Comparative analysis of machine learning models for anomaly detection in manufacturing

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.

Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: A systematic review

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.

Artificial Intelligence (AI) in healthcare enables predicting diabetes using data-driven methods instead of the traditional ways of screening the disease, which include hemoglobin A1c (HbA1c), oral glucose tolerance test (OGTT), and fasting plasma glucose (FPG) screening techniques, which are invasive and limited in scale. Machine learning (ML) and deep neural network (DNN) models that use large datasets to learn the complex, nonlinear feature interactions, but the conventional ML algorithms are data sensitive and often show unstable predictive accuracy. Conversely, DNN models are more robust, though the ability to reach a high accuracy rate consistently on heterogeneous datasets is still an open challenge. For predicting diabetes, this work proposed a hybrid DNN approach by integrating a bidirectional long short-term memory (BiLSTM) network with a bidirectional gated recurrent unit (BiGRU). A robust DL model, developed by combining various datasets with weighted coefficients, dense operations in the connection of deep layers, and the output aggregation using batch normalization and dropout functions to avoid overfitting. The goal of this hybrid model is better generalization and consistency among various datasets, which facilitates the effective management and early intervention. The proposed DNN model exhibits an excellent predictive performance as compared to the state-of-the-art and baseline ML and DNN models for diabetes prediction tasks. The robust performance indicates the possible usefulness of DL-based models in the development of disease prediction in healthcare and other areas that demand high-quality analytics.

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

The aim of this research is maximizing differentiation quality of skin neoplasms by Raman Spectroscopy (RS) and Autofluorescence (AF) using conventional Machine Learning (ML) algorithms (which means excluding Neural Networks). Thus, a basic task of this research consists of making and testing ML algorithm ensembles on Raman spectra, that were obtained in vivo in Samara Oncology Clinical Center. The data (spectra) have been obtained in a form of text files containing the identifier of a patient, as well as a Raman spectrum in the form of pair values – a wavelength and an appropriate value. All data (964 spectra) has been divided for two classes: Tumor and Skin. Further preprocessing of the input data and the analysis of models of machine learning for a problem of classification have been carried out. We used the following ML tools, namely Python 3.7.3, an open source ML libraries Scikit-learn v0.21.2, NumPy 1.17.0, and Pandas 1.0.1, IDE Anaconda Enterprise 5.3 and cloud service Google Colaboratory with an interactive environment Jupyter Notebook. Machine learning models that show a high accuracy result include Classification and Regression Tree (CART), Support Vector Classification (SVC), Logistic Regression (LR), K-nearest neighbors algorithm (KNN). These classifiers show high quality classification on standard parameters already. The Soft Voting Classification module was selected as an ensemble, that allows us to use several models of classifiers that are not similar to each other at once, combine them into one classifier. Results of this ensemble testing from these ML algorithms showed that the classification accuracy, unlike the best qualifier, has not been increased. However, the metrics of classification quality show that the model has become stabler and steady. Results: specificity - 93%, sensitivity - 88, harmonic mean between precision and recall (F1 score) - 90%. An analysis of validation and training curves indicated a small size of training data and, for some cases, a high complexity of the model, which led to a decrease in the classification accuracy.

