Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam

Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak

• ML models for predicting SCM concretes compressive strength developed • Multiple datasets devised from a global dataset were used for training • Multiple ML models used in conjunction with multiple datasets for best results • Accurate prediction results obtained for all SCM concrete types studied • Prediction uncertainty bounds established by conformal prediction analysis Supplementary cementitious materials (SCM) used as partial replacements of cement can reduce carbon emissions and construction costs. The amount and types of SCM used directly influence the compressive strength of concrete, which is a crucial indicator of concrete performance. However, predicting compressive strength is challenging for SCM-based concretes due to their complex compositions, especially when multiple types of SCM are used. This study used five machine learning (ML) models, namely artificial neural network (ANN), random forest (RF), extra trees (ET), extreme gradient boosting (XGB), and adaptive boosting (AdaBoost) to predict the 28-day compressive strength of SCM concretes incorporating fly ash (FA), silica fume (SF), and ground granulated blast furnace slag (GGBFS). Firstly, a global dataset comprising 1456 data points was organized into seven individual and six combined datasets for training of the five ML models. After this, the performances of the ML models’ predictions using individual and combined datasets were compared using root mean squared error (RMSE) and coefficient of determination (R 2 ) to identify the optimal ML model-dataset configurations for each type of SCM-based concrete. It is found that in general, XGB models developed using the combined datasets demonstrated superior prediction accuracy, achieving an R² value exceeding 0.8. Both Shapley Additive Explanation (SHAP) and partial dependence plots (PDPs) were employed to illustrate the effects of input variables on compressive strength. Finally, uncertainty quantification using the Jackknife method combined with a normalized nonconformity measure was employed to provide accurate uncertainty bounds for compressive strength predictions.

Chronic pain around the temporomandibular joint (TMJ) and masticatory muscles is a primary symptom of temporomandibular disorders (TMD). However, the clinical significance of magnetic resonance imaging (MRI) features in predicting TMD-related pain remains unclear. This study aimed to develop and interpret machine learning (ML) models based on MRI characteristics for predicting pain severity in patients with TMD. The present retrospective study included 584 patients with TMD between January 2022 and December 2024, yielding a total of 755 TMJ MRI data sets. Pain severity was classified using the visual analogue scale (VAS). Demographic variables (age, sex) and MRI features-including lesion side, disc position, disc morphology, disc signal, disc perforation, bilaminar zone tear, joint space, joint effusion, condylar movement, bony changes and morphology/signal of the lateral pterygoid muscle-were collected. Eleven ML models based on demographic and MRI features were developed: logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), adaptive boosting (AdaBoost), gradient boosting classifier (GBC), bagging classifier (BC), extremely randomised trees (ETC), decision tree classifier (DTC) and multilayer perceptron (MLP). Model performance was evaluated using multiple metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity and F1 score. Precision-recall (PR) curves and calibration curves were plotted to assess discrimination and model calibration. Decision curve analysis (DCA) was conducted to evaluate the clinical net benefit across a range of threshold probabilities. Model interpretability was enhanced using Shapley Additive Explanations (SHAP), which quantified the contribution of each feature to individual predictions. Feature selection was conducted based on mean SHAP values, and separate LightGBM models were constructed using the Top 3, 5, and 9 most important features, as well as the full-feature set, for performance comparison. The data set was randomly divided into a training set (n = 604) and a test set (n = 151). Among the 11 ML models, the LightGBM model demonstrated the best predictive performance, with an AUC of 0.899, and was therefore identified as the optimal model. SHAP analysis identified age, disc position and condylar movement as the top three contributing features. Feature selection analysis indicated that selecting the top nine SHAP-ranked variables led to the highest diagnostic performance, with an AUC of 0.829. This study developed an interpretable, high-performing MRI-based ML model incorporating SHAP analysis to integrate imaging and clinical features for objective pain assessment, which may help identify high-risk TMD patients and guide personalised treatment strategies.

