Complexity Of Parameterization Research Articles

Methodological Insights into Implementing cellular automata models for simulating seagrass dynamics: Responses to global change effects

This study introduces an innovative methodology employing Cellular Automata (CA) models to simulate seagrass dynamics in response to global environmental changes. The primary objective is to outline a procedural framework for constructing and deploying CA models applied to seagrass ecosystems, and potentially to other marine or terrestrial environments. The methodology encompasses various components, including conceptualization, workflow delineation, model parameterization, and execution steps. By utilizing Mediterranean and Zanzibari (East Africa) seagrass ecosystems as case studies, we demonstrate the versatility and applicability of the proposed approach across diverse geographical regions, species composition and model components. Through these case studies, we demonstrated how CA models can effectively capture the dynamics of seagrass communities subjected to climate change, invasive species, and nutrient regimes. Despite its strengths, the proposed CA model has limitations, including parameterization complexity and uncertainties related to species-specific environmental thresholds, growth rates and species interactions, alongside the difficulty of validating our models with real-world scenarios. Addressing these limitations in future studies will enhance the model's accuracy and applicability. This study serves as a foundation for future research in other regions and ecosystems, facilitating a better understanding of the complex interactions driving ecosystem dynamics.•This study introduces a methodology using Cellular Automata (CA) models to simulate seagrass dynamics detailing conceptualization, workflow, parameterization, and execution.•Case studies in Mediterranean and East Africa ecosystems demonstrate the versatility of CA models in capturing the impacts of climate change, invasive species, and nutrient regimes.•Despite strengths, the CA model has limitations and uncertainties like parameterization complexity and model validations suggesting future research to enhance accuracy and applicability.

Aerothermal optimization of turbine cascade squealer tip with non-uniform squealer height

The squealer tip has significant influence on both the aerodynamic and heat transfer characteristics of the high-pressure turbine blade. However, due to the complexity of parameterization and meshing of the squealer and the complicated flow structure within the over-tip region, the existing squealer designs in the open literature have constant squealer heights. In this paper, the design space to the squealer height with non-uniform squealer height is extended and the new flow features it may bring are investigated. A parameterization system specifically designed for the non-uniform squealer height using five control parameters is implemented to automatically generate the geometry and hybrid meshes. Combining it with the multi-objective optimization system using genetic algorithms, a transonic turbine cascade squealer tip is optimized employing Reynolds-averaged Navier–Stokes k–ω shear stress transport model. The main objective of this study is to obtain a squealer configuration with the lowest total pressure loss coefficient and heat transfer coefficient. The optimum configuration with non-uniform squealer height achieves improvements in both the aerodynamic efficiency and the heat transfer performance, relative to the baseline conventional squealer tip geometry with the constant squealer height. Additionally, this work demonstrates that a flow structure in which the main flow forms a “blanket” below the leakage flow in the squealer is beneficial for aerothermal performance, especially reducing heat transfer losses, which provides valuable insight into the squealer tip design of advanced high-pressure turbines.

A distributed agroecosystem model (RegWHCNS) for water and N management at the regional scale: A case study in the North China Plain

Process-based agroecosystem model is difficult to apply at the regional scale due to the complexity of model parameterization and execution efficiency. Thus, developing a regional agroecosystem model is challenging. In this study, a parallel computing framework is introduced to upscale an agroecosystem model (WHCNS model) from field to regional scale by integrating parallel computing, inversing modeling, and spatial calculation. The newly developed model was tested in the North China Plain (NCP) for modeling water consumption, N fate, and crop growth for winter wheat–summer maize rotation system under different water and N management practices. The optimum irrigation water and fertilizer N demands of winter wheat–summer maize system was estimated based on the auto-irrigation and auto-fertilization component in the model. Multisite testing showed that the model well reproduced soil water and N dynamics, crop biomass, LAI, yield, and crop N uptake, with R2 exceeding 0.886. The estimated annual average irrigation water and fertilizer N demands of winter wheat–summer maize rotation system was 395.8 mm and 529.5 kg N ha−1, respectively. Winter wheat was the major consumer of irrigation water and fertilizer N, accounting for 79.2 % and 54.4 % of annual irrigation water and chemical N fertilizer use, respectively. These results suggested that winter wheat was a crop with high water and fertilizer N demand in the NCP. The RegWHCNS model has potential value to assess and optimize regional water and N management for intensive agricultural-production systems. This study can provide as a scientific reference to upscale agroecosystem models from field to region for other agroecosystem models.

