Bayesian Information Criterion Values Research Articles

Predicting Potential Evapotranspiration for Kalaburagi District using a Seasonal Arima Model

Forecasting potential evapotranspiration (PET) is of great importance in effectively managing irrigation systems. This article centers around models designed to simulate future PET levels for the Kalaburagi district. The study calculates potential evapotranspiration using temperature data in degrees Celsius, employing the Thornthwaite method, and prediction is performed using the Seasonal Autoregressive Moving Average (SARIMA) method. These models are developed based on autocorrelation function (ACF) and partial autocorrelation function (PACF) analysis. Model selection is based on minimizing Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) values. The chosen models for different stations in Kalaburagi, Chincholi, Sedam, Chittapur, Aland, Jewargi, and Afzalpur respectively are SARIMA (1,0,1)(2,1,0)12, SARIMA(1,0,1)(2,1,0)12, SARIMA(1,0,0)(2,1,0)12, SARIMA(1,0,1)(2,1,0)12, SARIMA (1,0,1) (2,1,0) 12, and SARIMA(1,0,1)(2,1,0)12. The results indicate that the models developed for Jewargi and Chincholi stations show particular promise compared to the other two stations, with all four models performing well. These models have the potential to significantly enhance decision-making in irrigation planning and command area management practices, contributing to improved water resource management.

Determinants of COVID-19 vaccine intention in a University population: a serial mediation approach

Abstract Background While many studies have used the Health Belief Model (HBM) to understand vaccination intention, few have explored the relationships between the HBM constructs. The aim of this study is to develop a serial mediation model dealing with latent variables to assess direct and indirect effects of the six HBM constructs (perceived susceptibility, severity, benefits, barriers, self-efficacy and cue to action) on COVID-19 vaccine intention. Methods From April to June 2021, a questionnaire on vaccine intention against COVID-19 was administered to staff and students at the University of Liège (Belgium). To evaluate direct and indirect effects of the HBM constructs on vaccine intention (score 0-100), serial mediation models for each latent variable permutation were assessed with Partial Least Squares Path Modeling (PLS-PM). Bayesian information criterion (BIC) was used to compare models. Internal consistency reliability and discriminant validity were evaluated. Sociodemographic variables, health literacy, psychological profile, body mass index, chronic disease and previous COVID-19 infection were included in the models as covariates. Results The sample consisted of 1256 participants. After running all permutation chains, the final causal chain, with the lowest BIC value, was barriers (-0.09 (-0.15 - -0.03))* - severity (-0.13 (-0.20 - -0.07))* - low self-efficacy (0.20 (0.15 - 0.25))* - low susceptibility (-0.55 (-0.60 - -0.51))* - vaccine intention (outcome). This highlighted a significant indirect and direct effect (-0.20 (-0.25 - -0.15))* between barriers and vaccine intention. Constructs benefits and cue to action were removed due to no significant path and weak reliability. Non-significant confounding factors were also removed. *estimate Conclusions The results showed that perceived barriers are a key determinant of COVID-19 vaccine resistance. Public health actors should communicate messages to remove barriers that reduce vaccine intention primarily. Key messages • Serial mediations allow a better understanding of how a vaccination intention works. • Public health communications should primarily focus on messages to remove barriers to get vaccinated.

A Non-Stationarity Analysis of Annual Maximum Floods: A Case Study of Campaspe River Basin, Australia

A design flood is an essential input for water infrastructure design and flood protection. A flood frequency analysis has been traditionally performed under stationarity assumption indicating that the statistical properties of historical flooding will not change over time. Climate change and variability challenges the stationarity assumption, and a flood frequency analysis without consideration of non-stationarity can result in under- or overestimation of the design floods. In this study, non-stationarity of annual maximum floods (AMFs) was investigated through a methodology consisting of trend and change point tests, and non-stationary Generalized Extreme Value (NSGEV) models, and the methodology was applied to Campaspe River Basin as a case study. Statistically significant decreasing trends in AMFs were detected for almost all stations at the 0.01 significance level in Campaspe River Basin. NSGEV models outperformed the stationary counterparts (SGEV) for some stations based on statistical methods (i.e., Akaike information criterion (AIC) and Bayesian information criterion (BIC)) and graphical approaches (i.e., probability and quantile plots). For example, at Station 406235, AIC and BIC values were found to be 334 and 339, respectively, for the SGEV model, whereas AIC and BIC values were calculated as 330 and 334, respectively, for the NSGEV 15 model with time-varying location and scale parameters. Deriving a design flood from conventional stationary models will result in uneconomical water infrastructure design and poor water resource planning and management in the study basin.

