Calibration Of Model Parameters Research Articles

Parallelization of AMALGAM algorithm for a multi-objective optimization of a hydrological model

A calibration procedure is essential step to achieve a realistic model simulation particularly in hydrological model which simulates water cycle in the basin. This process is always faced with challenges due to selection of objective function and highly time-consuming. This study aimed to take advantage of parallel processing to accelerate the computations involved with simulation process of hydrologic model linked with the multi-objective optimization algorithm of AMALGAM for multi-site calibration of SWAT hydrologic model parameters. In order to illustrate how meaningful SWAT model calibration trade-off between the four objective functions involved in AMALGAM optimization program, the Pareto solution sets were provided. Furthermore, it is implemented a group of model runs with a number of cores involved (from one to eight) to demonstrate and evaluate the running of parallelized AMALGAM with taking advantages of “spmd” method to decrease the running time of the SWAT model. The results revealed the robustness of the method in reducing computational time of the parameter calibration significantly. This strategy with 4-objective functions focuses on high streamflow (Nash–Sutcliffe coefficient), low streamflow (Box–Cox transformed root–mean–square error), water balance (runoff coefficient error), and flashiness (slope of the flow duration curve error) provided an efficient tool to decide about the best simulation based on the investigated objective functions. This study also provides a strong basis for multi-objective optimization of hydrological and water quality models and its general analytical framework could be applied to other parts of the world.

Bias-blind and bias-aware assimilation of leaf area index into the Noah-MP land surface model over Europe

Abstract. Data assimilation (DA) of remotely sensed leaf area index (LAI) can help to improve land surface model estimates of energy, water, and carbon variables. So far, most studies have used bias-blind LAI DA approaches, i.e. without correcting for biases between model forecasts and observations. This might hamper the performance of the DA algorithms in the case of large biases in observations or simulations or both. We perform bias-blind and bias-aware DA of Copernicus Global Land Service LAI into the Noah-MP land surface model forced by the ERA5 reanalysis over Europe in the 2002–2019 period, and we evaluate how the choice of bias correction affects estimates of gross primary productivity (GPP), evapotranspiration (ET), runoff, and soil moisture. In areas with a large LAI bias, the bias-blind LAI DA leads to a reduced bias between observed and modelled LAI, an improved agreement of GPP, ET, and runoff estimates with independent products, but a worse agreement of soil moisture estimates with the European Space Agency Climate Change Initiative (ESA CCI) soil moisture product. While comparisons to in situ soil moisture in areas with weak bias indicate an improvement of the representation of soil moisture climatology, bias-blind LAI DA can lead to unrealistic shifts in soil moisture climatology in areas with strong bias. For example, when the assimilated LAI data in irrigated areas are much higher than those simulated without any irrigation activated, LAI will be increased and soil moisture will be depleted. Furthermore, the bias-blind LAI DA produces a pronounced sawtooth pattern due to model drift between DA updates, because each update pushes the Noah-MP leaf model to an unstable state. This model drift also propagates to short-term estimates of GPP and ET and to internal DA diagnostics that indicate a suboptimal DA system performance. The bias-aware approaches based on a priori rescaling of LAI observations to the model climatology avoid the negative effects of the bias-blind assimilation. They retain the improvements in GPP anomalies from the bias-blind DA but forego improvements in the root mean square deviations (RMSDs) of GPP, ET, and runoff. As an alternative to rescaling, we discuss the implications of our results for model calibration or joint parameter and state update DA, which has the potential to combine bias reduction with optimal DA system performance.

Simulation of bi-directional pedestrian flow in corridor based on direction fuzzy visual field

