Bayesian Inference Research Articles

Comparison of Bayesian and frequentist monitoring boundaries motivated by the Multiplatform Randomized Clinical Trial.

The coronavirus disease 2019 pandemic highlighted the need to conduct efficient randomized clinical trials with interim monitoring guidelines for efficacy and futility. Several randomized coronavirus disease 2019 trials, including the Multiplatform Randomized Clinical Trial (mpRCT), used Bayesian guidelines with the belief that they would lead to quicker efficacy or futility decisions than traditional "frequentist" guidelines, such as spending functions and conditional power. We explore this belief using an intuitive interpretation of Bayesian methods as translating prior opinion about the treatment effect into imaginary prior data. These imaginary observations are then combined with actual observations from the trial to make conclusions. Using this approach, we show that the Bayesian efficacy boundary used in mpRCT is actually quite similar to the frequentist Pocock boundary. The mpRCT's efficacy monitoring guideline considered stopping if, given the observed data, there was greater than 99% probability that the treatment was effective (odds ratio greater than 1). The mpRCT's futility monitoring guideline considered stopping if, given the observed data, there was greater than 95% probability that the treatment was less than 20% effective (odds ratio less than 1.2). The mpRCT used a normal prior distribution that can be thought of as supplementing the actual patients' data with imaginary patients' data. We explore the effects of varying probability thresholds and the prior-to-actual patient ratio in the mpRCT and compare the resulting Bayesian efficacy monitoring guidelines to the well-known frequentist Pocock and O'Brien-Fleming efficacy guidelines. We also contrast Bayesian futility guidelines with a more traditional 20% conditional power futility guideline. A Bayesian efficacy and futility monitoring boundary using a neutral, weakly informative prior distribution and a fixed probability threshold at all interim analyses is more aggressive than the commonly used O'Brien-Fleming efficacy boundary coupled with a 20% conditional power threshold for futility. The trade-off is that more aggressive boundaries tend to stop trials earlier, but incur a loss of power. Interestingly, the Bayesian efficacy boundary with 99% probability threshold is very similar to the classic Pocock efficacy boundary. In a pandemic where quickly weeding out ineffective treatments and identifying effective treatments is paramount, aggressive monitoring may be preferred to conservative approaches, such as the O'Brien-Fleming boundary. This can be accomplished with either Bayesian or frequentist methods.

Efficacy of different strategies for reducing labor pain: A Bayesian analysis.

The effectiveness of different medical interventions in managing labor pain has yielded mixed results. Therefore, this systematic review and network meta-analysis aimed to provide a comprehensive summary of the available evidence on the impact of different strategies for reducing labor pain. We systematically searched 3 English databases (Pubmed, Embase and the Cochrane Library) from inception to January 2023. Randomized controlled trials (RCTs) or controlled clinical trials that investigating the effects of different strategies for reducing labor pain were included in this network meta-analysis. Risk of bias (ROB) was assessed using the Cochrane ROB tools. Network meta-analysis was performed using the R software (version 4.2.1) with gemtc package. A total of 9 studies involving 823 patients, including 9 treatments (acupressure, birth ball exercise, Bonapace method, distraction techniques, ice pressure, LI4 acupressure, lidocaine spray, smartphone-based music intervention and placebo). The surface under the cumulative ranking (SUCRA) shows that Bonapace method ranked first (SUCRA, 79.5%), LI4 acupressure ranked second (SUCRA, 65.6%), distraction technique ranked third (SUCRA, 57.6%), birth ball exercise ranked fourth (SUCRA, 51.8%). According to the results of the network meta-analysis, among the different strategies examined, the Bonapace Method stands out as the most effective nonpharmacological intervention for reducing labor pain. The results of this meta-analysis can aid both patients and healthcare professionals in choosing the most effective techniques to reduce labor pain.

