Simulation-based Methods Research Articles

Pairwise likelihood estimation of latent autoregressive count models.

Latent autoregressive models are useful time series models for the analysis of infectious disease data. Evaluation of the likelihood function of latent autoregressive models is intractable and its approximation through simulation-based methods appears as a standard practice. Although simulation methods may make the inferential problem feasible, they are often computationally intensive and the quality of the numerical approximation may be difficult to assess. We consider instead a weighted pairwise likelihood approach and explore several computational and methodological aspects including estimation of robust standard errors and the role of numerical integration. The suggested approach is illustrated using monthly data on invasive meningococcal disease infection in Greece and Italy.

A mixed methods study for the evaluation of a digital health solution for cancer multidisciplinary team meetings using simulation-based research methods.

e14063 Background: Simulation-based research (SBR) methods involve setting up structured scenarios to replicate real-world situations, with the aim of eliciting real-world reactions and behaviours. SBR is useful for the evaluation of new healthcare solutions without compromising patient safety or navigating complex ethical review processes. However, there are few trials using SBR methods for the evaluation of digital health interventions (DHIs), the adoption of which has been hindered in the NHS due to a lack of evidence-base for their efficacy. SBR methods could be an appropriate tool for testing digital solutions. Methods: The Institute of Global Health Innovation (IGHI) developed a series of simulated lung cancer multidisciplinary team (MDT) meetings to test the NAVIFY Tumor Board solution by Roche Diagnostic Information Solutions, a digital solution for the preparation and conduct of cancer MDTs. To provide an environment for participants to evaluate the capabilities of the NAVIFY Tumor Board solution, 10 simulation sessions were organised in which groups of five to six clinicians were recruited to discuss up to 10 mock patient cases across two simulated MDTs, first using standard tools commonly used to conduct MDTs and then using the NAVIFY Tumor Board solution. The cases were developed by the study team at IGHI and consultants in respiratory medicine and oncology. 56 healthcare professionals (respiratory physicians, oncologists, radiologists, histopathologists, clinical nurse specialists and thoracic surgeons) were recruited. The sessions were video recorded and observations were noted by the study team, followed by a focus group in which participants provided feedback about their experience of the simulated MDTs. Results: Through this study we were able to generate evidence and multi-professional recommendations for Roche regarding the functionality, usability and applicability of the solution in the NHS, as well as beneficial features and those which could be improved. Participants reported that the simulations were realistic and a meaningful way of conducting evaluations of digital health solution without impacting clinical practice or patient safety. Conclusions: This study demonstrated the utility and validity of testing commercial tools in simulated settings to generate meaningful insights from endusers. SBR methods were shown to be an acceptable way for frontline practicing clinicians to participate in the testing and development of digital health tools in a standardised setting.

AK-MSS: An adaptation of the AK-MCS method for small failure probabilities

Structural reliability analysis aims to estimate the failure probability of a structure with respect to the performance function. This estimation may be a difficult task when the computation of a structural response requires large computational efforts. Although simulation-based methods can be used directly, they may require a large number of calls to the performance function for small failure probabilities. Metamodels (such as Kriging) that can replace the original performance function can be applied to address the computational costs. Among these methods, the active learning reliability method combining Kriging and Monte Carlo simulation (AK-MCS) is efficient, except for small failure probabilities and system reliability analysis. In this paper, a modified algorithm that combines the AK-MCS and the modified subset simulation (MSS) is proposed to estimate small failure probabilities. The strategy replaces the initial population with a large population that is generated by the MSS. No prior knowledge about the probability level is needed, and the sample size will adaptively change according to the estimation that is obtained in the last iteration and the target coefficient of variation. Therefore, the limit state can be covered by the new population, which is important for refining the Kriging model. The efficiency and accuracy of the proposed algorithm are illustrated using several examples.

Calibration of individual-based models to epidemiological data: A systematic review.

