Integrated Parameter Estimation Research Articles

Assessing the influence of land use/land cover dynamics and climate change on water resources in Upper Blue Nile, Ethiopia

ABSTRACT This study evaluates the impact of climate and land use changes on the Lake Tana Basin's hydrology, using datasets on land use, weather patterns, topography, soil characteristics, and discharge. Future climate data were obtained from Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase Five (CMIP5) and generated using the Weather Generator (LARS-WG) tool from the Long Ashton Research Station with five distinct GCMs. Land use changes were projected using the Markov chain model based on cellular automata (CA). The Soil and Water Assessment Tool (SWAT) model was used to assess changes in hydrological elements between reference and future periods, with calibration and validation ensured by the Integrated Parameter Estimation and Uncertainty Analysis Tool (IPEAT). Projections indicate a 4.9 °C increase in ensemble mean annual temperature and a 16% rise in precipitation by the end of the 21st century under Representative Concentration Pathway (RCP) 8.5. Additionally, average annual hydrological components, including water yield, soil water, percolation, lateral flow, runoff, and actual and potential evapotranspiration, are expected to increase due to combined climate and land use changes. Therefore, it is crucial to fully understand these cumulative impacts before formulating and implementing water resource management strategies in the basin.

Impacts of Land-Use Change on the Hydrology of Lake Tana Basin, Upper Blue Nile River Basin, Ethiopia.

Human activities impact hydrology through changes in land use and land cover. This study examins the effects of changing land use on hydrological processes using the soil and water assessment tool (SWAT) model. The data is acquired from Landsat 4‐5 Thematic Mapper (TM) in 1989, Landsat 7 Enhanced Thematic Mapper Plus (ETM+) in 2005, and Landsat 8 Operational Land Inventory (OLI) in 2019. Image preprocessing, which includes georeferencing, radiometric and atmospheric correction, image enhancement, band composite, mosaicking, and sub‐setting, are performed. After that, supervised classification, accuracy assessment, and change detection are carried out. The hydrological changes in 1989, 2005, and 2019 are analyzed using land‐use maps. The SWAT model's calibration, validation, and sensitivity analysis are performed using the Integrated Parameter Estimation and Uncertainty Analysis Tool in the four main rivers of the basin. Farmlands and built‐up lands are found to have steadily increased in the basin, while shrublands, grasslands, and bare lands declined. Due to an expansion of agricultural and built‐up lands and a decrease in shrublands and grasslands, the basin's mean annual water yield and surface runoff increased in 2019, while evapotranspiration and lateral flow decreased compared to 1989 and 2005. Therefore, future watershed and basin management shall consider changing land use.

Integrated methodology for state and parameter estimation of spark-ignition engines

To develop an effective control and monitoring scheme for automotive engines, a precise knowledge of the parameters and unmeasurable states of the nonlinear model capturing the overall dynamics of engines is of utmost importance. For a new vehicle out of the assembly line, the nonlinear model has constant parameters. However, in the long run, due to regular wear-and-tear, and for other unpredictable disturbances, they may change. The main challenges are how to obtain the information of parameters and states under the influence of process noise and measurement noise. To address these challenges, we present a new integrated state and parameter estimation algorithm in this paper for spark ignition (SI) engines based on the constrained unscented Kalman filter and the improved recursive least square technique. The system under consideration is a highly nonlinear mean value SI engine model consisting of the throttle, intake manifold, engine speed dynamics, and fuel system. The performance of the proposed algorithm in terms of root-mean-square-error and robustness with regards to initial conditions and random noises is analysed through exhaustive simulation scenarios considering constant, and time-varying parameters. In addition, the performance of other state-of-the-art estimation algorithms is also compared with that of the developed integrated algorithm.

Improving the flood forecasting capability of the Xinanjiang model for small- and medium-sized ungauged catchments in South China

Prediction in ungauged basins (PUB) is as crucial as it is challenging. Thus far, there have been abundant regionalization studies on PUB, whereas "regionalization" is the main research method. In order to estimate Xinanjiang model parameters in ungauged areas and improve the accuracy of flood simulation for small and medium-sized ungauged catchments, the Xinanjiang model was applied on 33 mountainous small- and medium-sized catchments in south China. This study investigated the relative benefits of traditional regionalization methods (physical similarity and parameter regression) and physically consistent parameter estimation method. The latter can directly estimate three sensitive parameters of the Xinanjiang model without the need of regionalization. In addition, the effect of the number of donor catchments was addressed. The results show that the prediction accuracy of the traditional regionalization methods did not obtain satisfactory prediction results. However, the integrated schemes, which combine the regionalization methods with physically consistent methods, performed considerably better than the traditional regionalization methods, indicating that directly performing a parameter estimation from underlying surface data of ungauged catchments can improve the transferability of the Xinanjiang model in these catchments. On the other hand, the best accuracy was obtained when the number of donor catchments was equal to five in the integrated schemes. The integrated parameter estimation schemes proposed in this study support more effective hourly flood event simulation for small- and medium-sized ungauged catchments in southern China.