A stochastic gravitational wave background in LISA from unresolved white dwarf binaries in the large magellanic cloud

Abstract The Laser Interferometer Space Antenna (LISA) is expected to detect a wide variety of gravitational wave sources in the mHz band. Some of these signals will elude individual detection, instead contributing as confusion noise to one of several stochastic gravitational-wave backgrounds (SGWBs) – notably including the ‘Galactic foreground’, a loud signal resulting from the superposition of millions of unresolved double white dwarf binaries (DWDs) in the Milky Way. It is possible that similar, weaker SGWBs will be detectable from other DWD populations in the local universe, including the Large Magellanic Cloud (LMC). We use the Bayesian LISA Inference Package (${\tt BLIP}$) to investigate the possibility of an anisotropic SGWB generated by unresolved DWDs in the LMC. To do so, we compute the LMC SGWB from a realistic DWD population generated via binary population synthesis, simulate four years of time-domain data with BLIP comprised of stochastic contributions from the LMC SGWB and the LISA detector noise, and analyze this data with BLIP’s spherical harmonic anisotropic SGWB search. We also consider the case of spectral separation from the Galactic foreground. We present the results of these analyses and show, for the first time, that the unresolved DWDs in the LMC will comprise a significant SGWB for LISA.

Commentary: Efficacy and safety of the innovative monoclonal antibodies in adults with generalized myasthenia gravis: a Bayesian network analysis

Commentary: Efficacy and safety of the innovative monoclonal antibodies in adults with generalized myasthenia gravis: a Bayesian network analysis

Genomic evaluation of mud sunfish in the atlantic slope drainage

Mud Sunfish (Acantharchus pomotis, Baird), a cryptic species native to the Atlantic and Gulf slope drainages of North America, have experienced a documented reduction in their geographic range. Atlantic Slope populations of Mud Sunfish were evaluated to determine the population structure, assess genetic diversity, and to identify presence of evolutionary significant units (ESU) in order to develop fundamental understandings of its occurrence. Tissue samples were collected from across the Atlantic Slope for genomic comparison. Restriction site Associated DNA Sequencing (RAD-Seq) was performed on 95 individuals from 14 populations. Mantel test provided evidence of a correlation between genetic and geographical distances, suggesting a pattern of isolation-by-distance. Differences among populations of Mud Sunfish largely occur along a north-south latitudinal gradient. Bayesian clustering of putatively outlier SNP loci, suggests that regional adaptive pressure is occurring. Despite a large geographic distance and significant differences between the majority of the populations of Mud Sunfish, Bayesian clustering analysis suggests that two unresolved clades are persisting within the Atlantic Slope Drainage: a northern clade of Mud Sunfish ranging from New Jersey to North Carolina and a southern clade ranging from South Carolina to Florida. A population of Mud Sunfish in Florida appears distinct from all others in the Atlantic Coastal plain and likely represents a distinct management unit.

Exploring different classification-dependent QSAR modelling strategies for HDAC3 inhibitors in search of meaningful structural contributors.

Histone deacetylase 3 (HDAC3), a Zn2+-dependent class I HDACs, contributes to numerous disorders such as neurodegenerative disorders, diabetes, cardiovascular disease, kidney disease and several types of cancers. Therefore, the development of novel and selective HDAC3 inhibitors might be promising to combat such diseases. Here, different classification-based molecular modelling studies such as Bayesian classification, recursive partitioning (RP), SARpy and linear discriminant analysis (LDA) were conducted on a set of HDAC3 inhibitors to pinpoint essential structural requirements contributing to HDAC3 inhibition followed by molecular docking study and molecular dynamics (MD) simulation analyses. The current study revealed the importance of hydroxamate function for Zn2+ chelation as well as hydrogen bonding interaction with Tyr298 residue. The importance of hydroxamate function for higher HDAC3 inhibition was noticed in the case of Bayesian classification, recursive partitioning and SARpy models. Also, the importance of substituted thiazole ring was revealed, whereas the presence of linear alkyl groups with carboxylic acid function, any type of ester function, benzodiazepine moiety and methoxy group in the molecular structure can be detrimental to HDAC3 inhibition. Therefore, this study can aid in the design and discovery of effective novel HDAC3 inhibitors in the future.

A High-Resolution Time Reversal Method for Target Localization in Reverberant Environments

Reverberation in real environments is an important factor affecting the high resolution of target sound source localization (SSL) methods. Broadband low-frequency signals are common in real environments. This study focuses on the localization of this type of signal in reverberant environments. Because the time reversal (TR) method can overcome multipath effects and realize adaptive focusing, it is particularly suitable for SSL in a reverberant environment. On the basis of the significant advantages of the sparse Bayesian learning algorithm in the estimation of wave direction, a novel SSL is proposed in reverberant environments. First, the sound propagation model in a reverberant environment is studied and the TR focusing signal is obtained. We then use the sparse Bayesian framework to locate the broadband low-frequency sound source. To validate the effectiveness of the proposed method for broadband low-frequency targeting in a reverberant environment, simulations and real data experiments were performed. The localization performance under different bandwidths, different numbers of microphones, signal-to-noise ratios, reverberation times, and off-grid conditions was studied in the simulation experiments. The practical experiment was conducted in a reverberation chamber. Simulation and experimental results indicate that the proposed method can achieve satisfactory spatial resolution in reverberant environments and is robust.