The Impact of Groundwater Model Parametrization on Calibration Fit and Prediction Accuracy—Assessment in the Form of a Post-Audit at the SLOVNAFT Oil Refinery Site, in Slovakia

The present work is focused on the effect of increasing model complexity on calibration fit and prediction accuracy. Groundwater flow was numerically simulated at a field site with a hydraulic groundwater protection system in operation with many pumping and observation wells at the site of the Slovnaft refinery in southwestern Slovakia. The adjusted parameters during the calibration included hydraulic conductivity, as well as recharge, evapotranspiration, and riverbed conductance. Four model scenarios were built (V1–V4) within the model calibration for the conditions in the year 2008, with increasing complexity mainly within artificial K-field zonation, which was created and step-wise upgraded based on groundwater head residuals’ distribution. Selected descriptive statistics were evaluated together with chosen information criteria after the models were calibrated. Subsequently, the real predictive accuracy of individual calibrated scenarios was evaluated for conditions in the year 2019 in the form of a post-audit. Within the overall evaluation, the calibration fit increased with increased parameterization complexity. However, the Akaike information criterion, corrected Akaike information criterion, and Bayesian information criterion detected opposite trends for model predictability. A post-audit of prediction accuracy revealed a significant improvement of the V2, V3, and V4 scenarios against the simplest V1 scenario. However, among the V2–V4 scenarios, the degree of prediction accuracy improvement was almost insignificant. The level of effort spent on V3 and V4 parameterization seems disproportionate to the benefit of a negligible improvement in prediction accuracy. Groundwater flow path analysis showed that similarly successful scenarios (measured by prediction accuracy) can generate very different groundwater pathlines.

A semi-mechanistic model for predicting daily variations in species-level live fuel moisture content

• Live Fuel Moisture Content (LFMC) is an essential constraint of wildfire activity. • LFMC estimated from weather/vegetation indices lacks species-specific predictions. • We use a water balance model to predict species-specific LFMC from water potential. • Our model shows an improvement in predictive performance over existing approaches. • Our approach can be implemented within large-scale fire danger forecast systems. Live Fuel Moisture Content (LFMC) is one of the main factors affecting forest ignitability as it determines the availability of existing live fuel to burn. Currently, LFMC is monitored through spectral vegetation indices or inferred from meteorological drought indices. While useful, neither approach provides mechanistic insights into species-specific LFMC variation and they are limited in the ability to forecast LFMC under altered future climates. Here, we developed a semi-mechanistic model to predict daily variation in LFMC across woody species from different functional types by adjusting a soil water balance model which estimates predawn leaf water potential (Ψ pd ). Our overarching goal was to balance the trade-off between biological realism, which enhances model applicability, and parameterization complexity, which may limit its value within operational settings. After calibration, model predictions were validated against a dataset comprising 1659 LFMC observations across peninsular Spain, belonging to different functional types and from contrasting climates. The overall goodness of fit for our model ( R 2 = 0.5) was better than that obtained by an existing models based on drought indices ( R 2 = 0.3) or spectral vegetation indices ( R 2 = 0.1). We observed the best predictive performance for seeding shrubs ( R 2 = 0.6) followed by trees ( R 2 = 0.5) and resprouting shrubs ( R 2 = 0.4). Through its relatively simple parameterization, the approach developed here may pave the way for a new generation of process-based models that can be used for operational purposes within fire risk mitigation scenarios.