Shale caprocks, often organic-rich, play a significant role in hydrogen sequestration, but their wettability is typically altered from water-wet to H2-wet due to the organic matter, compromising their sealing performance. Silica nanofluids can restore water-wet conditions, which is vital for effective containment of H2 in geological formations. However, laboratory experiments to test the effects of nanomaterials on wettability and interfacial tension (IFT) are resource-intensive and time-consuming. To address this challenge, data-driven machine learning (ML) models offer a promising alternative, allowing for the prediction of key UHS parameters—such as advancing and receding contact angles without the need for extensive laboratory testing. We have applied multiple ML models, including gradient boosting, extreme gradient boosting (XGboost), Catboost, random forest, and extra trees to predict the wettability of organic rich shale samples treated with silica nanofluids. The ML models were trained on a comprehensive dataset generated through laboratory experiments conducted under a wide range of pressure (0.5 – 1600psi) and temperature (298-323 K) conditions, using brine (1 wt.% KCl + 2 wt.% NaCl) and varying silica nanofluid concentrations (0.05–1.0 wt.%). Advanced visualization techniques, including swarm maps, box plots, and heatmap plots, were utilized to analyze the experimental dataset thoroughly. To enhance the generalization capabilities of the ML models, we employed Bayesian optimization for hyperparameter tuning, along with k-fold cross-validation and grid-search optimization techniques. Both statistical and graphical analyses were performed to show the reliability and performance of the models. The results demonstrated that the implemented ML models, particularly the XGboost and Catboost, accurately predicted the wettability behavior under various operating conditions. The training and testing average R² scores were above 0.998 and 0.949, respectively, confirming the high accuracy of the predictions. These findings highlight the reliability of the ML algorithms in predicting the complex interaction between shale, brine, and H2. Further investigation into the feature importance revealed that nanomaterials had the greatest influence on the wettability of the ternary system. The silica nanofluid concentrations played a key role in modifying wettability, with varying impacts on the receding contact angles, emphasizing the effect of nanomaterial treatment. The model's predictions were consistent with experimental results, suggesting that geological conditions, including pressure and temperature, have a profound influence on rock mineral wettability, directly impacting H2 geo-storage capacity. This study is the first to employ data-driven ML models to predict critical UHS parameters in organic-rich shales treated with silica nanofluids. The ability of the ML models to predict UHS parameters across a wide range of conditions provides a valuable tool for optimizing UHS system designs, offering a more efficient and scalable approach compared to traditional experimental methods.

Summary A meticulous interpretation of steady-state or unsteady-state relative permeability (Kr) experimental data is required to determine a complete set of Kr curves. In this work, different machine learning (ML) models were developed to assist in a faster estimation of these curves from steady-state drainage coreflooding experimental runs. These ML algorithms include gradient boosting (GB), random forest (RF), extreme gradient boosting (XGB), and deep neural network (DNN) with a main focus on and comparison of the two latter algorithms (XGB and DNN). Based on existing mathematical models, a leading-edge framework was developed where a large database of Kr and capillary pressure (Pc) curves were generated. This database was used to perform thousands of coreflood simulation runs representing oil-water drainage steady-state experiments. The results obtained from these simulation runs, mainly pressure drop along with other conventional core analysis data, were used to estimate analytical Kr curves based on Darcy’s law. These analytically estimated Kr curves along with the previously generated Pc curves were fed as features into the ML model. The entire data set was split into 80% for training and 20% for testing. The k-fold cross-validation technique was applied to increase the model’s accuracy by splitting 80% of the training data into 10 folds. In this manner, for each of the 10 experiments, nine folds were used for training and the remaining fold was used for model validation. Once the model was trained and validated, it was subjected to blind testing on the remaining 20% of the data set. The ML model learns to capture fluid flow behavior inside the core from the training data set. In terms of applicability of these ML models, two sets of experimental data were needed as input; the first was the analytically estimated Kr curves from the steady-state drainage coreflooding experiments, while the other was the Pc curves estimated from centrifuge or mercury injection capillary pressure (MICP) measurements. The trained/tested model was then able to estimate Kr curves based on the experimental results fed as input. Furthermore, to test the performance of the ML model when only one set of experimental data is available to an end user, a recurrent neural network (RNN) algorithm was trained/tested to predict Kr curves in the absence of Pc curves as an input. The performance of the three developed models (XGB, DNN, and RNN) was assessed using the values of the coefficient of determination (R2) along with the loss calculated during training/validation of the model. The respective crossplots along with comparisons of ground truth vs. artificial intelligence (AI)-predicted curves indicated that the model is capable of making accurate predictions with an error percentage between 0.2% and 0.6% on history-matching experimental data for all three tested ML techniques. This implies that the AI-based model exhibits better efficiency and reliability in determining Kr curves when compared to conventional methods. The developed ML models by no means replace the need to conduct drainage coreflooding or centrifuge experiments but act as an alternative to existing commercial platforms that are used to interpret experimental data to predict Kr curves. The two main advantages of the developed ML models are their capability of predicting Kr curves within a matter of a few minutes as well as with limited intervention from the end user. The results also include a comparison between classical ML approaches, shallow neural networks, and DNNs in terms of accuracy in predicting the final Kr curves. The research presented here is an extension of the state-of-the-art framework proposed by Mathew et al. (2021). However, the two main aspects of the current study are the application of deep learning for the prediction of Kr curves and the application of feature engineering. The latter not only reduces the training/testing time for the ML models but also enables the end user to obtain the final predictions with the least set of experimental data. The various models discussed in this research work currently focus on the prediction of Kr curves for drainage steady-state experiments; however, the work can be extended to capture the imbibition cycle as well.