Bi-directional pedestrian flow in corridors is a complex dynamic system due to the diversity in pedestrian psychological characteristics. Incorporating individual differences of pedestrians is vital for improving pedestrian flow models. However, due to the inherent complexity and variability of pedestrian movement, model parameter calibration remains challenging. Controlled experiments are needed to collect empirical pedestrian movement data under different environments. This enriches the database on pedestrian movement patterns and provides necessary support for improving pedestrian flow models. To address this issue, we conducted controlled experiments to quantify pedestrian heterogeneity by defining the direction of fuzzy visual field (DFVF). The DFVF incorporates various static and dynamic pedestrian factors. We used it to modify the traditional cellular automata model. This improved model simulates bi-directional pedestrian movements in the corridors, reproduces density-speed and density-volume relationships, and reveals self-organization phenomena. Furthermore, an analysis was conducted to examine the impacts of pedestrian density and facility spatial layout on evacuation time. Pedestrian interactions were also studied to uncover fundamental bi-directional flow properties. As pedestrian density increased, the evacuation time showed an exponential upward trend. Corridor length significantly impacts evacuation time, while increasing corridor width helps control it. As crowd density increases, pedestrian flows exhibit three distinct steady states: the strolling flow at low densities, directional separated flows at medium densities, and dynamic multi-lane flows at high densities. In summary, the modified cellular automata model successfully incorporates pedestrian heterogeneity and reveals intrinsic bi-directional pedestrian flow patterns. This study provides valuable insights for pedestrian facility design and optimizing pedestrian flow organization.

Seismic performance of a sheet-pile retaining structure in liquefiable soils: Numerical simulations of LEAP-2022 centrifuge tests

Seismic performance of a sheet-pile retaining structure in liquefiable soils: Numerical simulations of LEAP-2022 centrifuge tests

An inverse method for automatic determination of material models for metal cutting based on multi-objective optimization

Cutting simulation is a crucial tool that enables engineers and operators to optimize machining processes virtually, before producing physical parts. The accuracy of these simulations relies heavily on validated models, encompassing both friction and material parameters. The prevalent technique for calibrating material models in cutting simulations is the inverse method. This state-of-the-art approach indirectly determines model parameters by comparing simulated outcomes with experimental data. However, the manual calibration process can be complex and time-consuming due to the intricacies of numerical simulation setups and the abundance of material model parameters. To address these challenges, this paper presents a novel fully-automated calibration approach utilizing multi-objective optimization algorithms. This approach integrates a modular design, simplifying the calibration process and enabling automatic calibration of any model parameters within cutting simulations. The approach has been successfully applied to calibrate the model parameters of AISI 1045 and X30CrMoN15-1 materials. Moreover, through a comparison of various optimization algorithms, this paper underscores the efficiency of the swarm optimizer in calibrating model parameters, particularly in scenarios with restricted computational resources.

A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest

A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest

Method for predicting the wettability of micro-structured surfaces by continuum phase-field modelling

Method for predicting the wettability of micro-structured surfaces by continuum phase-field modelling

Two-dimensional evolution of temperature and deformation fields during dynamic shear banding: In-situ experiments and modeling

Two-dimensional evolution of temperature and deformation fields during dynamic shear banding: In-situ experiments and modeling

Retail location modeling of supermarket chains in Taipei city

Retail location modeling of supermarket chains in Taipei city

Model Updating Based on Bayesian Theory and Improved Objective Function

Model updating is the process of calibrating model parameters to improve the accuracy of numerical prediction. To improve the accuracy and efficiency of model updating, this paper proposes a model updating method based on Bayesian theory and improved objective function. A natural frequency damage index is proposed based on the Bayesian theory, which is calculated according to the established damage position function and the measured frequency data. The distribution of the index can determine the damage location and the number of updated parameters for model updating. An objective function with weight terms is proposed based on strain assurance criterion to describes the difference between the finite element model and the actual structure, and the weight term of the objective function is determined by the sensitivity coefficient. Examples show that the improved model updating method is more accurate and efficient.

BITS: A Bayesian Isotope Turnover and Sampling model for strontium isotopes in proboscideans and its potential utility in movement ecology