The rearrangement features and phylogenetic signals of mitochondrial genomes of gall midge subfamily Cecidomyiinae (Diptera: Cecidomyiidae) with first complete mitogenome of an economically important mango gall midge pest, Procontarina fructiculi from Guangxi, China

ABSTRACT In this study, we obtained the first complete mitochondrial genome of Procontarina fructiculi, an important mango gall midge pest from Guangxi, China. The mitogenome of P. fructiculi was sequenced and annotated for the first time, and compared with all the published and annotated data of Cecidomyiidae species to summarise the gall midge organisation and rearrangement features of mitochondrial genomes with phylogenetic analysis. The comparison of gall midge mitochondrial genome structure shows that the rearrangement of the mitochondrial gene structure is very common, and most rearrangements are embodied in rearrangement in the order of tRNAs. The phylogenetic results based on the maximum likelihood (ML) and Bayesian inference (BI) methods both highly supported the monophyly of Cecidomyiinae. Cecidomyiinae can be divided into two distinct clades (except Cecidomyiidae sp. 3): A: (Mayetiola destructor + Rhopalomyia pomum) and B: (Asphondylia rosetta + ((Procontarinia fructiculi + Orseolia oryzae) + ((Cecidomyiidae sp. 1 + Aphidoletes aphidimyza) + Cecidomyiidae sp. 2))). Based on the above results, some phylogenetic signals in rearrangement have been found in the mitochondrial genome structure of Cecidomyiinae for the first time.

Measurement of the neutron total cross section of 9Be at the Back-n white neutron source of CSNS

Abstract The neutron total cross section data of 9Be play an important role in nuclear structure model research of light nuclei and nuclear power installations. A measurement of the neutron total cross section of 9Be in the 0.3 eV-120 MeV energy region has been carried out by using time-of-flight method and transmission method with the Neutron Total Cross Sectional Spectrometer (NTOX) based on the multi-cell fast fission chamber at the China Spallation Neutron Source (CSNS)- Back-n white neutron source (Back-n). The fission count-neutron energy distributions of 235U and 238U without sample and with Be samples in three thicknesses were measured in the double-bunch operation mode during beam power of 100 kW. The Bayesian method is used to eliminate the influence of the double-bunch problem on neutron measurement in the energy region above 10 keV. The neutron total cross section of 9Be results is well consistent with ENDF/B-VIII.0 evaluation library data in 0.3 eV-20 MeV energy region. In the energy range of 0.3 eV to 10 keV, the deviation between our results and the evaluation results of ENDF/B-VIII.0 is within 2.5%, which is within 15% from 0.01-20 MeV. In the resonance energy region, the measured resonance energies in our experiment are 0.63 MeV, 0.82 MeV and 2.8 MeV respectively. The results show that the total cross section uncertainties of three thicknesses Be samples are within 2.2% in the energy region below 1 MeV. The total cross section uncertainty of 30 mm Be from 235U is the smallest and less than 5% in the energy region 0.3 eV-120 MeV. The results of this experiment can provide technical support for further data analysis and related nuclear data evaluation.

A comparison of computational algorithms for the Bayesian analysis of clinical trials.