A study on road accident prediction and contributing factors using explainable machine learning models: analysis and performance

Accurately predicting thermophysical properties across various physical states is essential for both industrial and scientific applications. However, experimental data often exhibit variability and noise, requiring robust modeling approaches. In this work, we employ machine learning (ML) techniques to predict methane's thermophysical properties in liquid, vapor, and supercritical phases, including isobaric and isochoric heat capacities, density, specific volume, Joule-Thomson coefficients, enthalpies, sound speed, and shear viscosities, applying an approach recently developed [ACS Eng. Au 5, 226 (2025)10.1021/acsengineeringau.5c00001]. We explore various ML algorithms, including Adaptive Boosting, Bagging, Decision Trees, Extra Trees, Gradient Boosting, Histogram-based Gradient Boosting Regression Tree, K-Nearest Neighbors, Light Gradient Boosting Machine, Nu-Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Artificial Neural Networks. The ML models, which use raw experimental data without removing outliers, produce predictions that are closer to those obtained from the equations of state (EOS) employed by the National Institute of Standards and Technology than to the same raw experimental data used as input for developing the EOS. These results highlight ML's potential to identify and generalize complex patterns, smooth inherent noise, and manage the variability of different thermophysical properties. They indicate that ML models, particularly Extra Trees and Gradient Boosting, can offer a scalable alternative for thermophysical property predictions, offering consistency and efficiency over traditional methods. Although our approach does not eliminate preprocessing, it demonstrates that ML can effectively manage noisy data independently, offering a more efficient and cost-effective alternative to conventional pre- and postprocessing techniques.

Machine learning-based water quality prediction using octennial in-situ Daphnia magna biological early warning system data

Harmful Algal Blooms (HABs), driven by environmental pollution, pose significant threats to water quality, public health, and aquatic ecosystems. This study aims to enhance the prediction of HABs in Lake Erie, part of the Great Lakes system, by utilizing ensemble machine learning (ML) models coupled with explainable artificial intelligence (XAI) for interpretability. Using water quality data from 2013 to 2020, various physical, chemical, and biological parameters were analyzed to predict chlorophyll-a (Chl-a) concentrations, a proxy for algal blooms. The study employed multiple ensemble ML models, including Random Forest (RF), Deep Forest (DF), Gradient Boosting (GB), and XGBoost, and compared their performance against individual models such as Support Vector Machine (SVM), Decision Tree (DT), and Multi-Layer Perceptron (MLP). The findings reveal that ensemble models, particularly XGBoost and Deep Forest (DF), achieve superior predictive accuracy with R² values of 0.8517 and 0.8544, respectively. The application of SHapley Additive exPlanations (SHAP) provided insights into the relative importance of input features, identifying Particulate Organic Nitrogen (PON), Particulate Organic Carbon (POC), and Total Phosphorus (TP) as critical factors influencing Chl-a concentrations. This research demonstrates the effectiveness of integrating ensemble ML models with XAI to improve HAB prediction accuracy and interpretability. The results support the development of proactive water quality management strategies and highlight the potential of advanced ML techniques in environmental monitoring.

