Prediction Precision Of Model Research Articles

Variant pathogenicity prediction based on the ESGMM algorithm

Modeling the functional impact of sequence variation is a critical issue for both understanding and developing proteins. An Evolutionary Sequence and Gaussian Mixture Model (ESGMM) for predicting variant pathogenicity is presented in this paper. The model is trained on 2715 clinical proteins and their homologous sequences, using a Transformer-based protein language model to discover evolutionary patterns of amino acids from multiple sequence alignment (MSA). To fully mine deep information of MSA two-dimensional data, an axial attention mechanism is introduced during training. The model estimates the probability of all variants compared to the wild type and calculates variant scores. To categorize variations as pathogenic or benign, a global–local Gaussian mixture model is then constructed for each variant, and ESGMM scores are produced for each variant employing a combination of global and local information. Particle swarm optimization (PSO) is introduced to optimize the local Gaussian mixture model and further quantify the uncertainty of the classification, which enhances the model prediction precision. Experimental results demonstrate the superiority of the optimized ESGMM algorithm in predicting the pathogenicity of variants.

A robust ensemble of hybrid and bivariate statistical models for flood prediction mapping in Lower Damodar River Basin of India

This research explores flood prediction in the Lower Damodar River Basin (LDRB) using a hybrid ensemble of a Naïve Bayes Tree (NBT) and five bivariate statistical models such as Evidential Belief Function (EBF), Index of Entropy (IOE), Frequency Ratio (FR), Statistical Index (SI), and Modified Information Value (MIV). A total of 348 flood locations and 15 conditioning factors including hydrological, topographical and land cover were considered for this analysis. To ensure the precision of model predictions, a multicollinearity assessment was executed. Receiver operating characteristic (ROC) curve, area under curve (AUC), mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE) were performed to compare and asses each of the models all. The results reveal that all models performed well in creating flood hazard maps with AUC >0.8 and RMSE <0.4. FR and EBF models demonstrated the highest predictive accuracy (AUC=0.85), followed by the IOE, SI, and MIV models. The ensemble of NBT with bivariate models shows promising results, showcasing reduced error metrics and improved accuracy for the IOE, SI, and MIV models. This study highlights the potential of ensemble models in flood hazard prediction, offering valuable insights for global flood risk management. The successful application of these data-driven models showcases their importance in forecasting flood risks, aiding decision-makers and planners in developing more effective flood mitigation strategies.

Prediction of Oil–Water Two-Phase Flow Patterns Based on Bayesian Optimisation of the XGBoost Algorithm

With the continuous advancement of petroleum extraction technologies, the importance of horizontal and inclined wells in reservoir exploitation has been increasing. However, accurately predicting oil–water two-phase flow regimes is challenging due to the complexity of subsurface fluid flow patterns. This paper introduces a novel approach to address this challenge by employing extreme gradient boosting (XGBoost, version 2.1.0) optimised through Bayesian techniques (using the Bayesian-optimization library, version 1.4.3) to predict oil–water two-phase flow regimes. The integration of Bayesian optimisation aims to enhance the efficiency of parameter tuning and the precision of predictive models. The methodology commenced with experimental studies utilising a multiphase flow simulation apparatus to gather data across a spectrum of water cut rate, well inclination angles, and flow rates. Flow patterns were meticulously recorded via direct visual inspection, and these empirical datasets were subsequently used to train and validate both the conventional XGBoost model and its Bayesian-optimised counterpart. A total of 64 datasets were collected, with 48 sets used for training and 16 sets for testing, divided in a 3:1 ratio. The findings highlight a marked improvement in predictive accuracy for the Bayesian-optimised XGBoost model, achieving a testing accuracy of 93.8%, compared to 75% for the traditional XGBoost model. Precision, recall, and F1-score metrics also showed significant improvements: precision increased from 0.806 to 0.938, recall from 0.875 to 0.938, and F1-score from 0.873 to 0.938. The training accuracy further supported these results, with the Bayesian-optimised XGBoost (BO-XGBoost) model achieving an accuracy of 0.948 compared to 0.806 for the traditional XGBoost model. Comparative analyses demonstrate that Bayesian optimisation enhanced the predictive capabilities of the algorithm. Shapley additive explanations (SHAP) analysis revealed that well inclination angles, water cut rates, and daily flow rates were the most significant features contributing to the predictions. This study confirms the efficacy and superiority of the Bayesian-optimised XGBoost (BO-XGBoost) algorithm in predicting oil–water two-phase flow regimes, offering a robust and effective methodology for investigating complex subsurface fluid dynamics. The research outcomes are crucial in improving the accuracy of oil–water two-phase flow predictions and introducing innovative technical approaches within the domain of petroleum engineering. This work lays a foundational stone for the advancement and application of multiphase flow studies.