Clinical trials are increasingly using Bayesian methods for their design and analysis. Inference in Bayesian trials typically uses simulation-based approaches such as Markov Chain Monte Carlo methods. Markov Chain Monte Carlo has high computational cost and can be complex to implement. The Integrated Nested Laplace Approximations algorithm provides approximate Bayesian inference without the need for computationally complex simulations, making it more efficient than Markov Chain Monte Carlo. The practical properties of Integrated Nested Laplace Approximations compared to Markov Chain Monte Carlo have not been considered for clinical trials. Using data from a published clinical trial, we aim to investigate whether Integrated Nested Laplace Approximations is a feasible and accurate alternative to Markov Chain Monte Carlo and provide practical guidance for trialists interested in Bayesian trial design. Data from an international Bayesian multi-platform adaptive trial that compared therapeutic-dose anticoagulation with heparin to usual care in non-critically ill patients hospitalized for COVID-19 were used to fit Bayesian hierarchical generalized mixed models. Integrated Nested Laplace Approximations was compared to two Markov Chain Monte Carlo algorithms, implemented in the software JAGS and stan, using packages available in the statistical software R. Seven outcomes were analysed: organ-support free days (an ordinal outcome), five binary outcomes related to survival and length of hospital stay, and a time-to-event outcome. The posterior distributions for the treatment and sex effects and the variances for the hierarchical effects of age, site and time period were obtained. We summarized these posteriors by calculating the mean, standard deviations and the 95% equitailed credible intervals and presenting the results graphically. The computation time for each algorithm was recorded. The average overlap of the 95% credible interval for the treatment and sex effects estimated using Integrated Nested Laplace Approximations was 96% and 97.6% compared with stan, respectively. The graphical posterior densities for these effects overlapped for all three algorithms. The posterior mean for the variance of the hierarchical effects of age, site and time estimated using Integrated Nested Laplace Approximations are within the 95% credible interval estimated using Markov Chain Monte Carlo but the average overlap of the credible interval is lower, 77%, 85.6% and 91.3%, respectively, for Integrated Nested Laplace Approximations compared to stan. Integrated Nested Laplace Approximations and stan were easily implemented in clear, well-established packages in R, while JAGS required the direct specification of the model. Integrated Nested Laplace Approximations was between 85 and 269 times faster than stan and 26 and 1852 times faster than JAGS. Integrated Nested Laplace Approximations could reduce the computational complexity of Bayesian analysis in clinical trials as it is easy to implement in R, substantially faster than Markov Chain Monte Carlo methods implemented in JAGS and stan, and provides near identical approximations to the posterior distributions for the treatment effect. Integrated Nested Laplace Approximations was less accurate when estimating the posterior distribution for the variance of hierarchical effects, particularly for the proportional odds model, and future work should determine if the Integrated Nested Laplace Approximations algorithm can be adjusted to improve this estimation.

Enhancing disruption prediction through Bayesian neural network in KSTAR

In this research, we develop a data-driven disruption predictor based on Bayesian deep probabilistic learning, capable of predicting disruptions and modeling uncertainty in KSTAR. Unlike conventional neural networks within a frequentist approach, Bayesian neural networks can quantify the uncertainty associated with their predictions, thereby enhancing the precision of disruption prediction by mitigating false alarm rates through uncertainty thresholding. Leveraging 0D plasma parameters from EFIT and diagnostic data, a temporal convolutional network adept at handling multi-time scale data was utilized. The proposed framework demonstrates proficiency in predicting disruptions, substantiating its effectiveness through successful applications to KSTAR experimental data.

Dynamic Bayesian Game Optimization Method for Multi-Energy Hub Systems with Incomplete Load Information

The interconnection of Energy Hub can enhance the energy efficiency and reliability associated with the independent operation of energy systems. However, the traditional optimal scheduling methods are hard to tackle the situation with the incomplete load information and competitive constraints of a multi-Energy Hub. This paper proposes a dynamic Bayesian game optimization scheduling strategy considering incomplete information on users’ load-side demand. First, a load forecast error coefficient is introduced to handle the conditional probability problem caused by incomplete information through the Bayesian rule. The constraint relationship among multi-Energy Hub under the dynamic price mechanism is analyzed to meet the economic and environmental coordination goals under load uncertainty. Second, extending from Nash equilibrium to Bayes Nash equilibrium proves the existence and uniqueness of Nash equilibrium solutions in Bayesian game models. The decision game algorithm is used to optimize the scheduling of the multi-Energy Hub and subsequently ensure the autonomous scheduling of the system and solve the information barriers. Finally, an Integrated Energy System composed of three Energy Hubs was used to validate the effectiveness and superiority of the proposed optimization method. Results show that under the Bayesian game decision, the multi-Energy Hub game model has reduced the total cost of the Integrated Energy System by 0.78%, which can improve the economic benefit and address the environmental pollution problem.