We aimed to evaluate the diagnostic performance of neuroimaging-based machine learning (ML) models for non-invasive prediction of H3K27M mutations in diffuse midline gliomas (DMG). Following PRISMA-DTA guidelines, we searched four databases up to May 2025 to identify eligible studies evaluating neuroimaging-based ML for predicting H3K27M mutations in DMG. Study quality was assessed using PROBAST+AI and GRADE. Bivariate random-effects models were used to pool sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Sixteen studies were included, comprising 2,357 patients in internal validation cohorts and 1,792 patients in external validation cohorts. MRI-based ML models showed strong diagnostic performance in internal validation, with pooled sensitivity of 0.86 (95% CI: 0.79-0.91), specificity of 0.82 (95% CI: 0.75-0.87), and AUC of 0.91 (95% CI: 0.88-0.93). For PET/CT-based ML models, pooled sensitivity was 0.58 (95% CI: 0.44-0.71) and pooled specificity was 0.65 (95% CI: 0.46-0.81), with an AUC of 0.61 (95% CI: 0.57-0.66). MRI-based ML showed significantly higher sensitivity (Z = 3.71; P < 0.01) and AUC (Z = 11.42; P < 0.01) than PET/CT-based ML. Subgroup analysis indicated that MRI-based deep learning (DL) models outperformed conventional machine learning (cML) algorithms (P = 0.01). Models using DNA sequencing as the reference standard showed higher specificity than those using immunohistochemistry (P < 0.001). MRI-based ML demonstrates high accuracy and generalizability for non-invasive H3K27M prediction in DMG, seemingly outperforming current PET/CT-based ML. The adoption of DL architectures and DNA sequencing as the reference standard may further improve performance, supporting the clinical utility of MRI-based ML for molecular stratification. https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=1078673, identifier CRD420251078673.

Understanding the Performance of Machine Learning Models to Predict Credit Default: A Novel Approach for Supervisory Evaluation

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.

Introduction While the majority of intracranial aneurysms (IAs) remain asymptomatic, rupture results in subarachnoid hemorrhage, a devastating event with high morbidity and mortality. Traditional rupture risk assessment relies on morphology, size, and location, yet these parameters lack individualized precision. Recent advances in machine learning (ML) and radiomics provide an opportunity to integrate high‐dimensional imaging features and clinical data to predict rupture risk with greater accuracy. This systematic review and meta‐analysis evaluates the performance of ML‐ and radiomics‐based approaches for IA rupture prediction. Methods We reviewed studies applying ML or radiomics for IA rupture risk prediction, including systematic reviews, retrospective datasets, and prospective validation studies. Eligible reports used angiographic imaging (CTA, MRA, or DSA) with reference standards of rupture status or clinical outcomes. Extracted data included sample size, sensitivity, specificity, and area under the curve (AUC). Random‐effects models were used to synthesize predictive performance. Subgroup analyses evaluated conventional ML algorithms, radiomics‐based models, and deep learning approaches. Results Four representative studies highlight the evolving field. A meta‐analysis by Akhtar et al. (2024) demonstrated pooled sensitivity and specificity of 83% for ML rupture prediction, with AUC ∼0.85. Stumpo et al. (2022) reported that ML models consistently outperformed conventional risk scores, achieving sensitivity and specificity near 80%. Hu et al. (2023), using digital subtraction angiography in 263 patients, achieved exceptional performance with an AUC of 0.982, sensitivity of 94.4%, and specificity of 97.5% in distinguishing ruptured from unruptured aneurysms. In contrast, Heo et al. (2020) leveraged a national claims dataset to develop population‐level ML models, reaching AUROC 0.765 for rupture prediction, demonstrating the feasibility of integrating epidemiological and imaging data. Pooled analysis across studies yielded an average AUC of 0.85 (95% CI: 0.80‐0.89), with sensitivity ∼0.85 and specificity ∼0.86. Notably, radiomics models incorporating texture, intensity, and morphological signatures demonstrated superior discrimination compared to size‐based parameters alone. However, performance was heterogeneous across datasets, and generalizability was limited by small sample sizes and retrospective designs. Conclusion Machine learning and radiomics approaches extend aneurysm assessment “beyond detection,” offering promising accuracy in rupture risk prediction. With pooled AUCs near 0.85 and select studies reporting &gt;0.95 performance, these models surpass traditional size‐based criteria. However, variability across cohorts and lack of standardized imaging features limit clinical translation. Future directions include prospective multicenter validation, integration of radiomics with clinical and hemodynamic markers, and development of explainable AI frameworks to ensure reproducibility. Ultimately, ML‐guided rupture risk stratification holds potential to personalize management and reduce subarachnoid hemorrhage incidence.

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

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.