Individual-based models (IBMs) informing public health policy should be calibrated to data and provide estimates of uncertainty. Two main components of model-calibration methods are the parameter-search strategy and the goodness-of-fit (GOF) measure; many options exist for each of these. This review provides an overview of calibration methods used in IBMs modelling infectious disease spread. We identified articles on PubMed employing simulation-based methods to calibrate IBMs informing public health policy in HIV, tuberculosis, and malaria epidemiology published between 1 January 2013 and 31 December 2018. Articles were included if models stored individual-specific information, and calibration involved comparing model output to population-level targets. We extracted information on parameter-search strategies, GOF measures, and model validation. The PubMed search identified 653 candidate articles, of which 84 met the review criteria. Of the included articles, 40 (48%) combined a quantitative GOF measure with an algorithmic parameter-search strategy-either an optimisation algorithm (14/40) or a sampling algorithm (26/40). These 40 articles varied widely in their choices of parameter-search strategies and GOF measures. For the remaining 44 (52%) articles, the parameter-search strategy could either not be identified (32/44) or was described as an informal, non-reproducible method (12/44). Of these 44 articles, the majority (25/44) were unclear about the GOF measure used; of the rest, only five quantitatively evaluated GOF. Only a minority of the included articles, 14 (17%) provided a rationale for their choice of model-calibration method. Model validation was reported in 31 (37%) articles. Reporting on calibration methods is far from optimal in epidemiological modelling studies of HIV, malaria and TB transmission dynamics. The adoption of better documented, algorithmic calibration methods could improve both reproducibility and the quality of inference in model-based epidemiology. There is a need for research comparing the performance of calibration methods to inform decisions about the parameter-search strategies and GOF measures.

Speed-Up in Test Methods Using Probabilistic Merit Indicators

Deterministic test generation methods are time consuming and, this has led to emergence of simulation-based approaches. The basis of simulation-based methods is to propose a number of test vectors, evaluate the efficiency of the proposed vectors, and accept or reject them. In these methods, the efficiency of each test vector is traditionally evaluated based on fault coverage of that vector. In this paper an alternative criterion based on probabilistic simulation approaches is proposed that can be calculated much faster than fault coverage. Statistical evaluations confirm a strong correlation between the proposed measure and fault coverage. The proposed measure can be used in simulation-based and meta-heuristic test generation methods to achieve speedup. In order to indicate efficiency of the proposed probabilistic measure, two test generation methods that utilize this measure are suggested and exploited to generate test for ISCAS85 benchmark circuits. These test generation methods are called PMI_PRUNE and PMI_GA. Our results demonstrate that PMI_PRUNE can achieve up to 21.2x, while the maximum speedup in PMI_GA is 5.0x. The average speedup in PMI_PRUNE and PMI_GA is about 9.6x, and 2.7x respectively.

Objective Assessment and Standard Setting for Basic Flexible Ureterorenoscopy Skills Among Urology Trainees Using Simulation-Based Methods.

Objective: To objectively assess the performance of graduating urology residents performing flexible ureterorenoscopy (fURS) using a simulation-based model and to set an entrustability standard or benchmark for use across the educational spectrum. Methods: Chief urology residents and attending endourologists performed a standardized fURS task (ureterorenoscopy and repositioning of stones) using a Boston Scientific© Lithovue ureteroscope on a Cook Medical© URS model. All performances were video-recorded and blindly scored by both endourology experts and crowd-workers (C-SATS) using the Ureteroscopic Global Rating Scale, plus an overall entrustability score. Validity evidence supporting the scores was collected and categorized. The Borderline Group (BG) method was used to set absolute performance standards for the expert and crowdsourced ratings. Results: A total of 44 participants (40 chief residents, 4 faculties) completed testing. Eighty-three percent of participants had performed >50 fURS cases at the time of the study. Only 47.7% (mean score 12.6/20) and 61.4% (mean score 12.4/20) of participants were deemed "entrustable" by experts and crowd-workers, respectively. The BG method produced entrustability benchmarks of 11.8/20 for experts and 11.4/20 for crowd-worker ratings, resulting in pass rates of 56.9% and 61.4%. Conclusion: Using absolute standard setting methods, benchmark scores were set to identify trainees who could safely carry out fURS in the simulated setting. Only 60% of residents in our cohort were rated as entrustable. These findings support the use of benchmarks to earlier identify trainees requiring remediation.

Simulation-Based Methods for Model Building and Refinement in Cryoelectron Microscopy.

Advances in cryoelectron microscopy (cryo-EM) have revolutionized the structural investigation of large macromolecular assemblies. In this review, we first provide a broad overview of modeling methods used for flexible fitting of molecular models into cryo-EM density maps. We give special attention to approaches rooted in molecular simulations-atomistic molecular dynamics and Monte Carlo. Concise descriptions of the methods are given along with discussion of their advantages, limitations, and most popular alternatives. We also describe recent extensions of the widely used molecular dynamics flexible fitting (MDFF) method and discuss how different model-building techniques could be incorporated into new hybrid modeling schemes and simulation workflows. Finally, we provide two illustrative examples of model-building and refinement strategies employing MDFF, cascade MDFF, and RosettaCM. These examples come from recent cryo-EM studies that elucidated transcription preinitiation complexes and shed light on the functional roles of these assemblies in gene expression and gene regulation.