Examining Factors Contributing to Motorcycle Collisions with Left-Turning Vehicles at Urban Intersection Locations

Motorcycle crashes account for a significant proportion of traffic-related fatalities on U.S. roadways. Compared with motor vehicles, motorcycles traveling straight ahead are more susceptible to collisions with left-turning vehicles at intersections (note – in a system where traffic travels on the right-hand side of the road). The limited knowledge of the causes and influences of this specific type of crash deters efforts to improve motorcycle safety and is partly influenced by two issues. First, significant variables are unknown; second, motorcycles comprise a small proportion of vehicles in the traffic stream. This study sought to understand the factors that may contribute to the disproportionate crash risk left-turning vehicles pose for motorcyclists while accounting for the imbalance of vehicle proportions. Data containing motorcycle-motor vehicle and motor vehicle-motor vehicle crashes involving left-turning motor vehicles at intersections in South Florida were collected from 2015 to 2017. The study applied logistic regression on a balanced dataset generated using the random oversampling technique. The proposed model improved the predictive accuracy and enabled the identification of factors contributing to motorcycle crashes with left-turning vehicles. A Bayesian network analysis was also applied to the balanced data to analyze the interrelationship of factors associated with motorcycle crashes with left-turning vehicles. Results indicated that the type of intersection and traffic control, time of day, age of drivers, sex of the motorcyclist, roadway type, and weather were significantly associated with motorcyclists’ susceptibility to collisions with left-turning vehicles. Recognizing these attributes could help devise engineering measures and policies for promoting motorcycle safety.

On Bayesian premium credibility: maximum entropy prior for unknown claims process and numerical example

In the complex field of insurance, determining the credibility of the most efficient premium is a major challenge, especially when the distribution of claims remains unknown. This study uses a Bayesian methodology to address this issue. In this framework, the initial assumptions concerning the unidentified parameters of the claims process are expressed by means of a distribution function. The objective is to establish premium credibility by maximizing the insurance company's expected utility while respecting specific constraints. By applying the maximum entropy method, we can derive the posterior distribution of the loss process from the observed data available. This approach is advantageous because it offers modeling flexibility and enables estimates to be progressively updated as new data becomes available. It is also particularly useful in situations where data is limited or incomplete, as is often the case in the insurance sector. The study highlights the characteristics and advantages of this Bayesian method. It highlights how the approach handles uncertainty and integrates prior knowledge into the estimation process. In addition, several numerical illustrations are included to demonstrate the practical application of the method and its impact on determining premium credibility. In summary, this research offers an innovative perspective on risk management in the insurance sector. It proposes a method that not only adheres to the playful approach to risk management, but also to the premium credibility approach.

Exploring the Efficacy of Binary Surveys versus Likert Scales in Assessing Student Perspectives Using Bayesian Analysis

Likert-scale surveys are the undeniable protagonists of online evaluations. They ask the respondent to express their degree of agreement with a series of statements related to the development of a subject. In contrast, in social networks, dichotomous surveys are mostly used. They force respondents to polarize their opinions by selecting “like” or “dislike”. This study compares the efficacy of binary and Likert surveys in gathering student opinions on mechanical engineering program subjects. Using Bayesian analysis, it analyzes the similarity of responses obtained from both formats. For each question and scale, the ratio of “I like” among the total responses collected was calculated. The Bayesian factor method was used to compare the ratios obtained. The null hypothesis was equality between the ratios obtained by the different scales for the same question. This hypothesis was rejected in only 7 of the 49 questions evaluated—less than 15%. Finally, the students were surveyed on the preference for use of both scales. More than 80% stated a preference for the use of the dichotomous format. In view of the results obtained, we recommend more frequent use of the dichotomous scale to gather students’ opinions.

Primulina guarouba, a new endemic cave‐dwelling species of Gesneriaceae from northern Guangxi, China

Primulina guarouba, a new species from the limestone area of northern Guangxi, China, is described and illustrated. According to the maximum likelihood (ML) and Bayesian inference (BI) analysis, it nests in a strongly supported clade (BP = 99%) that includes P. cordifolia, P. repanda and P. huangii. Of these, P. guarouba is similar to the P. cordifolia in morphology, but it is easily distinguished from the latter by some of the distinct characters in leaf blade, peduncles, bracts, calyx, corolla, pistil.

Residual network improves the prediction accuracy of genomic selection.