Propofol is a widely used sedative-hypnotic agent for critically-ill patients requiring invasive mechanical ventilation (IMV). Despite its clinical benefits, propofol is associated with increased risks of hypertriglyceridemia. Early identification of patients at risk for propofol-associated hypertriglyceridemia is crucial for optimizing sedation strategies and preventing adverse outcomes. Machine learning (ML) models offer a promising approach for predicting individualized patient risks of propofol-associated hypertriglyceridemia. We propose the development of a ML model aimed at predicting the risk of propofol-associated hypertriglyceridemia in ICU patients receiving IMV. The study will utilize retrospective data from four Mayo Clinic sites. Nested cross-validation (CV) will be employed, with a 10-fold inner CV loop for model tuning and selection as well as an outer loop using leave-one-site-out CV for external validation. Feature selection will be conducted using Boruta and LASSO-penalized logistic regression. Data preprocessing steps include missing data imputation, feature scaling, and dimensionality reduction techniques. Six ML algorithms will be tuned and evaluated. Bayesian optimization will be used for hyperparameter selection. Global model explainability will be assessed using permutation importance, and local model explainability will be assessed using SHapley Additive exPlanations (SHAP). The proposed ML model aims to provide a reliable and interpretable tool for clinicians to predict the risk of propofol-associated hypertriglyceridemia in ICU patients. The final model will be deployed in a web-based clinical risk calculator. The model development process and performance measures obtained during nested cross-validation will be described in a study publication to be disseminated in a peer-reviewed journal. The proposed study has received ethics approval from the Mayo Clinic Institutional Review Board (IRB #23-007416). Robust external validation using a nested cross-validation (CV) framework will help assess the generalizability of models produced from the modeling pipeline across different hospital settings.A diverse set of machine learning (ML) algorithms and advanced hyperparameter tuning techniques will be employed to identify the most optimal model configuration.Integration of feature explainability will enhance the clinical applicability of the ML models by providing transparency in predictions, which can improve clinician trust and encourage adoption.Reliance on retrospective data may introduce biases due to inconsistent or erroneous data collection, and the computational intensity of the validation approach may limit replication and future model expansion in resource-constrained settings.

Highlights Estimating the moisture content (MC) of cereal grain and oilseed in real time is a challenge. We explored the accuracy of MC estimation with machine learning models when cereal grain and oilseed were dried with a microwave device. The frequency, attenuation, phase shift, and dielectric properties were utilized for model training. Out of five machine learning models, a custom DNN model performed the best, and the RF model was the next best-performing model. ABSTRACT. Estimating moisture content (MC) in cereal grains and oilseeds plays a major role in their quality control, ensuring safe storage and determining their market value. Conventional MC measurement methods, such as oven drying, are time-consuming and not suitable for continuous monitoring. This study focuses on evaluating various machine learning algorithms to estimate MC in cereal grains and oilseeds by analyzing their dielectric properties (dielectric constant, e', and dielectric loss factor, e"). Machine learning (ML) models, including a modified Deep Neural Network (DNN), Autoencoder, LeNet5, Support Vector Machine (SVM), and Random Forest (RF), were evaluated for MC determination. The dataset consisted of multiple cereal grain and oilseed types of oats, soybeans, sorghum, corn, barley, and wheat (hereafter referred to as “grains” for simplicity). The models were trained on three test sets: (I) only dielectric properties from all grains, (II) all measured physical properties from all grains, and (III) all measured physical properties from wheat. Results showed that the DNN outperformed other models on test sets I and III with an R 2 value of 0.9084 and 0.9995, respectively, whereas RF showed the best performance in test set II with an R 2 value of 0.9784. These results highlight the effectiveness of ML algorithms in estimating the MC level of grains accurately, providing an alternative to traditional methods and enabling real-time and non-destructive monitoring in agricultural processes. Keywords: Cereal grains, Dielectric properties, Machine learning, Microwave sensing, Moisture content, Oilseeds.

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.