A Bimodal Diagnostic Cloud Fraction Parameterization. Part II: Evaluation and Resolution Sensitivity

AbstractA wide range of approaches exists to account for subgrid cloud variability in regional simulations of the atmosphere. This paper addresses the following questions: 1) Is there still benefit in representing subgrid variability of cloud in convection-permitting simulations? 2) What is the sensitivity to the cloud fraction parameterization complexity? 3) Are current cloud fraction parameterizations scale-aware across convection-permitting resolutions? These questions are addressed for regional simulations of a 6-week observation campaign in the U.S. southern Great Plains. Particular attention is given to a new diagnostic cloud fraction scheme with a bimodal subgrid saturation-departure PDF, described in Part I. The model evaluation is performed using ground-based remote sensing synergies, satellite-based retrievals, and surface observations. It is shown that not using a cloud fraction parameterization results in underestimated cloud frequency and water content, even for stratocumulus. The use of a cloud fraction parameterization does not guarantee improved cloud property simulations, however. Diagnostic and prognostic cloud schemes with a symmetric subgrid saturation-departure PDF underestimate cloud fraction and cloud optical thickness, and hence overestimate surface shortwave radiation. These schemes require empirical bias-correction techniques to improve the cloud cover. The new cloud fraction parameterization, introduced in Part I, improves cloud cover, liquid water content, cloud-base height, optical thickness, and surface radiation compared to schemes reliant on a symmetric PDF. Furthermore, cloud parameterizations using turbulence-based, rather than prescribed constant subgrid variances, are shown to be more scale-aware across convection-permitting resolutions.

Three-dimensional hydraulic tomography analysis of long-term municipal wellfield operations: Validation with synthetic flow and solute transport data

This study proposes the utilization of municipal well records as an alternative dataset for large-scale heterogeneity characterization of hydraulic conductivity (K) and specific storage (Ss) using hydraulic tomography (HT). To investigate the performance of HT and the feasibility of utilizing municipal well records, a three-dimensional aquifer/aquitard system is constructed and synthetic groundwater flow and solute transport experiments are conducted to generate data for inverse modeling and validation of results. In particular, we simultaneously calibrate four groundwater models with varying parameterization complexity using five datasets consisting of different time durations and periods. Calibration and validation results are qualitatively and quantitatively assessed to evaluate the performance of investigated models. The estimated K and Ss tomograms from different model cases are also validated through the simulation of independently conducted pumping tests and conservative solute transport. Our study reveals that: 1) the HT analysis of municipal well records is feasible and yields reliable heterogeneous K and Ss distributions where drawdown records are available; 2) accurate geological information is of critical importance when data density is low and should be incorporated for geostatistical inversions; 3) the estimated K and Ss tomograms from the geostatistical model with geological information are capable in providing robust predictions of both groundwater flow and solute transport. Overall, this synthetic study provides a general framework for large-scale heterogeneity characterization using HT through the interpretation of municipal well records, and provides guidance for applying this concept to field problems.

Solving hard stable matching problems involving groups of similar agents

Many important stable matching problems are known to be NP-hard, even when strong restrictions are placed on the input. In this paper we seek to identify structural properties of instances of stable matching problems which will allow us to design efficient algorithms using elementary techniques. We focus on the setting in which all agents involved in some matching problem can be partitioned into k different types, where the type of an agent determines his or her preferences, and agents have preferences over types (which may be refined by more detailed preferences within a single type). This situation would arise in practice if agents form preferences solely based on some small collection of agents' attributes. We also consider a generalisation in which each agent may consider some small collection of other agents to be exceptional, and rank these in a way that is not consistent with their types; this could happen in practice if agents have prior contact with a small number of candidates. We show that (for the case without exceptions), several well-studied NP-hard stable matching problems including Max SMTI (that of finding the maximum cardinality stable matching in an instance of stable marriage with ties and incomplete lists) belong to the parameterised complexity class FPT when parameterised by the number of different types of agents needed to describe the instance. For Max SMTI this tractability result can be extended to the setting in which each agent promotes at most one “exceptional” candidate to the top of his/her list (when preferences within types are not refined), but the problem remains NP-hard if preference lists can contain two or more exceptions and the exceptional candidates can be placed anywhere in the preference lists, even if the number of types is bounded by a constant.