Leveraging QSP Models for MIPD: A Case Study for Warfarin/INR.

Warfarin dosing remains challenging due to substantial inter-individual variability, which can lead to unsafe or ineffective therapy with standard dosing. Model-informed precision dosing (MIPD) can help individualize warfarin dosing, requiring the selection of a suitable model. For models developed from clinical data, the dependence on the study design and population raises questions about generalizability. Quantitative system pharmacology (QSP) models promise better extrapolation abilities; however, their complexity and lack of validation on clinical data raise questions about applicability in MIPD. We have previously derived a mechanistic warfarin/international normalized ratio (INR) model from a blood coagulation QSP model. In this article, we evaluated the predictive performance of the warfarin/INR model in the context of MIPD using an external dataset with INR data from patients starting warfarin treatment. We assessed the accuracy and precision of model predictions, benchmarked against an empirically based reference model. Additionally, we evaluated covariate contributions and assessed the predictive performance separately in the more challenging outpatient data. The warfarin/INR model performed comparably to the reference model across various measures despite not being calibrated with warfarin initiation data. Including CYP2C9 and/or VKORC1 genotypes as covariates improved the prediction quality of the warfarin/INR model, even after assimilating 4 days of INR data. The outpatient INR exhibited higher unexplained variability, and predictions slightly exceeded observed values, suggesting that model adjustments might be necessary when transitioning from an inpatient to an outpatient setting. Overall, this research underscores the potential of QSP-derived models for MIPD, offering a complementary approach to empirical model development.

Bayesian relative composite quantile regression with ordinal longitudinal data and some case studies

ABSTRACT In real applied fields such as clinical medicine, environmental sciences, psychology as well as economics, we often encounter the task of conducting statistical inference for longitudinal data with ordinal responses. The traditional methods of longitudinal data analysis are often inclined to model continuous responses, which are no longer suitable for such ordinal data. Logistic regression and probit regression are two considerable methods which are frequently used to model ordinal longitudinal responses. However, such modelling methods just depict the mean feature of latent outcome variable and may produce non-robust results when encountering nor-normal errors or outliers. As a proper alternative of mean regression models, composite quantile regression (CQR) method is usually employed to derive robust estimation. The target of this paper is to investigate the CQR estimation approach for ordinal latent longitudinal model. The joint Bayesian hierarchical model is established and a relative CQR estimation approach is suggested to conduct posterior inference for the considered model. Further, in longitudinal data modelling, excessive predictors may be brought into in the models which result in the decrease of the model prediction precision. Bayesian L 1 / 2 regularized prior is incorporated into ordinal longitudinal CQR model to conduct variable selection simultaneously. Finally, simulation studies and two ordinal longitudinal data analysis are hired to illustrate the considered method.

Environmental Risk Assessment with Energy Budget Models: A Comparison Between Two Models of Different Complexity.

The extrapolation of effects from controlled standard laboratory tests to real environmental conditions is a major challenge facing ecological risk assessment (ERA) of chemicals. Toxicokinetic-toxicodynamic (TKTD) models, such as those based on dynamic energy budget (DEB) theory, can play an important role in filling this gap. Through the years, different practical TKTD models have been derived from DEB theory, ranging from the full "standard" DEB animal model to simplified "DEBtox" models. It is currently unclear what impact a different level of model complexity can have on the regulatory risk assessment. In the present study, we compare the performance of two DEB-TKTD models with different levels of complexity, focusing on model calibration on standard test data and on forward predictions for untested time-variable exposure profiles. The first model is based on the standard DEB model with primary parameters, whereas the second is a reduced version with compound parameters, based on DEBkiss. After harmonization of the modeling choices, we demonstrate that these two models can achieve very similar performances both in the calibration step and in the forward prediction step. With the data presented in the present study, selection of the most suitable TKTD model for ERA therefore cannot be based alone on goodness-of-fit or on the precision of model predictions (within current ERA procedures for pesticides) but would likely be based on the trade-off between ease of use and model flexibility. We also stress the importance of modeling choices, such as how to fill gaps in the information content of experimental toxicity data and how to accommodate differences in growth and reproduction between different data sets for the same chemical-species combination. Environ Toxicol Chem 2024;43:440-449. © 2023 ibacon GmbH. Bayer AG and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

IBGJO: Improved Binary Golden Jackal Optimization with Chaotic Tent Map and Cosine Similarity for Feature Selection.