A New Statistical Distribution Derived from a Clayton Copula for Modeling Bivariate Processes

Abstract Rainfall and temperature extremes have become more frequent and severe in recent times due to changing climate. Since these catastrophic occurrences directly affect a region’s hydrology, it is imperative to develop models that can project and explain the joint behavior of climate variables. Copula functions have been used relatively successfully to capture multivariate processes. With climate being a multifaceted process, there is interdependence between variables, making copula use desirable since traditional bivariate distributions do not account for the dependent structure. In this study, we introduced a bivariate exponentiated Teissier distribution based on a Clayton copula. For parameter estimation, the maximum likelihood and inference functions for margin approaches are used. A simulation study that considered various sets of parameters is also conducted in order to select the most efficient parameter estimation method. Last, the applicability of the proposed model is demonstrated using real-world data from flood and temperature processes. After fitting, the log-likelihood, Akaike information criteria (AIC), and Bayesian information criteria (BIC) values of the proposed model are −145.00, 300.00, and 311.71 for flood data, respectively, and −128.71, 267.42, and 275.98 for temperature data, respectively. Estimated parameters are for flood data and for temperature data. It is concluded that this model may be effectively used for modeling the hydrological processes for calculating the probabilities of flood and extreme temperature events.

Reliability analysis of multi-stage degradation with stage-varying noises based on the nonlinear Wiener process

For multi-stage degradation processes, the degradation modeling and change-point detection play vital roles in the reliability assessment. The linear Wiener process with identical noise is mainly used to fit multi-stage degradation processes, which is often no consistent with practices, since many degradation processes are nonlinear and their noises are different in different stages. Furthermore, the maximum likelihood estimation method was often used to detect change-points from the entire process data, and it cannot detect the change-point timely. Therefore, considering the stage-varying noises, a degradation model based on the nonlinear Wiener process is proposed to characterize the multi-stage degradation process. Then, its nonlinear parameter is determined using the constrained optimization by linear approximations, and other parameters are estimated by the maximum likelihood estimation method. Note that under the same distribution, the Bayesian information criterion value of the observation data decreases with sample size, and a real-time adaptive change-point detection method is proposed by monitoring the change of Bayesian information criterion value. Finally, degradation processes of the battery and the wheel tread are analyzed to demonstrate the effectiveness of the proposed method.

A stacked generalisation methodology for estimating the uniaxial compressive strength of rocks

Uniaxial compressive strength (UCS) has become a highly essential strength parameter in the mining, civil and geomechanical industries. Estimating the exact value of the strength of rock has become a matter of great concern in real life. Despite this, there have been many works to indirectly/directly estimate the UCS of rocks. This study introduces a novel stacked generalisation methodology for estimating the UCS of rocks in geomechanics. In this study, generalised regression neural network (GRNN), radial basis function neural network (RBFNN), and random forest regression (RF) were used as the base learners and the multivariate adaptive regression spline (MARS) functioned as the meta-learner for the proposed stacking method. The proposed 3-Base learner stack model exhibited dominance over single applied AI methods of GRNN, RBFNN, and RF when confirmed with similar datasets by employing performance metrics like the Nash–Sutcliffe Efficiency Index (NSEI), Root Mean Squared Error (RMSE), Performance Index (PI), Scatter Index (SI) and Bayesian Information Criterion (BIC). The proposed 3-Base learner stack model scored the least RMSE, PI, and SI scores of 1.02775, 0.50691, and 0.00788 respectively for the testing datasets. In addition, it also produced the utmost NSEI value of 0.99969 and the least BIC value of 16.456 as likened to other competing models (GRNN, RBFNN and RF), reaffirming its power in forecasting the UCS of rocks in geomechanical engineering.

Analysis of occupational radiation dose data and determination of suitable probability distribution.

The first study on fitting dose data for workers was performed by Gale( 1) in 1965 where log-normal and normal distributions were used. Since then, various models of dose distribution have been proposed. The log-normal distribution and its different forms have been widely used for fitting the dose data. Most of the studies included one or two distributions under consideration. In this study five distributions are considered for fitting and four distributions are selected based on observation of Cullen-Frey graph. The Akaike's Information criteria (AIC) and Bayesian Information criteria (BIC) are applied to find the suitable distribution to fit the occupational dose data. The maximum likelihood method was used for calculation of AIC, BIC values and parameter estimation. A computer code is written in R-language and environment for statistical computing and graphics for analysis of occupational dose data of three institutions.