Abstract Strontium isotope ratios (87Sr/86Sr) of incrementally grown tissues have been widely used to study movement ecology and migration of animals. However, the time scale of 87Sr/86Sr incorporation from the environment into tissue and how it may influence data interpretation are still poorly understood. Using the relocation of a zoo elephant (Loxodonta africana) named Misha, we characterise and model the 87Sr/86Sr turnover process using high‐resolution measurements of its tusk dentine. We seek to develop a framework that can improve quantitative interpretation of 87Sr/86Sr data in tissues. The 87Sr/86Sr transition associated with the relocation is measured using laser ablation inductively coupled plasma mass spectrometry (LA‐ICP‐MS) on a prepared tusk slab. We develop a turnover model (BITS), with a rapidly exchanging central pool and a slowly exchanging peripheral pool, in a Bayesian statistical framework. The measured dentine data are first used to calibrate model parameters. The parameters are then used to estimate possible 87Sr/86Sr input time series from two datasets via model inversion: a fidelity test using Misha's dentine data and a case study using published dentine measurements from an Alaskan Woolly Mammoth (Mammuthus primigenius). The LA‐ICP‐MS data are consistent with a two‐compartment turnover process with equivalent half‐lives of 41 days for the central pool and 170 days for the peripheral pool. The model inversion shows good fidelity when estimating the intake 87Sr/86Sr time series associated with Misha's relocation. In the case study, the model suggests an abrupt pattern of change in, and a much wider range of, intake 87Sr/86Sr values than expressed in the woolly mammoth dentine data themselves. Our framework bridges the gap between environmental 87Sr/86Sr variation and data measured in tusk dentine or other incrementally grown tissues. It could be coupled with movement models and additional isotope tracers to study seasonal residency or the spatial and temporal patterns of movement/migration. The generic turnover processes can be adapted to other isotope systems, additional incremental tissues, or other organisms, thus expanding our modelling toolkit to investigate niche partitioning, life history traits and behavioural patterns in conservation biology, archaeology and paleoecology.

Bayesian-based calibration for water quality model parameters.

To improve the efficiency and accuracy of water quality model parameter calibration and avoid local optima and the phenomenon in which different parameters have the same effect, this paper proposed a novel Bayesian-based water quality model parameter calibration method. Using Bayesian inference, the parameter calibration problem was converted into a posterior probability function sampling problem, which was sampled using the Markov Chain Monte Carlo algorithm. The convergence speed of the calibration was further improved by setting the optimized initial sampling value. The influences of the initial sampling value, Markov chain length, and proposal distribution form on the calibration effect were evaluated using four specific cases. The results indicate that (1) the mean relative error (MRE) of the parameter calibration results of this method is less than 10%, with the calibration MRE of Dx and Dy being 5.3% and 8.3%, respectively; (2) when the parameter sensitivity is low, the calibration effect of this method is relatively poor, with a calibration MRE of 46% for k; (3) the parameter calibration can be completed more efficiently by setting an optimized initial value for the MCMC, choosing a reasonable Markov chain length and a suitable proposal distribution form. PRACTITIONER POINTS: Bayesian-based water quality model parameter calibration method is proposed and posterior probability distribution was sampled using the MCMC algorithm. Parameter calibration can be completed more efficiently by setting an optimized initial value for the MCMC. As a result, efficient and accurate parameter calibration of water quality models was achieved. This method is widely applicable to various models, and the calibration speed depends on the calculation speed of the model.

An advanced tool integrating failure and sensitivity analysis into novel modeling of the stormwater flood volume

Abstract. An innovative tool for modeling the specific flood volume was presented that can be applied to assess the need for stormwater network modernization as well as for advanced flood risk assessment. Field measurements for a catchment area in Kielce, Poland, were used to apply the model and demonstrate its usefulness. This model extends the capability of recently developed statistical and machine learning hydrodynamic models developed from multiple runs of the US Environmental Protection Agency (EPA) Storm Water Management Model (SWMM). The extensions enable the inclusion of (1) the characteristics of the catchment and its stormwater network, calibrated model parameters expressing catchment retention, and the capacity of the sewer system; (2) extended sensitivity analysis; and (3) risk analysis. Sensitivity coefficients of calibrated model parameters include correction coefficients for percentage area, flow path, depth of storage, and impervious area; Manning roughness coefficients for impervious areas; and Manning roughness coefficients for sewer channels. Sensitivity coefficients were determined with respect to rainfall intensity and characteristics of the catchment and stormwater network. Extended sensitivity analysis enabled an evaluation of the variability in the specific flood volume and sensitivity coefficients within a catchment, in order to identify the most vulnerable areas threatened by flooding. Thus, the model can be used to identify areas particularly susceptible to stormwater network failure and the sections of the network where corrective action should be taken to reduce the probability of system failure. The simulator developed to determine the specific flood volume represents an alternative approach to the SWMM that, unlike current approaches, can be calibrated with limited topological data availability; therefore, the aforementioned simulator incurs a lower cost due to the lower number and lower specificity of data required.

