Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction

In this study, six machine learning (ML) models, namely, random forest (RF), Gaussian process regression (GPR), support vector regression (SVR), decision tree (DT), least squares support vector machine (LSSVM), and multivariate adaptive regression spline (MARS) models, were employed to reconstruct the missing daily‐averaged discharge in a mega‐delta from 1980 to 2015 using upstream‐downstream multi‐station data. The performance and accuracy of each ML model were assessed and compared with the stage‐discharge rating curves (RCs) using four statistical indicators, Taylor diagrams, violin plots, scatter plots, time‐series plots, and heatmaps. Model input selection was performed using mutual information and correlation coefficient methods after three data pre‐processing steps: normalization, Fourier series fitting, and first‐order differencing. The results showed that the ML models are superior to their RC counterparts, and MARS and RF are the most reliable algorithms, although MARS achieves marginally better performance than RF. Compared to RC, MARS and RF reduced the root mean square error (RMSE) by 135% and 141% and the mean absolute error by 194% and 179%, respectively, using year‐round data. However, the performance of MARS and RF developed for the climbing (wet season) and recession (dry season) limbs separately worsened slightly compared to that developed using the year‐round data. Specifically, the RMSE of MARS and RF in the falling limb was 856 and 1,040 m3/s, respectively, while that obtained using the year‐round data was 768 and 789 m3/s, respectively. In this study, the DT model is not recommended, while the GPR and SVR models provide acceptable results.

Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia

Background and purpose — External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines. Material and methods — We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting. Results — We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43–89), with 6 items being reported in less than 4/18 of the studies. Interpretation — Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.

<div class="section abstract"><div class="htmlview paragraph">In recent years, diesel engine emissions regulations have been strengthened worldwide, necessitating the evaluation of regulatory values under transient conditions. Consequently, the need to assess transient states in the development of diesel engines has increased significantly. The evaluation using MBD (Model Based Development) is considered a promising method for achieving both low fuel consumption and simultaneous reduction of NOx and soot emissions. However, the mechanism of soot formation is complex, making it challenging to model mathematically directly. In this paper, hybrid machine learning approaches combining a physical model and a machine learning model are used to validate the prediction of soot emissions under transient conditions in a diesel engine with an EGR system. Various parameters such as fuel consumption and emissions predicted by the physical model are compared with measurements to validate the accuracy of the physical model. The prediction of soot emissions by the physical model is based on the Hiroyasu model. From these results, it is demonstrated that the physical model has sufficient accuracy to be used in hybrid machine learning approaches. However, it is shown that the physical model is inadequate as a prediction approach for soot emissions. Gaussian Process Regression (GPR), Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT) are used to develop the machine learning models, and each model is trained on data under steady-state conditions. The prediction accuracy of each model and the physical model is compared and validated. The results show that the hybrid machine learning approaches have higher predictive accuracy than the physical model for soot emissions predictions in both steady-state and transient conditions. The GPR model with the highest prediction accuracy shows a test R<sup>2</sup> of 0.87 under steady-state conditions and relative errors with the measured values of less than 10% for both Non-load Transient Cycle (NRTC) and Low Load Cycle (LLC), which are engine test cycles.</div></div>

This study highlights drought forecasting for understanding the semi-arid area in India, where drought phenomena play vital role in the irrigation, drinking water supplies, and sustaining the ecological with economic balance for every nation. Therefore, drought forecasting is important for the future drought planning based on the machine learning (ML) models. Hence, The Standardized Precipitation Index (SPI) at 3- and 6-month periods have been selected and used for future drought forecasting scenarios in area. The combinations of ten inputs SPI-1- and SPI-10 were used for predicting modeling for SPI-3 and SPI-6 timescales, that modeling developed based on the historical SPI datasets from 1989 to 2019 years. The SPI-3 and SPI-6 maximum and minimum values are shown SPI-3 (2.03 and -5.522) and SPI-6 (1.94 and -6.93). The SPI is a popular method for estimating the drought analysis and has been used everywhere at global level. The developed models have been compared with each other, with the best combination of input variables selected using subset regression models and sensitivity studies. After that, the active input parameters were used for forecasting of SPI-3 and SPI-6 values to understanding of drought in semi-arid area. The finest input variables combination have been used in the Ml models and established the novel five models such as robust linear regression, bagged trees, boosted trees, support vector regression (SVM-Linear), and Matern Gaussian Process Regression (Matern GPR) models. Such kind of models first time has been applied for the forecasting of future drought conditions. Whole models were fine and improved modeling by using hyperparameters tuning, bagging, and boosting models. Entire ML models’ accuracy was compared using different statistical metrics. Compared with five ML models accuracy, we have found that the Matern GPR model better accuracy than other ML models. The best model accuracy is R2 = 0.95 and 0.93, RMSE, MSE, MAE, MARE, and NSE values, respectively, for predicting SPI-3 and SPI-6 values in the area. Therefore, the Matern GPR model was identified as the finest ML algorithm for predicting SPI-3 and SPI-6 associated with other algorithms. This research demonstrates the Matern GPR model's efficacy in predicting multiscale SPI-3 and SPI-6 under climate variations. It can be helpful in soil and water resource conservation planning and management and understanding droughts in the entire basin areas of the country India.