Modeling and forecasting of lifetime milk production in Hariana cattle

The present study was conducted on performance records of 648 Hariana cattle with respect to first lactation and lifetime traits spread over a period of 57 years (1962-2018) from DUVASU farm, Mathura and 23 years (1995– 2017) from State livestock farm Babugarh, Hapur. Lifetime production traits, viz. Lifetime 2 Milk Yield (LT2MY), Lifetime 3 Milk Yield (LT3MY), Lifetime 4 Milk Yield (LT4MY) and Lifetime 5 Milk Yield (LT5MY) were predicted by multiple linear regression analysis. Step-wise regression procedure was used to predict the lifetime milk yield from first lactation traits, viz. age at first calving (AFC), first calving interval (FCI) and first lactation total milk yield (FLTMY). The optimum models were selected with the help of Akaike information criterion (AIC), Bayesian information criterion (BIC), Mallow’s conceptual predictive value and variance inflation factor. The models with lower AIC, BIC and RMSE values and with higher co-efficient of determination were selected as optimum. The optimal models for lifetime production traits, viz. LT2MY, LT3MY, LT4MY and LT5MY with co-efficient of determination were 75.46%, 66.63%, 59.4% and 55.03%, respectively.

In a Digital Version of the 9-Hole Peg Test, Intra-Task Speed, Accounting for Variance in Distance Traveled, May be a Better Measure of MS-Related Upper-Limb Disability Than Completion Time. (P3-3.006)

In a Digital Version of the 9-Hole Peg Test, Intra-Task Speed, Accounting for Variance in Distance Traveled, May be a Better Measure of MS-Related Upper-Limb Disability Than Completion Time. (P3-3.006)

Validation of the prognostic scoring system for in-hospital mortality prediction in cardiogenic shock patients requiring veno-arterial extracorporeal membrane oxygenation.

A few prognostic scoring systems have been developed for predicting mortality in patients with cardiogenic shock requiring veno-arterial extracorporeal membrane oxygenation (VA-ECMO), albeit with variations in performance. This study aimed to assess and compare various mortality prediction models in a cohort of patients receiving VA-ECMO following cardiogenic shock or arrest. We retrospectively analyzed 77 patients with cardiogenic shock who were placed on VA-ECMO support between March 2014 and August 2021. The APACHE II, SAPS II, SAVE, Modified SAVE, ENCOURAGE, and ECMO-ACCEPTS scores were calculated for each patient to predict the in-hospital mortality. Fifty-six (72.7%) patients died. All prediction model scores, except the ECMO-ACCEPTS, differed significantly between non-survivors and survivors as follows: ENCOURAGE, 23 versus 16 (p < 0.001); SAVE, -6 versus -3 (p = 0.008); Modified SAVE, -5 versus 0 (p = 0.005); APACHE II, 32 versus 22 (p = 0.009); and SAPS II, 67 versus 49 (p = 0.002). The ENCOURAGE score demonstrated the best discriminatory ability with an area under the receiver-operating characteristic curve of 0.81 (95% confidence interval: 0.7-0.81). All prognostic scoring systems possessed limited calibration ability. However, the SAPS II, SAVE, and ENCOURAGE scores had lower Akaike and Bayesian information criteria values, which were consistent with the results of the Hosmer-Lemeshow C statistic test, indicating better performance than the other scores. The ENCOURAGE score can help predict in-hospital mortality in all subsets of VA-ECMO patients, even though it was originally designed to predict intensive care unit mortality in the post-acute myocardial infarction setting.

Accelerated Failure Time Models with Applications to Endometrial Cancer Survival Data

The objective of this study is to determine the significant predictors of endometrial cancer using accelerated failure time models (AFTM). We have demonstrated the applications of AFTM viz. Exponential, Weibull, Log-normal, Log-logistic, Gompertz, Gamma and Generalized Gamma AFTM, as an alternative of Cox proportional hazard model. Data for the analysis was collected from Acharya Harihar Post Graduate Institute of Cancer (AHPGIC), Cuttack, Odisha during the period 2016–20. Based on the lowest Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) value, the Weibull AFTM has been chosen as the best fitted AFT model. The predictors such as age, comorbidity, tumor size, isolated para-aortic and adnexa have been found as significant predictors (p-value &lt; 0.05) to explain the survival of endometrial cancer patients. Hence, by optimizing different treatments, based on such prognostic factors plays an important role in managing endometrial cancer at an early stage.