Feature selection is a crucial process in machine learning and data mining that identifies the most pertinent and valuable features in a dataset. It enhances the efficacy and precision of predictive models by efficiently reducing the number of features. This reduction improves classification accuracy, lessens the computational burden, and enhances overall performance. This study proposes the improved binary golden jackal optimization (IBGJO) algorithm, an extension of the conventional golden jackal optimization (GJO) algorithm. IBGJO serves as a search strategy for wrapper-based feature selection. It comprises three key factors: a population initialization process with a chaotic tent map (CTM) mechanism that enhances exploitation abilities and guarantees population diversity, an adaptive position update mechanism using cosine similarity to prevent premature convergence, and a binary mechanism well-suited for binary feature selection problems. We evaluated IBGJO on 28 classical datasets from the UC Irvine Machine Learning Repository. The results show that the CTM mechanism and the position update strategy based on cosine similarity proposed in IBGJO can significantly improve the Rate of convergence of the conventional GJO algorithm, and the accuracy is also significantly better than other algorithms. Additionally, we evaluate the effectiveness and performance of the enhanced factors. Our empirical results show that the proposed CTM mechanism and the position update strategy based on cosine similarity can help the conventional GJO algorithm converge faster.

Proton Exchange Membrane Fuel Cell Power Prediction Based on Ridge Regression and Convolutional Neural Network Data-Driven Model

Research on the power prediction of proton exchange membrane fuel cells (PEMFCs) has garnered considerable attention. Because mainstream computational-fluid-dynamics-based methods are time-consuming, this study aimed to design a data-driven method based on Ridge regression (Ridge) and convolutional neural network (CNN) algorithms that can efficiently predict PEMFC power under uncertain conditions in real-world scenarios and reduce the time consumption. The measured data from a PEMFC test bench (3 kW) were collected as the data source for the model. First, we adopted Ridge to eliminate abnormal samples. Second, we analyzed and selected the variables that have a significant effect on PEMFC power. Moreover, we optimized the model using batch normalization, dropout, Nadam, Swish, and Huber techniques. Finally, the performance of the model was evaluated by combining real datasets and real polarization curves. The experimental results demonstrate that the polarization curves predicted by the CNN-based model agree with the real curves, with a prediction accuracy of approximately 0.96, a prediction time of 1 μs, and an iteration period of less than 1 s per cycle. A comparative analysis shows that the CNN-based model prediction precision was superior to that of other mainstream machine learning algorithms. In real scenarios, the CNN-based model accurately predicts the power of PEMFC.

ABiLSTM Based Prediction Model for AUV Trajectory

On 25 July 2021, the AUV of the Marine Science and Technology Research Center was lost under the sea due to a fracture of the wire rope when it was performing a mission offshore of China. A model is presented in the paper for predicting the trajectory of a lost AUV based on ABiLSTM. To increase the precision of model prediction, the model incorporates the soft attention mechanism and is based on the bidirectional Long Short-Term Memory (BiLSTM) network. In comparison to LSTM, BiLSTM, and attention-LSTM models, experiments have demonstrated that the proposed model enhanced prediction accuracy in terms of longitude, latitude, and altitude by 0.009° E, 0.008° N, and 2 m using representative root mean squared error as an assessment indicator. The findings of the study can improve marine rescue efforts and aid in the search and recovery of AUVs that have crashed.

A Bayesian network-GIS probabilistic model for addressing human disturbance risk to ecological conservation redline areas

Population growth and associated ecological space occupation are posing great risks to regional ecological security and social stability. In China, “Ecological Conservation Redline” (ECR) that prohibited urbanization and industrial construction has been proposed as a national policy to resolve spatial mismatches and management contradictions. However, unfriendly human disturbance activities (e.g., cultivation, mining, and infrastructure construction) still exist within the ECR, posing a great threat to ecological stability and safety. In this article, a Bayesian network (BN)-GIS probabilistic model is proposed to spatially and quantitatively address the human disturbance risk to the ECR at the regional scale. The Bayesian models integrate multiple human activities, ecological receptors of the ECR, and their exposure relationships for calculating the human disturbance risk. The case learning method geographic information systems (GIS) is then introduced to train BN models based on the spatial attribute of variables to evaluate the spatial distribution and correlation of risks. This approach was applied to the human disturbance risk assessment for the ECR that was delineated in 2018 in Jiangsu Province, China. The results indicated that most of the ECRs were at a low or medium human disturbance risk level, while some drinking water sources and forest parks in Lianyungang City possessed the highest risk. The sensitivity analysis result showed the ECR vulnerability, especially for cropland, that contributed most to the human disturbance risk. This spatially probabilistic method can not only enhance model's prediction precision, but also help decision-makers to determine how to establish priorities for policy design and conservation interventions. Overall, it presents a foundation for later ECR adjustments as well as for human disturbance risk supervision and management at the regional scale.