Modeling projected impacts of climate and land use/land cover changes on hydrological responses in the Lake Tana Basin, upper Blue Nile River Basin, Ethiopia

This study is being conducted in Lake Tana Basin, Upper Blue Nile River Basin, Ethiopia. This work focuses on the assessment of the separate and combined impacts on water balance components of both climate and LULC change. For calibration, validation and uncertainty analysis, the Soil and Water Assessment Tool (SWAT) was used in conjunction with the IPEAT (Integrated Parameter Estimation and Uncertainty Analysis Tool) package. To produce high resolution future climate data from CanESM2 GCM that could be used for impact assessment, the Statistical DownScaling Model (SDSM) was used while the future LULC prediction was generated using Cellular Automata-Markov Chain model. The hydrological response of the basin was assessed by dividing the future time periods in to 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100) through incorporating three scenarios, such as LULC change alone, climate change alone and combined climate and LULC change. The prediction of the LULC change using the CA-Markov chain model indicates that cropland, tree cover, and built-up areas are likely to increase by 2020s, 2050s, and 2080s at the expense of grassland and shrub cover areas, leading to an increase in evapotranspiration, baseflow and streamflow conditions in the basin. By considering basin average, the climate prediction result suggests an increase in both Tmax (up to 2.14 °C) and Tmin (up to 3.2 °C) temperatures, whereas precipitation would increase by up to 25% in the basin. The results show an increase of evapotranspiration by up to 0.84%, 59.8% and 55.5% under LULC, climate and combined climate and LULC change by the end of the 21st century under RCP8.5 compared to the baseline period, respectively. Furthermore, both stream-flow and lateral flow are projected to increase by up to 12.85% (9.9%), 28.5% (20.03%) and 26.4% (29.12%) under LULC, climate and combined climate and LULC change scenarios, respectively. As predicted, the shift in magnitude in RCP8.5 emissions is greater than RCP2.6 and RCP4.5. The impacts of climate change on water balances are relatively higher than the combined effects of changes in climate and LULC. Future LULC shifts, on the other hand, change comparatively offsetting hydrological components. In order to devise local-scale adaptation and mitigation strategies, the inclusion of predicted climate and LULC change for hydrological impact studies, is therefore, very useful.

Ground Maneuvering Targets Imaging for Synthetic Aperture Radar Based on Second-Order Keystone Transform and High-Order Motion Parameter Estimation

With the developments of the synthetic aperture radar (SAR) system, the long dwell time provides high resolution but at the same time it produces large range migration (RM) and Doppler frequency migration, which brings a new challenge for ground moving target imaging. To address those problems, a ground moving target refocusing method based on a novel parameter estimation algorithm, namely second-order Keystone transform (SOKT)-Radon-cubic phase function-Fourier transform (RCFT), is proposed in this work. First, the SOKT is applied to correct the range curvature. After that, the Hough transform is utilized to estimate the trajectory slope for the range walk correction and the across-track velocity acquirement. Finally, a novel coherently integrated parameter estimation method, termed as RCFT, is developed in this work to transform the azimuth quadratic frequency-modulated signal into a peak point in two-dimensional (2-D) time-frequency plane. In the process, the second- and third-order motion parameters are simultaneously obtained, which eliminates the error propagation effect. Due to 2-D coherent integration realization and its bilinear function feature, the proposed RCFT method is capable of obtaining accurate parameter estimates in low signal-to-noise ratio scenario with a relatively low computational burden. Compared with the conventional SAR imaging methods using the second-order phase model, the proposed method produces better imaging quality since the third-order Doppler frequency migration is effectively eliminated. The performance of the proposed method is demonstrated by both the simulated and real data.