BackgroundIn patients with suspected obstructive coronary artery disease (CAD), evaluation using a pre-test probability model is the key element for diagnosis; however, its accuracy is controversial. This study aimed to develop machine learning (ML) models using clinically relevant biomarkers to predict the presence of stable obstructive CAD and to compare ML models with an established pre-test probability of CAD models.MethodsEight machine learning models for prediction of obstructive CAD were trained on a cohort of 1,312 patients [randomly split into the training (80%) and internal validation sets (20%)]. Twelve clinical and blood biomarker features assessed on admission were used to inform the models. We compared the best-performing ML model and established the pre-test probability of CAD (updated Diamond-Forrester and CAD consortium) models.ResultsThe CatBoost algorithm model showed the best performance (area under the receiver operating characteristics, AUROC, 0.796, and 95% confidence interval, CI, 0.740–0.853; Matthews correlation coefficient, MCC, 0.448) compared to the seven other algorithms. The CatBoost algorithm model improved risk prediction compared with the CAD consortium clinical model (AUROC 0.727; 95% CI 0.664–0.789; MCC 0.313). The accuracy of the ML model was 74.6%. Age, sex, hypertension, high-sensitivity cardiac troponin T, hemoglobin A1c, triglyceride, and high-density lipoprotein cholesterol levels contributed most to obstructive CAD prediction.ConclusionThe ML models using clinically relevant biomarkers provided high accuracy for stable obstructive CAD prediction. In real-world practice, employing such an approach could improve discrimination of patients with suspected obstructive CAD and help select appropriate non-invasive testing for ischemia.

Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.

PD27-01 DEVELOPMENT OF A MACHINE LEARNING (ML) MODEL TO AUTOMATICALLY AND PRECISELY IDENTIFY KIDNEY STONES FROM URETEROSCOPY VIDEO RECORDINGS

12074 Background: Skeletal-related events (SREs) are detrimental clinical events in bone metastasis from advanced cancer, defined by pathologic fracture, spinal cord compression, and inevitable surgical or radiational intervention to the bone. Given their negative impact on quality of life and prognosis and interpatient heterogeneity in the SRE risk, accurate identification of patients with high SRE risk is critical. Methods: The patient-level data from three randomized clinical trials that administered zoledronic acid (ZA) to patients with bone-metastatic breast cancer, castration-resistant prostate cancer (CRPC), and other types of cancer were analyzed. (N = 460, 452, and 315, respectively) Machine learning (ML) models to predict SREs within 18 months (breast cancer), 12 months (CRPC), and 9 months (other cancers) were developed based on more than 40 baseline clinical and laboratory data. Seven ML algorithms and five feature selection methods were utilized to develop multiple models. The ML model with the best performance was identified based on the F1 score and the area under the receiver operating characteristic curve (AUC-ROC) for each cancer type and interrogated for important features with Shapley additive explanations. Lastly, the ML models’ ability to stratify patients by the cumulative SRE risk was evaluated by calculating hazard ratio (HR) with Cox-proportional hazards models. Results: Among the multiple ML models developed with different algorithms and feature selection methods, the model developed utilizing the random forests algorithm and the Boruta method for selecting features demonstrated the best performance in all types of cancer (F1 0.70, 0.67, and 0.67, and AUC-ROC 0.72, 0.68, and 0.73 for breast cancer, CRPC, and other cancers, respectively). In the ML model for breast cancer, performance status (PS), history of SRE, serum alkaline phosphatase (ALP), history of anti-neoplastic surgery, radiation therapy, and pathologic fracture were included as important features. Serum ALP, albumin, sodium, Gleason scores, and geographic regions were shown to be relevant in the CRPC model. For other cancers, serum ALP, albumin, total protein, phosphorus, red blood cell count, white blood cell count, visceral metastases, and history of arthritis were incorporated in the ML model. The ML model prediction successfully stratified the patients for cumulative SRE risk in all three cohorts. (HR with 95% confidence interval: 2.43 [1.86 – 3.18], 1.92 [1.51 – 2.45], and 3.06 [2.29 – 4.09] for breast cancer, CRPC, and other types of cancer, respectively). Conclusions: ML models incorporating baseline clinical and laboratory data can identify patients with bone metastasis on ZA harboring a high SRE risk.