Tapping into Knowledge: Ontological Data Mining Approach for Detecting Cardiovascular Disease Risk Causes Among Diabetes Patients

The prevalence of cardiovascular disease (CVD) is a serious public health issue, and it is of particular concern for people with diabetes because of the increased risk of cardiovascular problems that these people experience. In this study, we suggest a novel method of Ontological Data Mining (ODM) for identifying the origins of CVD risk in diabetic patients. We want to improve the readability and precision of prediction models by incorporating domain knowledge and semantic linkages into the data mining process. In this work, we examine a large dataset consisting of 70,000 patient records with 11 attributes, all of which are derived through a thorough clinical history and physical examination. As part of our methodology, we use decision trees, support vector machines (SVMs), and gradient boosting (GB). The distribution patterns of critical variables with respect to CVD outcomes can be better understood through the use of visual representations such as box plots, distributional plots, and pie charts. Finding significant connections and causal relationships between risk factors and CVD outcomes is made possible by the suggested ODM method. Our research has promising implications for bettering the treatment of patients with diabetes, facilitating targeted interventions, and enhancing risk assessment and preventative methods for cardiovascular disease.

Modelling important areas for breeding waders as a tool to target conservation and minimise conflicts with land use change

Breeding waders are high profile species of conservation concern because of their declining populations. Their future depends on the outcomes of land-use policy and local management decisions. However, the low spatial resolution of extensive data on wader occurrence is poorly suited to directing conservation initiatives or to minimising detrimental impacts arising from land-use changes such as forest expansion. We used statistical models to produce high-resolution maps of predicted wader abundance in Britain and tested whether these were sufficiently accurate to be used for decision making. Random forest regression trees were developed using Bird Atlas data modelled with a range of environmental data sets to predict the relative abundances of ten species of breeding wader across Britain at 1-km square resolution. Similar analytical frameworks could usefully be applied in other geographical areas.Correlations with Bird Atlas metrics and other independent data indicate that these predictive models worked best for Curlew Numenius arquata and Oystercatcher Haematopus ostralegus. Model performance was poorer for species with more restricted distributions and/or habitat requirements, such as Greenshank Tringa nebularia and Ringed Plover Charadrius hiaticula. The precision of model predictions was also limited by the rescaling of data to 1-km square resolution.To facilitate practical and consistent interpretation by stakeholders influencing the status of breeding waders, model outputs were categorised into five discrete strata of relative abundance. These strata were used to produce sensitivity maps (available at https://app.bto.org/wader-map/), primarily to inform high-level policy decisions. The model outputs were also used to investigate broad-scale associations of breeding wader distribution with landscapes and land uses. These show that high proportions of populations (relative to their extent) are supported by areas with comparatively low predation risk (e.g. islands and grouse moors) and also by nature reserves. At more local scales, the sensitivity maps can be used to help decide on the levels of scrutiny required to assess development proposals (e.g. for establishment of new tree planting).

Application of multi-wavelength dual-position absorption spectrum to improve the accuracy of leukocyte spectral quantitative analysis based on “M + N” theory