Model complexity affects species distribution projections under climate change

AbstractAimStatistical species distribution models (SDMs) are the most common tool to predict the impact of climate change on biodiversity. They can be tuned to fit relationships at various levels of complexity (defined here as parameterization complexity, number of predictors, and multicollinearity) that may co‐determine whether projections to novel climatic conditions are useful or misleading. Here, we assessed how model complexity affects the performance of model extrapolations and influences projections of species ranges under future climate change.LocationEurope.Taxon34 European tree species.MethodsWe sampled three replicates of predictor sets for all combinations of 10 levels (n = 3–12) of environmental variables (climate, terrain, soil) and 10 levels of multicollinearity. We used these sets for each species to fit four SDM algorithms at three levels of parameterization complexity. The >100,000 resulting SDM fits were then evaluated under environmental block cross‐validation and projected to environmental conditions for 2061–2080 considering four climate models and two emission scenarios. Finally, we investigated the relationships of model design with model performance and projected distributional changes.ResultsModel complexity affected both model performance and projections of species distributional change. Fits of intermediate parameterization complexity performed best, and more complex parameterizations were associated with higher projected loss of current ranges. Model performance peaked at 10–11 variables but increasing number of variables had no consistent effect on distributional change projections. Multicollinearity had a low impact on model performance but distinctly increased projected loss of current ranges.Main conclusionsSDM‐based climate change impact assessments should be based on ensembles of projections, varying SDM algorithms as well as parameterization complexity, besides emission scenarios and climate models. The number of predictor variables should be kept reasonably small and the classical threshold of maximum absolute Pearson correlation of 0.7 restricts collinearity‐driven effects in projections of species ranges.

A Fixed-Parameter Perspective on #BIS

The problem of (approximately) counting the independent sets of a bipartite graph (#BIS) is the canonical approximate counting problem that is complete in the intermediate complexity class mathsf {#RH}Pi _1. It is believed that #BIS does not have an efficient approximation algorithm but also that it is not NP-hard. We study the robustness of the intermediate complexity of #BIS by considering variants of the problem parameterised by the size of the independent set. We map the complexity landscape for three problems, with respect to exact computation and approximation and with respect to conventional and parameterised complexity. The three problems are counting independent sets of a given size, counting independent sets with a given number of vertices in one vertex class and counting maximum independent sets amongst those with a given number of vertices in one vertex class. Among other things, we show that all of these problems are NP-hard to approximate within any polynomial ratio. (This is surprising because the corresponding problems without the size parameter are complete in mathsf {#RH}Pi _1, and hence are not believed to be NP-hard.) We also show that the first problem is #W[1]-hard to solve exactly but admits an FPTRAS, whereas the other two are W[1]-hard to approximate even within any polynomial ratio. Finally, we show that, when restricted to graphs of bounded degree, all three problems have efficient exact fixed-parameter algorithms.

The Relative Impact of Ice Fall Speeds and Microphysics Parameterization Complexity on Supercell Evolution

Abstract The use of bin or bulk microphysics schemes in model simulations frequently produces large changes in the simulated storm and precipitation characteristics, but it is still unclear which aspects of these schemes give rise to these changes. In this study, supercell simulations using either a bin or a double-moment bulk microphysics scheme are conducted with the Regional Atmospheric Modeling System (RAMS). The two simulations produce very different storm morphologies. An additional simulation is run for each scheme in which the diameter–fall speed relationships for ice hydrometeors are modified to be similar to those used by the other scheme. When fall speed relationships are homogenized, the two parameterization schemes simulate similar storm morphology. Therefore, despite the use of largely dissimilar approaches to parameterizing microphysics, the difference in storm morphology is found to be related to the choice of diameter–fall speed relationships for ice hydrometeors. This result is investigated further to understand why. Higher fall speeds lead to higher mixing ratios of hydrometeors at low levels and thus more melting. Consequently, stronger downdrafts and cold pools exist in the high fall speed storms, and these stronger cold pools lead to storm splitting and the intensification of a left mover. The results point to the importance of hydrometeor fall speed on the evolution of supercells. It is also suggested that caution be used when comparing the response of a cloud model to different classes of microphysics schemes since the assumptions made by the schemes may be more important than the scheme class itself.