Applying Linear Forms of Pseudo-Second-Order Kinetic Model for Feasibly Identifying Errors in the Initial Periods of Time-Dependent Adsorption Datasets

Initial periods of adsorption kinetics play an important role in estimating the initial adsorption rate and rate constant of an adsorption process. Several adsorption processes rapidly occur, and the experimental data of adsorption kinetics under the initial periods can contain potential errors. The pseudo-second-order (PSO) kinetic model has been popularly applied in the field of adsorption. The use of the nonlinear optimization method to obtain the parameters of the PSO model can minimize error functions during modelling compared to the linear method. However, the nonlinear method has limitations in that it cannot directly recognize potential errors in the experimental points of time-dependent adsorption, especially under the initial periods. In this study, for the first time, the different linear types (Types 1–6) of the PSO model are applied to discover the error points under the initial periods. Results indicated that the fitting method using its linear equations (Types 2–5) is really helpful for identifying the error (doubtful) experimental points from the initial periods of adsorption kinetics. The imprecise points lead to low adjusted R2 (adj-R2), high reduced χ2 (red-χ2), and high Bayesian information criterion (BIC) values. After removing these points, the experimental data were adequately fitted with the PSO model. Statistical analyses demonstrated that the nonlinear method must be used for modelling the PSO model because its red-χ2 and BIC were lower than the linear method. Type 1 has been extensively applied in the literature because of its very high adj-R2 value (0.9999) and its excellent fitting to experimental points. However, its application should be limited because the potential errors from experimental points are not identified by this type. For comparison, the other kinetic models (i.e., pseudo-first-order, pseudo-nth-order, Avrami, and Elovich) are applied. The modelling result using the nonlinear forms of these models indicated that the fault experimental points from the initial periods were not detected in this study.

The Multimodal Extension of the Balakrishnan Alpha Skew Normal Distribution: Properties and Applications

This paper introduces a new class of Balakrishnan distribution by extending the multimodal skew-normal distribution proposed by Chakraborty et al. (2015). Statistical properties of the new family of distributions are studied in detail. In particular, explicit expressions of the density and distribution function, moments, skewness, kurtosis and the moments generating function are derived. Furthermore, estimation of the parameters using the maximum likelihood method of the new family of distributions is considered. Finally, the paper ends with an illustration of real-life data sets and then comparing the value of Akaike Information Criterion and Bayesian information criterion of the new distribution with some other known distributions. For the nested models, the Likelihood Ratio Test is carried out.

On method of regional non-stationary flood frequency analysis under the influence of large reservoir group and climate change

Global climate change and reservoir regulations can alter the natural flow of rivers. Influenced by these two drivers, flood sequences may no longer satisfy the assumption of stationary, thereby making it difficult to accurately analysis flood frequency and to design water conservancy projects. Therefore, it is of great significance to analyse the non-stationary frequency of flood sequences in a changing environment. In this study, we proposed a method for conducting nonstationary flood frequency analysis caused by cascade reservoirs as well as the low-frequency climate indices. The proposed non-stationary model 2, with the explanatory variables of climate indices and modified reservoir index (MRI), was compared with the traditional stationary model and the widely used non-stationary model 1 with time as the explanatory variable. The study was conducted at six hydrological stations in the main stream and tributaries of the upper reaches of the Yangtze River in China (considered as the Three Gorges Reservoir Area). The results of the generalized additive model for location, scale and shape (GAMLSS) showed that the Akaike information criterion and Bayesian information criterion values of the proposed non-stationary model method 2 are smaller than those of the two comparison models. When the low-frequency South Oscillation Index is high or the Arctic Oscillation and North Pacific Oscillation are low, the stationary model underestimates the design value of flood quantiles compared with the non-stationary model 2. Compared with the non-stationary model 1, the MRI and low-frequency climate indices as the explanatory variables in model 2 can better describe the non-stationary characteristics of flood frequency and amplitude. In addition, the non-stationary model considering external physical factors can provide better prediction of future design flood compared with two traditional models.