Leukocytes are the most important immune cells in human body, which are very important to maintain the immune function of human body. They can phagocytose foreign bodies and produce antibodies to resist the invasion of pathogens. Nowadays, the abuse of antibiotics is widespread, and the detection and analysis of leukocytes is very important for clinical diagnosis. It is of great medical significance to use chemical quantitative analysis method based on spectrum to realize the rapid and trace detection of leukocytes in clinic. It is the development direction of clinical detection in the future and provides a new way to improve the abuse of antibiotics. However, due to the influence of nonlinearity introduced by the measurement, the relationship between absorbance and concentration deviates from Lambert-Beer law, which leads to low measurement accuracy and restricts its development in clinical application. In order to improve the accuracy of spectral analysis, this paper with the guidance of “M + N” theory measured the transmission spectra of 392 whole blood samples under two different optical path lengths, and subtracts them to obtain the multi-band differential absorption spectra for modeling and prediction of leukocyte concentration. The following experiments were designed: using the transmission spectra measured at one position and the absorption spectra obtained by subtracting the transmission spectra measured at position 1 and position 2 as the input of modeling. Partial least squares (PLS) method was proposed in this paper for modeling and predicting the concentration of leukocyte. The experimental results show that the modeling results of dual-position absorption spectrum have been significantly improved and promoted compared with the modeling results of transmission spectrum in one position, and the calibration set correlation coefficient (RC) values has increased by 57.92% to 0.864904, where the prediction set correlation coefficient (RP) increased by 106.81% to 0.8502. The root mean square error of the calibration set (RMSEC) decreased by 40.01% from 3.1149 to 1.8686. The results suggest that modelling and analysing leukocytes with a multi-band dual-position absorption spectrum may reduce the influence of nonlinearity to a certain amount, significantly increase the model's prediction precision and accuracy, and obtain satisfactory results. This paper provides the possibility for rapid clinical micro-detection of leukocytes, as well as the ideas and directions for improving the accuracy of spectral quantitative analysis of components in complex solutions.

Prediction of the intramuscular fat and protein content of freeze‐dried ground meat from cattle and sheep using near‐infrared spectroscopy (NIRS)

SummaryThis study assessed the ability of near‐infrared spectroscopy to predict intramuscular fat (IMF) and protein content of freeze‐dried ground beef and sheep meat determined using the modified Soxhlet extraction and Dumas combustion, and ascertain if robust calibration models could be developed for both assays using the combined spectral data from both species. Samples were sourced from sheep and cattle slaughtered for a variety of experiments, which covered a range in age, diet fed and carcase cut. All samples were scanned using a benchtop NIRS instrument to develop calibration models, a subset of which was used for later validation. Intramuscular fat and protein content of samples ranged from 5.1 to 32.8 and 63.4 to 89.8, respectively, with combined species of the R2 values of 98.4 and 96.5 (RMSE 0.74 and 0.76) for IMF and protein respectively. Additionally, animal age and carcase cut were found to have no significant impact on the precision of prediction models. Results from our study showed that individual species as well as combined calibration models can be successfully developed for predicting IMF and protein content of freeze‐dried ground beef and sheep meat.

Unified Modelling of Flow Stress and Microstructural Evolution of 300M Steel under Isothermal Tension

Constitutive models that reflect the microstructure evolution is of great significance to accurately predict the forming process of forging. Through thermal tension of 300M steel under various temperatures (950~1150 °C) and strain rates (0.01~10 s−1), the material flow and microstructure evolutions were investigated. In order to describe both the exponential hardening phenomenon at a higher temperature, and the softening phenomenon due to recrystallization at a lower temperature, a constitutive model considering microstructure evolution was proposed based on the Kocks–Mecking model. It was found that considering the stress-strain curve to be exponential in the work-hardening stage could improve the constitutive model prediction precision. The average error was 2.43% (3.59 MPa), showing that the proposed model was more precise than the modified Arrhenius model and the Kocks–Mecking model. The models to describe recrystallization kinetics and average grain size were also constructed. This work enabled the Kocks–Mecking model to predict stress-strain curves with a higher accuracy, and broadened the applicable range of the Kocks–Mecking model.

Investigation of the compatibility taper equations of Schima superba based on nonlinear measurement errors

Forest resource environment monitoring aims to observe, analyze, and evaluate both the number and quality of forest resources, which has aroused great attention by scholars from all over the world. Investigating stand growth condition is of great significance to the number estimation and management of forest resources. In order to more accurately estimate the increment and stand volume of Schima superba Gardn. et Champ., this study focused on forest survey data of Schima superba artificial forest and determined the optimal basic models of the DBH growth, the tree height curve, and the taper equation using the TOPSIS method with entropy weights, during which the coefficient of determination (R2), the model prediction precision (P (%)), the model accuracy (V), the absolute bias (AB), the root-mean-square error (RMSE), and the sum of squares of errors (SSE) were selected as the evaluation indexes. Then, the equations were combined using nonlinear measurement error method for parameter solving. Results show that the new model shows higher precision with remarkably enhanced evaluation indexes. Among the three optimal basic models, the V, AB, the RMSE, and the SSE decreased significantly. According to the paired t test results, the predicted values using the new model with measurement error model exhibit no significant difference from the measured data, suggesting good adaptivity. Moreover, the present results can more accurately calculate the diameter and stand volume of any part of Schima superba, provide the basis for calculating both biomass and carbon sequestration, and offer technical supports for formulating the merchantable volume table. Therefore, owing to the consideration of model parameter inaccuracy caused by the measurement errors in forest survey, the established equation performs well in compatibility and consistency. The present study can provide scientific theoretical basis and decision schemes for the forest environment monitoring and evaluation, forest management, etc.