An efficient computational method for estimating failure credibility by combining genetic algorithm and active learning Kriging

Failure credibility is popular in measuring safety degree of structure under fuzzy uncertainty due to its excellent property of self-duality. Existing methods for estimating failure credibility can be mainly divided into two categories, i.e., the simulation-based methods and the optimization-based methods. The simulation-based methods are universal and robust, but time-consuming in dealing with high-dimensional problems. The optimization-based methods are efficient and flexible, but the precision of the failure credibility estimate will be impacted by the local optimum solution resulted from some optimization techniques. Thus, in order to overcome the disadvantages of existing methods, an efficient method by combining genetic algorithm and active learning Kriging (AK-GA) is proposed to estimate failure credibility in this paper. Firstly, by use of the fuzzy simulation, the estimation of failure credibility is transformed into finding for the failure/safety sample that processes the maximum joint membership function (MF) from the fuzzy simulation pool, which makes the credibility estimation less difficult to be computed. Secondly, the genetic algorithm is used to find the failure/safety sample with maximum joint MF. Thirdly, to drastically improve the computational efficiency of the proposed method, active learning Kriging is embedded to accurately and efficiently distinguish the failure and safety sample in computing the fitness function of genetic algorithm. Four mathematical and engineering test examples are used to demonstrate the accuracy and efficiency of the proposed AK-GA for estimating failure credibility.

Development and validation of a synthetic 3D-printed simulator for training in neuroendoscopic ventricular lesion removal.

Neuroendoscopic surgery using an ultrasonic aspirator represents a valid tool with which to perform the safe resection of deep-seated ventricular lesions, but the handling of neuroendoscopic instruments is technically challenging, requiring extensive training to achieve a steep learning curve. Simulation-based methods are increasingly used to improve surgical skills, allowing neurosurgical trainees to practice in a risk-free, reproducible environment. The authors introduce a synthetic, patient-specific simulator that enables trainees to develop skills for endoscopic ventricular tumor removal, and they evaluate the model's validity as a training instrument with regard to realism, mechanical proprieties, procedural content, and handling. The authors developed a synthetic simulator based on a patient-specific CT data set. The anatomical features were segmented, and several realistic 1:1 skull models with all relevant ventricular structures were fabricated by a 3D printer. Vascular structures and the choroid plexus were included. A tumor model, composed of polyvinyl alcohol, mimicking a soft-consistency lesion, was secured in different spots of the frontal horn and within the third ventricle. Neurosurgical trainees participating in a neuroendoscopic workshop qualitatively assessed, by means of a feedback survey, the properties of the simulator as a training model that teaches neuroendoscopic ultrasonic ventricular tumor surgery; the trainees rated 10 items according to a 5-point Likert scale. Participants appreciated the model as a valid hands-on training tool for neuroendoscopic ultrasonic aspirator tumor removal, highly rating the procedural content. Furthermore, they mostly agreed on its comparably realistic anatomical and mechanical properties. By the model's first application, the authors were able to recognize possible improvement measures, such as the development of different tumor model textures and the possibility, for the user, of creating a realistic surgical skull approach and neuroendoscopic trajectory. A low-cost, patient-specific, reusable 3D-printed simulator for the training of neuroendoscopic ultrasonic aspirator tumor removal was successfully developed. The simulator is a useful tool for teaching neuroendoscopic techniques and provides support in the development of the required surgical skills.

Optimal sizing of rainwater harvesting systems for domestic water usages: A systematic literature review

Abstract Rainwater harvesting systems (RWHS) are increasing in popularity because of their ability to alleviate water pressure on centralized systems, minimize or delay rainfall runoff, and fit relatively easily in both the centralized/decentralized infrastructure organization. Adequately sizing RWHS is critical to optimizing their operation because under-sizing results in systems that are unable to provide a sufficient, reliable source of water while oversizing increases the capital costs incurred with limited marginal benefits and poses potential water quality risks. In this paper, we conduct a systematic literature review to assess the state-of-art in the field of optimization of domestic rainwater harvesting systems. Sizing of storage is identified as the most important objective of optimization, yet sizing for cost is the most frequently implemented outcome of optimization. Optimizing for a local maximum is often favored over simulation-based optimization methods that produce global maxima. To derive more realistic sizing estimates, future optimization studies will have to take into account greater variation in water demands as well as various climate change scenarios, especially given that rainfall frequency and quantity are critical design variables of a rainwater harvesting system.