IPEAT+: A Built-In Optimization and Automatic Calibration Tool of SWAT+

For almost 30 years, the Soil and Water Assessment Tool (SWAT) has been successfully implemented to address issues around various scientific subjects in the world. On the other hand, it has been reaching to the limit of potential flexibility in further development by the current structure. The new generation SWAT, dubbed SWAT+, was released recently with entirely new coding features. SWAT+ is designed to have far more advanced functions and capacities to handle challenging watershed modeling tasks for hydrologic and water quality processes. However, it is still inevitable to conduct model calibration before the SWAT+ model is applied to engineering projects and research programs. The primary goal of this study is to develop an open-source, easy-to-operate automatic calibration tool for SWAT+, dubbed IPEAT+ (Integrated Parameter Estimation and Uncertainty Analysis Tool Plus). There are four major advantages: (i) Open-source code to general users; (ii) compiled and integrated directly with SWAT+ source code as a single executable; (iii) supported by the SWAT developer group; and, (iv) built with efficient optimization technique. The coupling work between IPEAT+ and SWAT+ is fairly simple, which can be conducted by users with minor efforts. IPEAT+ will be regularly updated with the latest SWAT+ revision. If users would like to integrate IPEAT+ with various versions of SWAT+, only few lines in the SWAT+ source code are required to be updated. IPEAT+ is the first automatic calibration tool integrated with SWAT+ source code. Users can take advantage of the tool to pursue more cutting-edge and forward-thinking scientific questions.

Distributed optimal consensus control for nonlinear multiagent systems based on DISOPE algorithm

SummaryIn this note, a distributed dynamic integrated system optimization and parameter estimation (DISOPE) algorithm for nonlinear multiagent system with general local finite time horizon performance index is proposed, which integrates the model optimization and parameter estimation techniques. Two difficult issues for optimal consensus problem, nonlinear multiagent system and nonlinear quadratic performance index, are considered. With the help of DISOPE algorithm, a distributed optimal control policy is designed to minimize the local performance index in finite time horizon. Furthermore, the convergence of distributed DISOPE algorithm is given, such that the convergence solution satisfies the optimality conditions. Finally, a simulation is employed to show the effectiveness of the distributed DISOPE algorithm.

Assessment of Optional Sediment Transport Functions via the Complex Watershed Simulation Model SWAT

The Soil and Water Assessment Tool 2012 (SWAT2012) offers four sediment routing methods as optional alternatives to the default simplified Bagnold method. Previous studies compared only one of these alternative sediment routing methods with the default method. The proposed study evaluated the impacts of all four alternative sediment transport methods on sediment predictions: the modified Bagnold equation, the Kodoatie equation, the Molinas and Wu equation, and the Yang equation. The Arroyo Colorado Watershed, Texas, USA, was first calibrated for daily flow. The sediment parameters were then calibrated to monthly sediment loads, using each of the four sediment routing equations. An automatic calibration tool—Integrated Parameter Estimation and Uncertainty Analysis Tool (IPEAT)—was used to fit model parameters. The four sediment routing equations yielded substantially different sediment sources and sinks. The Yang equation performed best, followed by Kodoatie, Bagnold, and Molinas and Wu equations, according to greater model goodness-of-fit (represented by higher Nash–Sutcliffe Efficiency coefficient and percent bias closer to 0) as well as lower model uncertainty (represented by inclusion of observed data within 95% confidence interval). Since the default method (Bagnold) does not guarantee the best results, modelers should carefully evaluate the selection of alternative methods before conducting relevant studies or engineering projects.

A joint particle filter and expectation maximization approach to machine condition prognosis

This paper presents a probabilistic model based approach for machinery condition prognosis based on particle filter by integrating physical knowledge with in-process measurements into a state space framework to account for uncertainty and nonlinearity in machinery degradation process. One limitation of conventional particle filter is that condition prognosis is performed based on the model with predetermined parameters obtained from simulation studies or lab-controlled tests. Due to the stochastic nature of machinery defect propagation under varying operating conditions, model parameters may vary in practice which causes prediction errors. To address it, an integrated state prediction and parameter estimation framework based on particle filter and expectation-maximization algorithm is formulated and investigated. The model parameters are adaptively estimated based on expectation-maximization algorithm utilizing hidden degradation state and available in-process measurements. Particle filter is then performed on the identified model with estimated parameters following Bayesian inference scheme to improve the robustness and accuracy of machinery condition prognosis. The effectiveness of the developed method is demonstrated through a simulation study and an experimental run-to-failure bearing test in a wind turbine.