Thermal and stoichiometry inhomogeneity investigation of large-format lithium-ion batteries via a three-dimensional electrochemical-thermal coupling model

Thermal and stoichiometry inhomogeneity investigation of large-format lithium-ion batteries via a three-dimensional electrochemical-thermal coupling model

DEM analyses of mesoscopic failure mechanism and stress bearing mechanism of compacted snow subjected to unconfined compressive loading

DEM analyses of mesoscopic failure mechanism and stress bearing mechanism of compacted snow subjected to unconfined compressive loading

Parametric Modelling of Rousselier ́s Damage Model Parameters with Instrumented Charpy Tests

This work presents Rousselier´s damage model parameters effect and their physical meaning on Charpy fracture propagation curves. Therefore, instrumented Charpy tests were performed at room test temperature to measure the load-displacement curve. The parameters were measured from a Grade A ship plate steel, employed for the construction of merchant ships. The effect of Rousselier´s model parameter was done by performing cellular automata finite element (CAFE) modelling, where Rousselier’s damage model was coded, and therefore Rousselier´s model parameters were incorporated using random number generators in the ductile arrays of cells, using Weibull distributions. Consequently, in each CAFE simulation, the model evaluates random values of Rousselier´s damage model parameters performing a more physically based modelling. The results showed that the present CAFE modelling was able to reproduce the hardening and fracture propagation regions of instrumented Charpy data. Furthermore, the present work showed a suitable Rousselier´s damage model parameters calibration procedure with Charpy data, and how each Rousselier´s model parameter can affect the hardening and fracture propagations regions when they are not properly calibrated, producing unrealistic results. Additionally, it can be observed that the present results can be used as a template for a better calibration of Rousselier´s damage model parameters in CAFE modelling.

UISCEmod: Open-source software for modelling water level time series in ephemeral karstic wetlands

Characterizing ephemeral karstic wetlands through hydrological modelling is key for sustainable protection of their ecosystems and to understand and mitigate the impact of flooding events. UISCEmod is a new open-source software for modelling water level time series, focused on ephemeral karstic wetlands, that requires minimal input information. UISCEmod contains both experimental and lumped hydrological models, and the calibration process is automated following a Bayesian approach. The main outputs of UISCEmod include volume, stage, inflow and outflow model time series, calibrated model parameters, and the associated uncertainties. UISCEmod was evaluated at 16 representative sites in Ireland obtaining Nash-Sutcliffe Efficiency (NSE) and Kling-Gupta Efficiency (KGE) above 0.85 for both stage and volume time series for most of the sites, showing its potential for covering the need for a simple, pragmatic, and flexible framework for modelling water levels in ephemeral karstic wetlands with relatively limited input data requirements.

Discrete element simulations of flexible ribbon-like particles

Discrete element simulations of flexible ribbon-like particles

Machine learning-enabled calibration of river routing model parameters

Abstract Streamflow prediction of rivers is crucial for making decisions in watershed and inland waterways management. The US Army Corps of Engineers (USACE) uses a river routing model called RAPID to predict water discharges for thousands of rivers in the network for watershed and inland waterways management. However, the calibration of hydrological streamflow parameters in RAPID is time-consuming and requires streamflow measurement data which may not be available for some ungauged locations. In this study, we aim to address the calibration aspect of the RAPID model by exploring machine learning (ML)-based methods to facilitate efficient calibration of hydrological model parameters without the need for streamflow measurements. Various ML models are constructed and compared to learn a relationship between hydrological model parameters and various river parameters, such as length, slope, catchment size, percentage of vegetation, and elevation contours. The studied ML models include Gaussian process regression, Gaussian mixture copula, Random Forest, and XGBoost. This study has shown that ML models that are carefully constructed by considering causal and sensitive input features offer a potential approach that not only obtains calibrated hydrological model parameters with reasonable accuracy but also bypasses the current calibration challenges.

Validating DayCent-CR for cropland soil carbon offset reporting at a national scale

Validating DayCent-CR for cropland soil carbon offset reporting at a national scale