Genetic improvement of complex traits in animal and plant breeding depends on the efficient and accurate estimation of breeding values. Deep learning methods have been shown to be not superior over traditional genomic selection (GS) methods, partially due to the degradation problem (i.e. with the increase of the model depth, the performance of the deeper model deteriorates). Since the deep learning method residual network (ResNet) is designed to solve gradient degradation, we examined its performance and factors related to its prediction accuracy in GS. Here we compared the prediction accuracy of conventional genomic best linear unbiased prediction, Bayesian methods (BayesA, BayesB, BayesC, and Bayesian Lasso), and two deep learning methods, convolutional neural network and ResNet, on three datasets (wheat, simulated and real pig data). ResNet outperformed other methods in both Pearson's correlation coefficient (PCC) and mean squared error (MSE) on the wheat and simulated data. For the pig backfat depth trait, ResNet still had the lowest MSE, whereas Bayesian Lasso had the highest PCC. We further clustered the pig data into four groups and, on one separated group, ResNet had the highest prediction accuracy (both PCC and MSE). Transfer learning was adopted and capable of enhancing the performance of both convolutional neural network and ResNet. Taken together, our findings indicate that ResNet could improve GS prediction accuracy, affected potentially by factors such as the genetic architecture of complex traits, data volume, and heterogeneity.

Evaluating distributed-learning on real-world obstetrics data: comparing distributed, centralized and local models

This study focused on comparing distributed learning models with centralized and local models, assessing their efficacy in predicting specific delivery and patient-related outcomes in obstetrics using real-world data. The predictions focus on key moments in the obstetric care process, including discharge and various stages of hospitalization. Our analysis: using 6 different machine learning methods like Decision Trees, Bayesian methods, Stochastic Gradient Descent, K-nearest neighbors, AdaBoost, and Multi-layer Perceptron and 19 different variables with various distributions and types, revealed that distributed models were at least equal, and often superior, to centralized versions and local versions. We also describe thoroughly the preprocessing stage in order to help others implement this method in real-world scenarios. The preprocessing steps included cleaning and harmonizing missing values, handling missing data and encoding categorical variables with multisite logic. Even though the type of machine learning model and the distribution of the outcome variable can impact the result, we reached results of 66% being superior to the centralized and local counterpart and 77% being better than the centralized with AdaBoost. Our experiments also shed light in the preprocessing steps required to implement distributed models in a real-world scenario. Our results advocate for distributed learning as a promising tool for applying machine learning in clinical settings, particularly when privacy and data security are paramount, thus offering a robust solution for privacy-concerned clinical applications.

Comparative analysis of C n2 estimation methods for sonic anemometer data

Wind speed and sonic temperature measured with ultrasonic anemometers are often utilized to estimate the refractive index structure parameter C n 2, a vital parameter for optical propagation. In this work, we compare four methods to estimate C n 2 from C T 2, using the same temporal sonic temperature data streams for two separated sonic anemometers on a homogenous path. Values of C n 2 obtained with these four methods using field trial data are compared to those from a commercial scintillometer and from the differential image motion method using a grid of light sources positioned at the end of a common path. In addition to the comparison between the methods, we also consider appropriate error bars for C n 2 based on sonic temperature considering only the errors from having a finite number of turbulent samples. The Bayesian and power spectral methods were found to give adequate estimates for strong turbulence levels but consistently overestimated the C n 2 for weak turbulence. The nearest neighbors and structure function methods performed well under all turbulence strengths tested.

Sustainable water–ecosystem management using the Bayesian network and variable relationship analysis

ABSTRACT Under the pressures of global climate change and human activities, the carrying capacity of water and soil resources in agricultural lands has decreased, and the traditional models of agricultural development are no longer sustainable. Land degradation, groundwater quality reduction and ecosystem instability are the consequences of agricultural development without considering sustainability indicators. This article aims to investigate the use of variable relationship analysis and Bayesian network methods to analyze and investigate the relationship between irrigation in agriculture and the sustainability of the groundwater ecosystem. Descriptive statistics of agriculture including cultivation pattern, time, precipitation, irrigation, and land slope were analyzed and combined with the simulated characteristics of groundwater including specific yield, hydraulic conductivity and hydrodynamic diffusion coefficients. Five crops of wheat, barley, paddy, alfalfa, and potato were studied to evaluate the effect of plants on the pattern of nitrate release due to irrigation and fertilization in agriculture. The results showed that managing the amount of fertilizer and the volume of irrigation can positively affect the nitrate distribution pattern in the groundwater even in a short period of time.