Assimilation of stream discharge for flood forecasting: Updating a semidistributed model with an integrated data assimilation scheme

AbstractReal‐time discharge observations can be assimilated into flood models to improve forecast accuracy; however, the presence of time lags in the routing process and a lack of methods to quantitatively represent different sources of uncertainties challenge the implementation of data assimilation techniques for operational flood forecasting. To address these issues, an integrated error parameter estimation and lag‐aware data assimilation (IEELA) scheme was recently developed for a lumped model. The scheme combines an ensemble‐based maximum a posteriori (MAP) error estimation approach with a lag‐aware ensemble Kalman smoother (EnKS). In this study, the IEELA scheme is extended to a semidistributed model to provide for more general application in flood forecasting by including spatial and temporal correlations in model uncertainties between subcatchments. The result reveals that using a semidistributed model leads to more accurate forecasts than a lumped model in an open‐loop scenario. The IEELA scheme improves the forecast accuracy significantly in both lumped and semidistributed models, and the superiority of the semidistributed model remains in the data assimilation scenario. However, the improvements resulting from IEELA are confined to the outlet of the catchment where the discharge observations are assimilated. Forecasts at “ungauged” internal locations are not improved, and in some instances, even become less accurate.

Assessment of Model Configuration Effect by Alternative Evapotranspiration, Runoff, and Water Routing Functions on Watershed Modeling Using SWAT

Abstract. The choice of different model structures and the subsequent parameter identification are relevant and usually have a strong subjective component. The impacts of various decisions associated with the internal configuration of a given watershed model need greater exploration to ensure confidence in hydrologic and water quality modeling results, yet they are often overlooked. In this study, a non-subjective approach to selecting alternative methods is developed. The goal is to assess the impact of different configurations of the Soil and Water Assessment Tool (SWAT) model on consequential hydrologic and water quality behavior. A total of 12 SWAT configurations, each containing a unique combination of alternative algorithms in estimating surface runoff, potential evapotranspiration, and water routing, were calibrated against flow and nutrient data using the Dynamically Dimensioned Search (DDS) optimization algorithm within the Integrated Parameter Estimation and Uncertainty Analysis Tool (IPEAT). The assessment ensures that non-subjective decisions are formed by fully exploring different model configurations and consequences in the optimization process. The accuracy of calibrated outputs was sensitive to the choice of alternative methods, especially for ammonia-N predictions: NSE values varied from 0.23 to 0.67 for streamflow, from 0.59 to 0.82 for sediment, and from -0.15 to 0.66 for ammonia-N. Similarly, uncertainty in the calibrated outputs varied among alternative methods: inclusion rate varied from 31% to 58% for streamflow, from 46% to 71% sediment, and from 25% to 87% for ammonia-N. The results highlight the significance of non-subjectivity in selecting alternative methods in the calibration of complex watershed models. The potential impact of selecting alternative methods should be fully explored in advance before further applications of complex watershed simulation models.

A framework for propagation of uncertainty contributed by parameterization, input data, model structure, and calibration/validation data in watershed modeling

Failure to consider major sources of uncertainty may bias model predictions in simulating watershed behavior. A framework entitled the Integrated Parameter Estimation and Uncertainty Analysis Tool (IPEAT), was developed utilizing Bayesian inferences, an input error model and modified goodness-of-fit statistics to incorporate uncertainty in parameter, model structure, input data, and calibration/validation data in watershed modeling. Applications of the framework at the Eagle Creek Watershed in Indiana shows that watershed behavior was more realistically represented when the four uncertainty sources were considered jointly without having to embed watershed behavior constraints in auto-calibration. Accounting for the major sources of uncertainty associated with watershed modeling produces more realistic predictions, improves the quality of calibrated solutions, and consequently reduces predictive uncertainty. IPEAT is an innovative tool to investigate and explore the significance of uncertainty sources, which enhances watershed modeling by improved characterization and assessment of predictive uncertainty.

An iterative algorithm based on model-reality differences for discrete-time nonlinear stochastic optimal control problems

An iterative algorithm, which is called the integrated optimal control and parameter estimation algorithm, is developed for solving a discrete time nonlinear stochastic control problem. It is based on the integration of the principle of model-reality differences and Kalman filtering theory, where the dynamic integrated system optimization and parameter estimation algorithm are used interactively. In this approach, the weighted least-square output residual is included in the cost function by appropriately monitoring the weighted matrix. An improved linear quadratic Gaussian optimal control model, rather than the original optimal control problem, is solved. Subsequently, the model optimum is updated using the adjusted parameters induced by the differences between the real plant and the model used. These updated solutions converge to the true optimum, despite model-reality differences. For illustration, the optimal control of a nonlinear continuous stirred tank reactor problem is considered and solved by using the method proposed.