A probabilistic approach for the detection of bolt loosening in periodically supported structures endowed with bolted flange joints

Abstract From the literature, only very limited research activities have been carried out for fault diagnose of periodic structural system following model-based approaches. This paper focuses on the development of a practical methodology for modeling and detecting bolt loosening on periodically supported beam-type structure endowed with bolted flange joints, representing typical supported pipeline system in industry, through using measured modal parameters. Within the framework of periodic system, an efficient analytical model of the complete periodic system is first developed for dynamic analysis in frequency domain. The highly accurate spectral element method is employed to formulate the supercell-based dynamic stiffness matrix (DSM) of periodic cell containing bolted flange connection in the midspan, and the transfer matrix-based method is also developed for assembling the system DSM of entire periodic structural system through the obtained DSM of each individual cell, where the computational effort required in dynamic analysis of the complete periodic system with a large amount of repeated cells is almost comparable to a single cell. Then, in the proposed methodology, the statistical detection of bolt loosening is accomplished through two phases. The most plausible model class with appropriate parameterization complexity is first recognized by following the Bayesian model class selection strategy in the first phase. In the subsequent phase, the posterior probability density function of the stiffness scaling parameters is identified following the particle filter-based approach. To demonstrate and validate the proposed methodology, this paper reports not only the theoretical development but also a comprehensive series of numerical and experimental case studies, and corresponding results achieved are very encouraging.

The many facets of upper domination

This paper studies Upper Domination, i.e., the problem of computing the maximum cardinality of a minimal dominating set in a graph with respect to classical and parameterised complexity as well as approximability.

Realistic simulations of the July 1, 2011 severe wind event over the Buffalo Ridge Wind Farm

AbstractSevere winds from thunderstorm outflows pose a challenge to wind turbine arrays. They can cause significant power ramps and disruption in energy production. They can also cause extreme structural damage to turbines as was seen in the severe storm event over the Buffalo Ridge Wind Farm on July 1, 2011. At this southwestern Minnesota site, blades from multiple turbines broke away and a tower buckled in the intense winds. In this study, we attempt to characterize meteorological conditions over the Buffalo Ridge Wind Farm area during this event. The observational network included NEXRAD radars, automated surface observation stations and a wind profiler. Storm reports from the Storm Prediction Center and damage surveys provided additional insight to the in situ measurements. Even with these datasets, assessing wind speeds around turbine rotors is difficult. Thus, Weather Research and Forecasting model simulations of the event are carried out that consider current and anticipated future operational model setups. This work addresses model spatial resolution versus parameterization complexity. Parameterizations of the planetary boundary layer and microphysics processes are evaluated based on their impact on storm dynamics and the low‐level wind field. Results are also compared with the Wind Integration National Dataset, which utilizes data assimilation and an extensive continental domain. Enhanced horizontal resolution with simplistic parameterization helps increase resolved wind speeds and ramp intensity. Enhanced sophistication of microphysics parameterizations also helps increase resolved wind speeds, improve storm timing and structure and resolve higher values of turbulent kinetic energy in the lowest 1 km of the atmosphere. Copyright © 2017 John Wiley & Sons, Ltd.

A Multi-Parameter Complexity Analysis of Cost-Optimal and Net-Benefit Planning

Aghighi and Bäckström have previously studied cost-optimal planning (COP) and net-benefit planning (NBP) for three action cost domains: the positive integers (Z_+), the non-negative integers (Z_0) and the positive rationals (Q_+). These were indistinguishable under standard complexity analysis for both problems, but separated for COP using parameterised complexity analysis. With the plan cost, k, as parameter, COP was W[2]-complete for Z_+, but para-NP-hard for both Z_0 and Q_+, i.e. presumably much harder. NBP was para-NP-hard for all three domains, thus remaining unseparable. We continue by considering combinations with several additional parameters and also the non-negative rationals (Q_0). Examples of new parameters are the plan length, l, and the largest denominator of the action costs, d. Our findings include: (1) COP remains W[2]-hard for all domains, even if combining all parameters; (2) COP for Z_0 is in W[2] for the combined parameter {k,l}; (3) COP for Q_+ is in W[2] for {k,d} and (4) COP for Q_0 is in W[2] for {k,d,l}. For NBP we consider further additional parameters, where the most crucial one for reducing complexity is the sum of variable utilities. Our results help to understand the previous results, eg. the separation between Z_+ and Q_+ for COP, and to refine the previous connections with empirical findings.