Simulation-Based Methodology for Determining the Dynamic Strength of Tire Inflation Restraining Devices

The article suggests and supports a simulation-based methodology for determining whether the dynamic strength of tire inflation restraining devices for tire inflation meet quality requirements and ensure operator safety during a potential tire explosion. Dynamic strength tests using an NM-600 safety shield and NK-0728 safety cage during a 29.5 R25X tire explosion at a pressure of 10 bar were presented as an example application of this methodology. The shield was subjected to destructive tests involving the use of a 2200 kg impactor, dropping it so that the minimum kinetic energy reached 20 kJ at the time of impact. Analyzed devices were constructed of S355 steel in accordance with EN 10025. The Cowper–Symonds model of material for strain rate phenomena was used in the calculations. Simulations of a 20 kJ ring impact against the cage were performed. The equivalent stress distribution was determined, and displacement contour lines for the maximum dynamic deformation value and plastic deformation were calculated. The plastic displacement obtained in numerical tests was equal to the permanent deformation recorded in the experimental test. Further, the simulations showed that the examined cage met the assumed strength criteria. The conducted tests confirmed the usefulness of the proposed methodology for assessing the dynamic strength of safety cages and shields for tire inflation. The full-scale, physical cage testing is difficult to implement because it requires placing a ring impacting the cage wall. This is a major boundary for closed cages, as considered in this publication. Thus, simulation-based methods are becoming a principal tool for safety assessment of tire inflation restraining devices.

Uncertainty quantification and statistical model validation for an offshore jacket structure panel given limited test data and simulation model

Due to inherent variability (i.e., aleatory uncertainty) in material properties, loading conditions, manufacturing processes, etc., simulation output responses should follow certain distributions, which are the outcome of input distribution models and simulation models. Thus, uncertainty quantification (UQ) using simulation-based methods is accurate only if we have (1) accurate input distribution models and (2) an accurate simulation model. However, in practical engineering applications, only limited numbers of input test data are available for modeling input distributions. Moreover, the simulation model could be biased due to assumptions and idealizations in the modeling process. Thus, the simulation model needs to be validated to correctly predict the output response of the physical system. The statistical validation of the simulation model would require large numbers of physical test data, which is extremely expensive. This study presents a computational method to obtain a target output distribution, which is a good approximation of the true output distribution given limited test data and a biased model. Using Bayesian analysis, possible candidates of output distribution are obtained, and a target output distribution is selected at the posterior median. The target output distribution is used to measure the bias of the simulation models and the surrogate models for UQ and statistical model validation. Furthermore, the cumulative distribution function of the simulation model prediction error is presented to provide (1) the median of model prediction error and (2) model confidence at a user-specified error level. To demonstrate the proposed statistical model validation method, a simulation model of an offshore jacket structure panel is generated using ANSYS. This example has eight uncertain input parameters (six of them are geometrical parameters and two of them are material properties). For input distributions of geometrical parameters, the standard deviation of manufacturing tolerances is used, and three material tests are carried out to obtain input distributions of two material properties. The output response of interest is a reaction force. The simulation model is validated using three sets of material test data and experimental test data to determine model confidence on output prediction and the validity of the simulation model. The result of statistical model validation shows how much bias the simulation model has and how much confidence engineers can have in the model prediction at a user-specified error level.

Development, Implementation, and Evaluation of a Faculty Development Workshop to Enhance Debriefing Skills Among Novice Facilitators.