The sea surface temperature (SST) is an environmental indicator closely related to climate, weather, and atmospheric events worldwide. Its forecasting is essential for supporting the decision of governments and environmental organizations. Literature has shown that single machine learning (ML) models are generally more accurate than traditional statistical models for SST time series modeling. However, the parameters tuning of these ML models is a challenging task, mainly when complex phenomena, such as SST forecasting, are addressed. Issues related to misspecification, overfitting, or underfitting of the ML models can lead to underperforming forecasts. This work proposes using hybrid systems (HS) that combine (ML) models using residual forecasting as an alternative to enhance the performance of SST forecasting. In this context, two types of combinations are evaluated using two ML models: support vector regression (SVR) and long short-term memory (LSTM). The experimental evaluation was performed on three datasets from different regions of the Atlantic Ocean using three well-known measures: mean square error (MSE), mean absolute percentage error (MAPE), and mean absolute error (MAE). The best HS based on SVR improved the MSE value for each analyzed series by 82.26%, 98.93%, and 65.03% compared to its respective single model. The HS employing the LSTM improved 92.15%, 98.69%, and 32.41% concerning the single LSTM model. Compared to literature approaches, at least one version of HS attained higher accuracy than statistical and ML models in all study cases. In particular, the nonlinear combination of the ML models obtained the best performance among the proposed HS versions.

This paper presents a data-driven approach to determine the load and flexural capacities of reinforced concrete (RC) beams strengthened with fabric reinforced cementitious matrix (FRCM) composites in flexure. A total of seven machine learning (ML) models such as kernel ridge regression, K-nearest neighbors, support vector regression, classification and regression trees, random forest, gradient boosted trees, and extreme gradient boosting (xgBoost) are evaluated to propose the best predictive model for FRCM-strengthened beams. Beam geometry, internal steel reinforcement area, FRCM reinforcement area, and mechanical characteristics of concrete, steel, and FRCM are the main input parameters included in the database. Among the studied ML models, the xgBoost model is the most accurate model with the highest coefficient of determination (R2=99.3%) and least root mean square (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). A comparative study of the performance of the proposed and existing analytical models revealed the superior predictive capability and robustness of the proposed model. The predicted flexural and load capacities of the beams based on the existing analytical models are highly scattered and either over-conservative or unsafe. A unified SHapley Additive exPlanations approach is employed to explain the output of the best ML model and identify the most significant input features and interactions that influence the capacity of FRCM-strengthened RC beams in flexure. Furthermore, a reliability analysis is performed to calibrate the resistance reduction factor (ϕ) to achieve a specified target reliability index (βT=3.5).

Thermogravimetric analysis (TGA) was utilised to compare the thermal stability of pure phase change material (D-mannitol) to that of nano-enhanced PCM (NEPCM) (i.e., PCM containing 0.5% and 1% multiwalled carbon nanotubes (MWCNT)). Using model-free kinetics techniques, the kinetics of pure PCM and NEPCM degradation were analysed. Three different kinetic models such as Kissinger-Akahira-Sunose (KAS), the Flynn-Wall-Ozawa (FWO), and the Starink were applied to assess the activation energies of the pure and nano-enhanced PCM samples. Activation energies for pure PCM using the Ozawa, KAS, and Starink methods ranged from 71.10–77.77, 79.36–66.87, and 66.53–72.52 kJ/mol, respectively. NEPCM’s (1% MWCNT) activation energies ranged from 76.59–59.11, 71.52–52.28, and 72.15–53.07 kJ/mol. Models of machine learning were utilised to predict the degradation of NEPCM samples; these included linear regression, support vector regression, random forests, gaussian process regression, and artificial neural network models. The mass loss of the sample functioned as the output parameter, while the addition of nanoparticles weight fraction, the heating rate, and the temperature functioned as the input parameters. Experiment-based TGA data can be accurately predicted using the created machine learning models.