This study focuses on using various machine learning (ML) models to evaluate the shear behaviors of ultra-high-performance concrete (UHPC) beams reinforced with glass fiber-reinforced polymer (GFRP) bars. The main objective of the study is to predict the shear strength of UHPC beams reinforced with GFRP bars using ML models. We use four different ML models: support vector machine (SVM), artificial neural network (ANN), random forest (R.F.), and extreme gradient boosting (XGBoost). The experimental database used in the study is acquired from various literature sources and comprises 54 test observations with 11 input features. These input features are likely parameters related to the composition, geometry, and properties of the UHPC beams and GFRP bars. To ensure the ML models' generalizability and scalability, random search methods are utilized to tune the hyperparameters of the algorithms. This tuning process helps improve the performance of the models when predicting the shear strength. The study uses the ACI318M-14 and Eurocode 2 standard building codes to predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams. The ML models' predictions are compared to the results obtained from these building code standards. According to the findings, the XGBoost model demonstrates the highest predictive test performance among the investigated ML models. The study employs the SHAP (SHapley Additive exPlanations) analysis to assess the significance of each input parameter in the ML models' predictive capabilities. A Taylor diagram is used to statistically compare the accuracy of the ML models. This study concludes that ML models, particularly XGBoost, can effectively predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams.

This study presents the first application of Machine Learning (ML) models to optimise Powder Bed Fusion using Laser Beam (PBF-LB) process parameters for H13 steel fabricated on a 350 °C preheated building platform. A total of 189 cylindrical specimens were produced for training and testing machine learning (ML) models using variable process parameters: laser power (250-350 W), scanning speed (1050-1300 mm/s), and hatch spacing (65-90 μm). Eight ML models were investigated: 1. Support Vector Regression (SVR), 2. Kernel Ridge Regression (KRR), 3. Stochastic Gradient Descent Regressor, 4. Random Forest Regressor (RFR), 5. Extreme Gradient Boosting (XGBoost), 6. Extreme Gradient Boosting with limited depth (XGBoost LD), 7. Extra Trees Regressor (ETR) and 8. Light Gradient Boosting Machine (LightGBM). All models were trained using the Fast Library for Automated Machine Learning & Tuning (FLAML) framework to predict the relative density of the fabricated samples. Among these, the XGBoost model achieved the highest predictive accuracy, with a coefficient of determination R2=0.977, mean absolute percentage error MAPE = 0.002, and mean absolute error MAE = 0.017. Experimental validation was conducted on 27 newly fabricated samples using ML predicted process parameters. Relative densities exceeding 99.6% of the theoretical value (7.76 g/cm3) for all models except XGBoost LD and KRR. The lowest MAE = 0.004 and the smallest difference between the ML-predicted and PBF-LB validated density were obtained for samples made with LightGBM-predicted parameters. Those samples exhibited a hardness of 604 ± 13 HV0.5, which increased to approximately 630 HV0.5 after tempering at 550 °C. The LightGBM optimised parameters were further applied to fabricate a part of a forging die incorporating internal through-cooling channels, demonstrating the efficacy of machine learning-guided optimisation in achieving dense, defect-free H13 components suitable for industrial applications.

Modeling of necking area reduction of carbon steel in hydrogen environment using machine learning approach

Multiple sclerosis (MS) is a heterogeneous immune-mediated disorder of the central nervous system, and primary progressive MS (PPMS) is characterized by insidious injury independent of clinical relapses from disease onset. Predicting disease progression remains an unmet need across the MS disease spectrum. We evaluated statistical and machine learning (ML) models for predicting progression in 596 patients with PPMS from ORATORIO, a randomized, placebo (PBO)-controlled, phase 3 clinical study of ocrelizumab (OCR). Using baseline clinical, magnetic resonance imaging, and biomarker (neurofilament light chain [NfL]) data, we trained 6 ML models to predict a progression event (logistic regression, support vector machines, random forest, neural network, gradient boosting, and extreme gradient boosting) and the time to progression (Cox proportional hazards [PH]) on upper limb function, as measured by 20% confirmed worsening on the 9-hole peg test (9HPT). Predictive performance was similar across the six ML models (area under the curve = 0.63 to 0.67) and was modest using the Cox PH model (C-index = 0.76, an improvement over previous studies). In the Cox PH model, treatment arm, baseline 9HPT, neurologic functional system scores, and serum NfL levels were identified as factors associated with progression; model performance was similar across the OCR, PBO, and combined OCR + PBO treatment arms. Accounting for potential interactions with treatment arm resulted in similar predictive performance. To our knowledge, this is the first study to systematically evaluate ML models and statistical methods for predicting progression in PPMS using a well-characterized clinical trial cohort.

Development of a Machine Learning Algorithm for Prediction of Complications and Unplanned Readmission Following Reverse Total Shoulder Arthroplasty

Prediction of rockhead using a hybrid N-XGBoost machine learning framework