IntroductionEffective debriefing during simulation-based training (SBT) is critical to promote learning outcomes. Despite debriefing’s central role in learning and various published debriefing methods and techniques, little is known about faculty development structure for debriefing training among novice facilitators. Continuing medical education courses often use simulation-based methods but provide minimal training in debriefing techniques to novice facilitators. We describe the development, implementation, and evaluation of a structured debriefing training workshop for novice facilitators.MethodsDesigned and conducted by simulation debriefing experts, a debriefing workshop was provided to novice facilitators serving as faculty during the simulation-based Sedation Provider Course (PC) at the 2018 Society of Pediatric Sedation conference. Emphasizing evidence-based key elements of effective debriefing, the workshop was divided into three components: 1) an introductory 30 minute didactic, 2) 75 minutes role modeling of simulated effective and ineffective debriefing 3) 120 minutes repetitive deliberate practice sessions with summative and formative feedback. Effective transfer of learned debriefing skills was assessed during facilitators’ PC debriefing using the Objective Structured Assessment of Debriefing (OSAD) tool, facilitators’ self-efficacy, and PC student learners’ evaluation of facilitator debriefings during the PC.ResultsSixteen facilitators participated in the 4-h workshop and the next day served as PC faculty. The median OSAD score was 31 (13-40) for all facilitators. OSAD components with lowest and highest performance were “Establishing Learning Environment” with a median score of 1 (1-5) and “Engagement of Learners,” with a median score of 4.75 (2.5-5). Facilitators' self- assessment in debriefing significantly improved on the 5-point Likert scale pre- and post-workshop, respectively. PC student learners’ evaluations revealed high degrees of satisfaction with debriefing quality.ConclusionsA proposed model integrating full-length debriefing and repetitive practice paired with summative and formative feedback provides a feasible and effective approach for debriefing training of novice facilitators for simulation-based educational courses.

Phenomenological Analysis of Thermo-Mechanical-Chemical Properties of GFRP during Curing by Means of Sensor Supported Process Simulation

Inherent process-induced deformations (PID) and residual stresses impede the application of composite parts. PID lead to a geometrical mismatch in assemblies and require subsequent work for tolerance compensation. Unknown residual stresses cause overweighted structures resulting from unnecessary high safety factors. To counteract the deformations, the tool design needs to be modified until the component geometry meets the specifications. This process is mostly carried out empirically and is time and cost intensive. To improve the efficiency of the development process, an in-deep comprehension of the manufacturing processes is mandatory. Therefore, experimental and simulation-based methods are increasingly applied and enhanced. The object of this work is to investigate the development of process-induced strains as well as the material behaviour during the manufacturing for a GFRP plate. The process-induced strains are monitored by optical fiber Bragg grating (FBG) sensors. The change of the material phases is detected by dielectric sensors. Furthermore, a detailed process simulation considering viscoelastic effects and reaction kinetics is performed. Finally, the measurements are correlated with the simulation data to validate the simulation approach. A very good correlation for both the reaction kinetics as well as the process-induced strains is observed.

Optimizing the transition between design and operation of ZEBs: Lessons learnt from the Solar Decathlon China 2018 SCUTxPoliTo prototype

In the context of the worldwide efforts to reduce energy consumptions and carbon dioxide (CO2) emissions related to the building sector, reaching the high performance of a zero energy building (ZEB) has been demonstrated to be feasible, especially when the design phase is supported by integrated simulation-based optimization methods. It is necessary to pay special attention to the transition from building design to building operation and it is hard to find examples of real ZEB buildings where the that are optimized considering design and operation at the same time. In this context, the Solar Decathlon competition is a unique experimental field to advance in research about design, simulation, and optimization of ZEBs.This study aims at presenting the energy-related scientific aspects behind and beyond the winner building prototype of the Solar Decathlon China 2018 competition. An optimization-based calibration supported by sensitivity analysis is carried out to calibrate the simulation model of the ZEB prototype, based on data collected throughout the design and construction phase. Then, an original simulation-based optimization method is tailored to the purpose of maximizing the contest score, considering parameters related to both design and operation of the building.A high level of model calibration was reached and the contest score was improved to help the ZEB prototype win the competition. Results demonstrated that the applied methodological framework was able to drive towards optimized and integrated design and operation strategies.

Life-cycle management of deteriorating bridge networks with network-level risk bounds and system reliability analysis

Structural deterioration poses a substantial threat to the safety, serviceability, and functionality of bridges. Since bridges are connected in transportation networks, their failure can dramatically alter traffic flow, causing immense social consequences such as large-scale traffic delay and additional vehicle operating cost. In this paper, a novel method is proposed for life-cycle management of deteriorating bridge networks. The proposed method aims at minimizing network-level risks associated with deterioration-induced bridge failure. To combat the deficiencies of existing Monte Carlo simulation-based risk assessment methods, the proposed method adopts a non-simulation approach that relies on risk bounds of deteriorating bridge networks. In particular, this method uses system reliability analysis to determine the occurrence probabilities of various bridge failure scenarios in a network. For each failure scenario considered, traffic assignment is conducted to predict the traffic flow in the damaged network and the network-level consequences. The upper and lower bounds of the network-level risk are then formulated, allowing for efficient and accurate risk assessment with only a few traffic assignment operations. Estimated by the average value of its upper and lower bounds, the network-level risk is used as the optimization objective in a metaheuristic search procedure to obtain optimal life-cycle maintenance plans including the maintenance schedule and the investment on each maintenance action. The proposed method excels in its ability to account for the impacts of unlikely, yet high-impact events, as well as its ability to obtain optimal or near-optimal life-cycle maintenance plans that can more effectively reduce risks than those obtained using simulation-based methods.