Research on Chengdu air cargo forecast based on improved ARIMA-GARCH

With the transformation of China's economic growth mode and the upgrading of industrial structure, aviation logistics is becoming a strategic industry for China's economic and social development. Accurately predicting the dynamic trend of air cargo volume plays an important role in China's aviation logistics planning and construction, which will promote China's economic and social sustainable development. Due to the long-term trend, seasonal effect and uncertainty characteristics of air cargo volume, a single prediction model can not fit the trend of air cargo volume better, resulting in lower prediction accuracy. In this paper, based on the improved ARIMA-GARCH model, the Chengdu air cargo volume prediction model is established. In which, based on the ARIMA-GARCH model, the default white noise sequence in the GARCH model is replaced by the distribution estimated by the Bootstrap algorithm, thus improving the model's prediction precision.

Research on Chengdu air cargo forecast based on improved ARIMA-GARCH

With the transformation of China's economic growth mode and the upgrading of industrial structure, aviation logistics is becoming a strategic industry for China's economic and social development. Accurately predicting the dynamic trend of air cargo volume plays an important role in China's aviation logistics planning and construction, which will promote China's economic and social sustainable development. Due to the long-term trend, seasonal effect and uncertainty characteristics of air cargo volume, a single prediction model can not fit the trend of air cargo volume better, resulting in lower prediction accuracy. In this paper, based on the improved ARIMA-GARCH model, the Chengdu air cargo volume prediction model is established. In which, based on the ARIMA-GARCH model, the default white noise sequence in the GARCH model is replaced by the distribution estimated by the Bootstrap algorithm, thus improving the model's prediction precision.

Development of a non-linear model for prediction of higher heating value from the proximate composition of lignocellulosic biomass

ABSTRACT The determination of calorific value (in terms of higher heating value or HHV) is of much significance for the assessment of the energy recovery potential of biomass. The literature reported that HHV of the biomass can be computed using the proximate composition of the biomass. The prediction of HHV from the proximate composition of biomass is of great interest as it can save expensive and time-consuming laboratory trials. The present work evaluates the available prediction models for the estimation of HHV of the biomass from the literature. A thorough review showed that most literature reported models are limited in terms of applicability across different biomass categories. The present study proposed a non-linear prediction model that can be universally used to predict calorific value across biomass categories. Accordingly, the proposed model was validated using the proximate composition of various biomass specimens extracted from the literature to confirm the universal applicability of the model. The accuracy and precision of model prediction were assessed in terms of statistical indicators. The values of the statistical indicators showed that the proposed model had better prediction efficiency (in terms of accuracy and precision) and less bias compared to the other literature reported models for all biomass categories. The model predicted maximum energy recovery potential for biomass having high fixed carbon, high volatile matter, and low ash. The maximum energy recovery of 19.148 MJ/kg predicted by the model on validation was found to be associated with less than 2% (1.19%) percent error.

Spatial prediction of COVID-19 epidemic using ARIMA techniques in India.

The latest Coronavirus (COVID-19) has become an infectious disease that causes millions of people to infect. Effective short-term prediction models are designed to estimate the number of possible events. The data obtained from 30th January to 26 April, 2020 and from 27th April 2020 to 11th May 2020 as modelling and forecasting samples, respectively. Spatial distribution of disease risk analysis is carried out using weighted overlay analysis in GIS platform. The epidemiologic pattern in the prevalence and incidence of COVID-2019 is forecasted with the Autoregressive Integrated Moving Average (ARIMA). We assessed cumulative confirmation cases COVID-19 in Indian states with a high daily incidence in the task of time-series forecasting. Such efficiency metrics such as an index of increasing results, mean absolute error (MAE), and a root mean square error (RMSE) are the out-of-samples for the prediction precision of model. Results shows west and south of Indian district are highly vulnerable for COVID-2019. The accuracy of ARIMA models in forecasting future epidemic of COVID-2019 proved the effectiveness in epidemiological surveillance. For more in-depth studies, our analysis may serve as a guide for understanding risk attitudes and social media interactions across countries.