Survival and Duration Analysis of MSMEs in Chiang Mai, Thailand: Evidence from the Post-COVID-19 Recovery

This study attempts to reveal the consequences of coronavirus disease 2019 (COVID-19) on micro, small, and medium enterprises (MSMEs) in Chiang Mai, Thailand. A total of 786 MSMEs were surveyed during May and August 2022, corresponding to the period when the recovery of businesses and livelihoods from the ongoing COVID-19 crisis became more perceptible. The perceptions of COVID-19’s impact on MSMEs and their survivability are explored and investigated. To achieve this goal, a copula-based sample selection survival model is introduced. This idea of the model is extended from the concept of the Cox proportional hazards model and copula-based sample selection model, enabling us to construct simultaneous equations—namely, the probability-of-failure equation (selection equation) and the duration-of-survival equation (time-to-event or outcome equation). Several copula functions with different dependence patterns are considered to join the failure equation and the duration-of-survival equation. By comparing the Akaike and Bayesian information criteria values of the candidate copulas, we find that Farlie–Gumbel–Morgenstern (FGM) copula performs the best-fit joint function in our analysis. Empirically, the results from this best-fit model reveal that the survival probability of MSMEs in the next year is around 80%. However, some MSMEs may not survive more than three months after the interview. Finally, our results also reveal that the tourism MSMEs have a lower chance of survival than the commercial and manufacturing MSMEs. Notably, the business size and the support schemes from the government—such as the debt restructuring process, the tax payment deadline extension, and the reduced social security contributions—exhibited a role in lengthening the survival duration of the non-surviving MSMEs.

Does Internet development have a spatial fluctuation spillover effect on green total factor productivity in China? A Spatial-SAR-ARCH model

PurposeThe purpose of this study is to examine the spatial fluctuation spillover effect of green total factor productivity (GTFP) under the influence of Internet development.Design/methodology/approachUsing panel data from 283 cities in China for the period 2003–2016, this paper explores the spatial fluctuation spillover effect of internet development on GTFP by applying the spatial autoregressive with autoregressive conditional heteroscedasticity model (SARspARCH).FindingsThe results of Moran's I test of the residual term and the Bayesian information criterion (BIC) value indicate that the GTFP has a spatial fluctuation spillover effect, and the estimated results of the SARspARCH model are more accurate than the spatial autoregressive (SAR) model and the spatial autoregressive conditional heteroscedasticity (spARCH) model. Specifically, the internet development had a positive spatial fluctuation spillover effect on GTFP in 2003, 2011, 2012 and 2014, and the volatility spillover effect weakens the positive spillover effect of internet development on GTFP. Moreover, Internet development has a significant positive spatial fluctuation spillover effect on GTFP averagely in eastern China and internet-based cities.Research limitations/implicationsThe results of this study provide digital solutions for policymakers in improving the level of GTFP in China, with more emphasis on regional synergistic governance to ensure growth.Originality/valueThis paper expands the research ideas for spatial econometric models and provides a more valuable reference for China to achieve green development.

Forecasting Potential Evapotranspiration for Yadgir District Karnataka, India using Seasonal Arima Model

The prediction of potential evapotranspiration (PET) is quite important task for reliable management of irrigation systems. This article is generally based on the models which try to mimic the actual occurrence of the Potential evapotranspiration in the future days for Yadgir district. In this study the potential evapotranspiration was estimated with the help of max and min temperature (o C) data using a Thornthwaite method and the prediction was carried out using the seasonal Autoregressive moving average method (SARIMA). The models were developed based upon autocorrelation function (ACF) and partial autocorrelation function (PACF). Furthermore, the model with the least Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) values were selected. The models selected for different stations were ARIMA(2,0,2) (2,1,0)12, ARIMA(2,0,2)(2,1,0)12, ARIMA (1,0,1)(2,1,0)12, and ARIMA(2,0,2)(2,1,0)12, for Yadgir, Gurmitkal, Shahapur and Shorapur respectively. Furthermore, the results showed that the models developed for Gurmitkaland Shahapur were found to be quite promising compared to the other two stations. All four models were found to be producing better results. The models provided significant potential in improving the decision making in irrigation planning and command area management practices for better management of water resources.