BackgroundAcute coronary syndromes (ACS) are the leading cause of global death. Optimizing mortality risk prediction and early identification of high-risk patients is essential for developing targeted prevention strategies. Many researchers have built machine learning (ML) models to predict the mortality risk in ACS patients. Our meta-analysis aimed to evaluate the predictive value of various ML models in predicting death in ACS patients at different times.MethodsPubMed, Embase, Web of Science, and Cochrane Library were searched systematically from database establishment to March 12, 2022 for studies developing or validating at least one ML predictive model for death in ACS patients. We used PROBAST to assess the risk of bias in the reported predictive models and a random-effects model to assess the pooled C-index and accuracy of these models.ResultsFifty papers were included, involving 216 ML prediction models, 119 of which were externally validated. The combined C-index of the ML models in the validation cohort predicting the in-hospital mortality, 30-day mortality, 3- or 6-month mortality, and 1 year or above mortality in ACS patients were 0.8633 (95% CI 0.8467–0.8802), 0.8296 (95% CI 0.8134–0.8462), 0.8205 (95% CI 0.7881–0.8541), and 0.8197 (95% CI 0.8042–0.8354), respectively, with the corresponding combined accuracy of 0.8569 (95% CI 0.8411–0.8715), 0.8282 (95% CI 0.7922–0.8591), 0.7303 (95% CI 0.7184–0.7418), and 0.7837 (95% CI 0.7455–0.8175), indicating that the ML models were relatively excellent in predicting ACS mortality at different times. Furthermore, common predictors of death in ML models included age, sex, systolic blood pressure, serum creatinine, Killip class, heart rate, diastolic blood pressure, blood glucose, and hemoglobin.ConclusionsThe ML models had excellent predictive power for mortality in ACS, and the methodologies may need to be addressed before they can be used in clinical practice.

Stream temperature (Ts) is an important water quality parameter that affects ecosystem health and human water use for beneficial purposes. Accurate Ts predictions at different spatial and temporal scales can inform water management decisions that account for the effects of changing climate and extreme events. In particular, widespread predictions of Ts in unmonitored stream reaches can enable decision makers to be responsive to changes caused by unforeseen disturbances. In this study, we demonstrate the use of classical machine learning (ML) models, support vector regression and gradient boosted trees (XGBoost), for monthly Ts predictions in 78 pristine and human-impacted catchments of the Mid-Atlantic and Pacific Northwest hydrologic regions spanning different geologies, climate, and land use. The ML models were trained using long-term monitoring data from 1980–2020 for three scenarios: (1) temporal predictions at a single site, (2) temporal predictions for multiple sites within a region, and (3) spatiotemporal predictions in unmonitored basins (PUB). In the first two scenarios, the ML models predicted Ts with median root mean squared errors (RMSE) of 0.69–0.84 °C and 0.92–1.02 °C across different model types for the temporal predictions at single and multiple sites respectively. For the PUB scenario, we used a bootstrap aggregation approach using models trained with different subsets of data, for which an ensemble XGBoost implementation outperformed all other modeling configurations (median RMSE 0.62 °C).The ML models improved median monthly Ts estimates compared to baseline statistical multi-linear regression models by 15–48% depending on the site and scenario. Air temperature was found to be the primary driver of monthly Ts for all sites, with secondary influence of month of the year (seasonality) and solar radiation, while discharge was a significant predictor at only 10 sites. The predictive performance of the ML models was robust to configuration changes in model setup and inputs, but was influenced by the distance to the nearest dam with RMSE <1 °C at sites situated greater than 16 and 44 km from a dam for the temporal single site and regional scenarios, and over 1.4 km from a dam for the PUB scenario. Our results show that classical ML models with solely meteorological inputs can be used for spatial and temporal predictions of monthly Ts in pristine and managed basins with reasonable (<1 °C) accuracy for most locations.

Over the past few decades, the construction industry has been suffering from project delays. It remains a recognized challenge to accurately predict the actual project’s progress, despite the vast amount of field data and scheduling methods available. In this paper, four different machine learning (ML) models including K-nearest neighbor (KNN), support vector regression (SVR), gradient boosting trees (GBT), and artificial neural network (ANN) were used for forecasting the construction duration of work areas. Field data were collected, including progress, workload, labor, weather, planned days, and location, as inputs and construction days as output for each work area to develop the models. A simple and widely used baseline model was used to confirm the accuracy and generalization capability of ML models. The comparison of the results revealed that ANN and GBT models produced significantly superior results. Additionally, the GBT model is substantially more computationally efficient. In conclusion, the GBT model could be successfully and effectively applied to improve the prediction of construction duration. The proposed ML models could be utilized as a decision support tool for construction project managers to adjust construction site resource allocation.