Fast and systematic approach for adjusting ROCOF relay used in islanding detection of SDG

Frequency-based relays, commonly used for islanding detection of synchronous distributed generation (SDG), may either have large non-detection zones (NDZs) or wrongly operate for non-islanding disturbances unless they are efficiently adjusted. Trial and error simulation-based methods, employing application region (AR) and/or power imbalance AR (PIAR), are presented in the literature to adjust these relays for quick islanding detection without false trips. However, the associated settings are not optimal and finding them is time-consuming. In this study, an approach utilising particle swarm optimisation algorithm is proposed to adjust a rate of change of frequency (ROCOF) relay, one of the most effective relays in SDG islanding detection. The proposed approach needs a set of analytical formulas for determining the ROCOF relay performance curve and NDZ as well as AR and PIAR. These formulas are developed to be employed in the relay adjustment. By applying the developed analytical formulas to the optimisation-based approach, the ROCOF relay is adjusted fast and systematically. It is shown that while the performance of the relay adjusted by the proposed approach is close to the one with setting obtained by corresponding trial and error-based method, the latter is much more time-consuming than the former.

Community-based in situ simulation: bringing simulation to the masses

Simulation-based methods are regularly used to train inter-professional groups of healthcare providers at academic medical centers (AMC). These techniques are used less frequently in community hospitals. Bringing in-situ simulation (ISS) from AMCs to community sites is an approach that holds promise for addressing this disparity. This type of programming allows academic center faculty to freely share their expertise with community site providers. By creating meaningful partnerships community-based ISS facilitates the communication of best practices, distribution of up to date policies, and education/training. It also provides an opportunity for system testing at the community sites. In this article, we illustrate the process of implementing an outreach ISS program at community sites by presenting four exemplar programs. Using these exemplars as a springboard for discussion, we outline key lessons learned discuss barriers we encountered, and provide a framework that can be used to create similar simulation programs and partnerships. It is our hope that this discussion will serve as a foundation for those wishing to implement community-based, outreach ISS.

The dynamic factor network model with an application to international trade

We introduce a dynamic network model with probabilistic link functions that depend on stochastically time-varying parameters. We adopt a blockmodel framework and allow the high-dimensional vector of link probabilities to be a function of a low-dimensional set of dynamic factors. The resulting dynamic factor network model has a basic and transparent structure. However, parameter estimation, signal extraction of stochastic loadings and dynamic factors, and the econometric analysis generally are intricate tasks for which simulation-based methods are needed. We provide feasible and practical solutions to these challenging tasks, based on a computationally efficient importance sampling procedure to evaluate the likelihood function. An extensive Monte Carlo study demonstrates the performance of our method in finite samples, both under correct and incorrect model specifications. In an empirical study, we use the novel framework to analyze global patterns of banana exports and imports. We identify groups of heavy and light traders in this highly active commodity market and their time-varying trade probabilities.

A multi-model ensemble approach for the assessment of climatic and anthropogenic impacts on river flow change

ABSTRACTClimate variability and human activities are considered to be the most likely reasons for negative trends in river inflow and the water level of some lakes and wetlands in the world. To quantify the uncertain impacts of climate variations and anthropogenic activities on Ajichay River flow in Iran, a multi-model ensemble approach based on the Bayesian model averaging (BMA) method is applied. Several statistical and simulation-based methods are used to distinguish the impacts of climatic and anthropogenic factors on river flow. The results show that almost all the methods identified human activities as the dominant impact on streamflow (about 73–85% of the change). The between-model and within-model uncertainty analyses using BMA showed that the 95% uncertainty intervals of the individual approaches have relatively large deviation ranges. The BMA mean prediction could reduce the range of between-model uncertainties to 14–27% for climate impacts and 74–80% for human impacts. This approach provides a way to better understand the contributions of climatic and anthropogenic impacts on river flow change.