Bayesian averaging sensitivity analysis of reservoir heterogeneity and anisotropy of carbon dioxide assisted gravity drainage of a large clastic oil reservoir

Identifying the specific reservoir properties that influence oil production is an essential part of field development planning. Distinguishing the relative influence of variables and eliminating those that have no influence assists the design of enhanced oil recovery processes. The conventional approach to identify the most influential variables is the multiple linear regression (MLR) step-wise elimination. Bayesian averaging (BA), a stochastic method, offers a novel alternative approach by generating fifty models compared to each other to select the best one with of the most influential parameters based on the highest R2 and posterior probability with the lowest value of Bayesian information criterion (BIC). BA and MLR step-wise elimination are compared in the evaluation of a simulated immiscible carbon dioxide-assisted gravity drainage (CO2-AGD) system applied to the multi-layered, heterogeneous, upper sandstone oil reservoir of the South Rumaila field. A history-matched compositional simulation covering the nearly 57 years of prior production is used for sensitivity analysis of reservoir variables in relation to CO2-AGD implementation over 10 future years with 22 additional CO2 injection wells and 11 new production wells. Oil is displaced downwards towards the new horizontal production wells installed above the oil water contact. The influential variables evaluated are permeability (K), permeability anisotropy (Kv/Kh) and porosity (ɸ) considered separately across multiple heterogeneous reservoir layers. BA and step-wise elimination methods identify K as the most influential variable in all reservoir layers. Kv/Kh is moderately influential in the production layers and underlying water zone but not in injection or transition reservoir layers. ɸ in all reservoir layers has no influence on oil recovery from the CO2-AGD producing wells. More specifically, the BA-based influential parameters were determined after selecting the best model among the fifty generated models. The best model achieved that highest values of R2 of 0.848 and posterior probability of 0.234, and the lowest value of BIC of −149 with 12 identified influential geological parameters across the reservoir layers. Based on validation tests, varying K, Kv/Kh. and ɸ substantially across all reservoir layers confirms these findings and agrees more closely with the BA-derived reduced variable models than those generated by MLR step-wise elimination.

Estimating the carbon dioxide emission levels of G7 countries: A count data approach

The G7 countries account for about 40% of the global gross domestic product and emit 25% of energy-related emissions due to their energy use, releasing so much carbon dioxide (CO2) into the atmosphere. This study uses competing count data models to model carbon emissions between 2002 and 2021. All the hypotheses are significant and with varied effects on the energy use of the G7 economies, backing the increasing levels of emissions. The findings show that emissions from manufacturing and construction have mixed effects on the energy use of the G7 countries, increasing by 2.37% and reducing by 6.65% and 8.39% in some countries. The study proves that the transport sector is the hard-to-abate sector as the effects of the transport covariate impact minimally between 6.49% and 4.13% of the G7. This means that the transport sector is the high-hanging fruit for deep decarbonization due to the low level of technology readiness. Also, emissions from solid fuels increase between 6.49% and 4.13% in the G7 countries, implying that coal consumption has peaked due to the current energy crisis. The cross-sectional dependence (CD) analysis proves a strong significant dependence among the study countries depicting the global nature of pollution. The fitness of the model was performed using the Akaike and Bayesian information criteria to determine the appropriate method to present robust and consistent results. The panel's negative Poisson regression model obtains the lowest Akaike information criteria and Bayesian information criteria values and is therefore the most appropriate model for the analysis. This will serve as a rallying point for achieving net zero emission (NZE) targets by mid-century and for scaling technologies to achieve that goal.

Arsenic removal from contaminated water utilizing novel green composite Chlorella vulgaris and nano zero-valent iron

Arsenic waste must be carefully managed because of the adverse effects of arsenic in wastewater on the ecosystem. In the present study, an environmentally friendly novel composite of Chlorella vulgaris microalgae and nano-zero valent iron (NZVI) was employed as an adsorbent to eliminate arsenic from the aqueous environment. Fourier Transform Infrared spectroscopy, X-ray diffraction, and scanning electron microscope images were used to characterize and analyze the CV/NZVI composites. Batch tests using initial arsenic concentrations ranging from 5 to 100 mg/L were conducted to evaluate removal efficiencies. According to kinetic analysis, the best model for fitting the experimental data was the pseudo first-order model, which had the lowest Akaike information criterion (AIC), and Bayesian information criterion (BIC) values of −23.878 and −7.902, respectively. Results alluded that physisorption is the primary mechanism influenced by As-removal by CV/NZVI composite. Due to the negative sign of the enthalpy and Gibbs free energy, the thermodynamic investigation revealed that the adsorption reaction was exothermic and spontaneous. The thermodynamic analysis also affirmed that the arsenic removal process involved primarily physisorption and slight chemisorption phenomena. Meanwhile, 1.5 g/L CV/NZVI dosage achieved 99 % As(V) removal efficiency in synthetic groundwater systems, confirming the high potential of the composite in complex aqueous systems.