institutions.

Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error Analysis

The global navigation satellite system (GNSS) position time series provides essential data for geodynamic and geophysical studies. Interpolation of the GNSS position time series is necessary because missing data will produce inaccurate conclusions made from the studies. The spatio-temporal correlations between GNSS reference stations cannot be considered when using traditional interpolation methods. This paper examines the use of machine learning models to reflect the spatio-temporal correlation among GNSS reference stations. To form the machine learning problem, the time series to be interpolated are treated as output values, and the time series from the remaining GNSS reference stations are used as input data. Specifically, three machine learning algorithms (i.e., the gradient boosting decision tree (GBDT), eXtreme gradient boosting (XGBoost), and random forest (RF)) are utilized to perform interpolation with the time series data from five GNSS reference stations in North China. The results of the interpolation of discrete points indicate that the three machine learning models achieve similar interpolation precision in the Up component, which is 45% better than the traditional cubic spline interpolation precision. The results of the interpolation of continuous missing data indicate that seasonal oscillations caused by thermal expansion effects in summer significantly affect the interpolation precision. Meanwhile, we improved the interpolation precision of the three models by adding data from five stations which have high correlation with the initial five GNSS reference stations. The interpolated time series for the North, East, and Up (NEU) are examined by principal component analysis (PCA), and the results show that the GBDT and RF models perform interpolation better than the XGBoost model.

A literature-mining method of integrating text and table extraction for materials science publications

Scientific literature, as an important tool to present research results, contains highly valuable information. Thus, there is an urgent need for mining methods to obtain key information from unstructured literature. However, existing literature mining efforts often ignore non-textual components which contain more detailed key information, such as tables. In this study, we propose a method for information processing to extract and analyze textual and tabular information from the large-scale literature of materials science. First, we propose a SciBERT-Fasttext-BiLSTM-CRF (SFBC) model for Named Entity Recognition (NER) in materials science literature, which combines generic dynamic word vectors (GDWVs) with domain-specific static word vectors (DSWVs). Second, a method is presented to extract material names, units, and compositions from the tables in literature. Compared to other table recognition methods, the method excels at extracting structured material composition information. Furthermore, Gradient Boosting Decision Tree algorithm is used to predict property trends including corrosion resistance, ductility, strength, and hardness on the basis of the material compositions, methods, properties and their changes extracted from texts and tables. The proposed method can be applied to predict material properties. Finally, we use stainless steel as an experiment example to validate our method. From 11,058 scientific papers on stainless steel, 2.36 million material entities and 7,970 material compositions are extracted. The extraction results are filtered and applied to predict four property trends of stainless steel. The method proposed in this paper can improve the accuracy of large-scale data extraction from material science literature, and the results can be used to guide the optimization of material properties and accelerate the pace of data-driven material design.

A hybrid electric vehicle load classification and forecasting approach based on GBDT algorithm and temporal convolutional network

Due to the participation of large-scale electric vehicles (EVs) in Vehicle-to-Grid (V2G) services, V2G dispatch centers need to predict the charging and discharging (C&D) loads of electric vehicles in a short time period. This study proposes a novel machine learning based approach for EV load forecasting in power supply systems facing high resource uncertainty. This method takes advantage of both Gradient Boosting Decision Tree (GBDT) algorithm and Time Convolutional Network (TCN) model. This study considers the service decisions of EV users and uses the GBDT algorithm to classify the EV discharge load dataset, with 92% accuracy. Also, the TCN model is used to capture local temporal features and predict the future C&D loads. In comparison with other baseline models, e.g. CNN-BILSTM, LSTM, PSO-BP, the stability of the TCN model is superior in real data charging load forecasting, and it is shown that the TCN model has the smallest error. The feasibility of the proposed GBDT-TCN hybrid model is verified by numerical cases,and achieves the RMSE of discharging forecasting less than 6.23%.

Application of Machine Learning Techniques to Improve Multi-Radar Mosaic Precipitation Estimates in Shanghai

In this study, we applied an explainable machine learning technique based on the LightGBM method, a category of gradient boosting decision tree algorithm, to conduct a quantitative radar precipitation estimation and move to understand the underlying reasons for excellent estimations. By introducing 3D grid radar reflectivity data into the LightGBM algorithm, we constructed three LightGBM models, including 2D and 3D LightGBM models. Ten groups of experiments were carried out to compare the performances of the LightGBM models with traditional Z–R relationship methods. To further assess the performances of the LightGBM models, rainfall events with 11,483 total samples during August-September of 2022 were used for statistical analysis, and two heavy rainfall events were specifically chosen for the spatial distribution evaluation. The results from both the statistical analysis and spatial distribution demonstrate that the performance of the LightGBM 3D model with nine points is the best method for quantitative precipitation estimation in this study. Through analyzing the explainability of the LightGBM models from Shapley additive explanations (SHAP) regression values, it can be inferred that the superior performance of the LightGBM 3D model is mainly attributed to its consideration of the rain gauge station attributes, diurnal variation characteristics, and the influence of spatial offset.