Evaluation of simple to complex parameterizations of bare ground evaporation

AbstractIntegrated hydrologic models coupled to land surface models link water and energy movement among the subsurface, land surface, and atmosphere. These connections are especially important when estimating a complex, nonlinear process like evaporation. A comprehensive sensitivity study of an evaporation parameterization was conducted using the integrated ParFlow‐Common Land Model (PF‐CLM). Estimates of ground evaporation using three forms of the same equation, two simplified closed‐form solutions and one fully‐coupled PF‐CLM equation, are systematically compared. The parameterizations vary in complexity, coupling strength, and nonlinearity. Forcing data from three climate regions (alpine, plains, and tropical) are used to compare estimates of bare ground evaporation across all three formulations, thus exploring the process and coupling sensitivity in a novel way. The overall behavior of ground evaporation is consistent throughout the year for all parameterizations, but magnitudes vary with respect to parameterization complexity during energy‐limited and water‐limited times of the year. A relationship between ground evaporation and ground temperature is shown to exist across all climates and aggregate by pressure, wind speed, and air temperature in the plains climate. Furthermore, results show how increasing complexity through the addition of land surface conditions, atmospheric conditions, and atmospheric stability uniquely compound to influence the relationship between bare ground evaporation and subsurface pressure. Identification of sensitive interactions and unique relationships is necessary to further understand and predict hydrologic processes like evaporation.

Self-adjusting Process Monitoring System in Series Production

Modern monitoring systems in machine tools are able to detect process errors promptly. Still, the application of monitoring systems is restricted by the complexity of parameterization for save monitoring. In most cases, only specially trained personnel can handle this job especially for multi-purpose machines. The aim of the research project “Proceed” is to figure out in which extent a self-parameterization and autonomous optimization of monitoring systems in industrial series production can be realized. Therefore, a self-adjusting and self-tuning process monitoring system for series production has been developed. This system is based on multi-criteria sensor signal evaluation and is able to assess its monitoring quality quantitatively. For this purpose, the complete process chain of parameterization has been automated. For series production it is assumed, that the first process is not defective. So, process sensitive features are identified by a correlation analysis with a reference signal. The reference signal is selected through an analysis of the process state by an expert system. To assess the monitoring quality resulting from automatic parameterization, normed specific values were used. These values describe the monitoring quality with the help of the distance between a feature and its threshold normed to signal amplitude and noise. A second indicator is the reaction of the monitoring system to a synthetic error added to signal a sequence. Thus it is possible to estimate monitoring quality corresponding to automatic parameterization. The validation is carried out by a comparison between the result of the assessment and the reaction ability of the monitoring system to real process errors from milling, drilling and turning processes.

Is Multiobjective Optimization Ready for Water Resources Practitioners? Utility’s Drought Policy Investigation

Is Multiobjective Optimization Ready for Water Resources Practitioners? Utility’s Drought Policy Investigation

Self-tuning of Teachless Process Monitoring Systems with Multi-criteria Monitoring Strategy in Series Production

Abstract Modern monitoring systems in machine tools are able to detect process errors promptly. Still, the application of monitoring systems is restricted by the complexity of parameterization for save monitoring. In most cases, only specially trained personnel can handle this job at multi-spindle machines or turn-mill centers. The aim of the research project “Proceed” is to figure out in which extent a self-parameterization and independent optimization of monitoring systems in industrial series production can be realized. Therefore, the complete parameterization of the processing chain, consisting of the choice of signal sources, character extraction, the monitoring- and decision making strategy, shall be automated. This paper deals with the self-parameterization of a multi-criteria monitoring system based on a genetic algorithm.

Groundwater modelling in decision support: reflections on a unified conceptual framework

Groundwater models are commonly used as basis for environmental decision-making. There has been discussion and debate in recent times regarding the issue of model simplicity and complexity. This paper contributes to this ongoing discourse. The selection of an appropriate level of model structural and parameterization complexity is not a simple matter. Although the metrics on which such selection should be based are simple, there are many competing, and often unquantifiable, considerations which must be taken into account as these metrics are applied. A unified conceptual framework is introduced and described which is intended to underpin groundwater modelling in decision support with a direct focus on matters regarding model simplicity and complexity.