Integrated parameter estimation of multi-component thermal systems with demonstration on a combined heat and power system

In experimental models of multi-component thermal systems, small errors in each submodel can propagate detrimentally through the overall model, resulting in large prediction errors as the prediction time increases. These errors can be problematic when using open-loop or feed-forward control schemes. This paper demonstrates the advantages of a whole-system or integrated parameter estimation approach as opposed to the component-by-component parameter estimation approach that is widespread in the literature. The approach is demonstrated on a combined heat and power system at a laboratory facility, and the resulting model is used to predict the system temperatures up to 20 min in advance. Results show that, when compared to conventional component-by-component parameter estimation, the integrated parameter estimation approach improves the model prediction accuracy significantly.

DISOPE-based networked MPC and performance analysis for plant-wide processes

A networked MPC scheme based on dynamic integrated system optimisation and parameter estimation (DISOPE) is proposed for online optimisation and control of plant-wide processes. Under network environment, online optimisation of the whole system is decomposed into that of several small-scale subsystems, and the connectivity of communication network is assumed to be sufficient for each subsystem to exchange information with other subsystems. An iterative algorithm for DISOPE-based networked MPC is developed in spite of the model-reality differences by iterating on modified linear optimal control problems on the basis of estimating parameters at each iteration. It can be proved that the converged solution of the iterative scheme satisfies the optimality conditions of the original non-linear optimisation problem under some assumptions via computational convergence analysis. The simulation results of the fossil fuel power unit (FFPU) are illustrated to verify the effectiveness and practicability of the presented algorithm.

An integrated optimal control algorithm for discrete-time nonlinear stochastic system

Consider a discrete-time nonlinear system with random disturbances appearing in the real plant and the output channel where the randomly perturbed output is measurable. An iterative procedure based on the linear quadratic Gaussian optimal control model is developed for solving the optimal control of this stochastic system. The optimal state estimate provided by Kalman filtering theory and the optimal control law obtained from the linear quadratic regulator problem are then integrated into the dynamic integrated system optimisation and parameter estimation algorithm. The iterative solutions of the optimal control problem for the model obtained converge to the solution of the original optimal control problem of the discrete-time nonlinear system, despite model-reality differences, when the convergence is achieved. An illustrative example is solved using the method proposed. The results obtained show the effectiveness of the algorithm proposed.

DISOPE distributed model predictive control of cascade systems with network communication

A novel distributed model predictive control scheme based on dynamic integrated system optimization and parameter estimation (DISOPE) was proposed for nonlinear cascade systems under network environment. Under the distributed control structure, online optimization of the cascade system was composed of several cascaded agents that can cooperate and exchange information via network communication. By iterating on modified distributed linear optimal control problems on the basis of estimating parameters at every iteration the correct optimal control action of the nonlinear model predictive control problem of the cascade system could be obtained, assuming that the algorithm was convergent. This approach avoids solving the complex nonlinear optimization problem and significantly reduces the computational burden. The simulation results of the fossil fuel power unit are illustrated to verify the effectiveness and practicability of the proposed algorithm.

Comparison of methods for estimating real process derivatives in on-line optimization

The problem of derivative information estimation for the on-line optimization control problem has been widely recognized. This paper investigates methods and techniques for the estimation of these derivatives, to be used within the well-known integrated system optimization and parameter estimation algorithm. Methods of finite difference approximation, dual control optimization, Broydon’s method and a dynamic identification method, with a linear and non-linear models, are discussed and applied, under simulation, to a cascade continuous stirred tank reactor system.

Effective Strategy for Automated Characterization in Complex Viscoelastoplastic and Damage Modeling for Isotropic/Anisotropic Aerospace Materials

The development of an overall strategy to estimate the material parameters for a class of recent viscoplastic and damage-material models is presented. The procedure is automated through an integrated parameter estimation scheme to enable the determination of an “optimum” set of material parameters by minimizing the errors between the experimental test data and the predicted response. The core ingredients are (1) the numerical experiment simulation analysis, which utilizes a finite-element-based solution scheme; (2) sensitivity analysis utilizing a direct-differentiation approach for the material response sensitivities; and (3) a gradient-based optimization technique of an error/cost function. This scheme is then applied to a variety of materials: (1) an anisotropic polymer matrix composite (PMC); (2) a 9Cr-1Mo material at 550°C; (3) a nickel-based Waspaloy at 700°C; and (4) a Cu-4 Cr-2 copper-based material at 200°C and 400°C. These first two materials were characterized for their deformation behavior, and the last two materials are characterized for both deformation and damage behavior. A rather comprehensive set of test conditions (e.g., uniaxial, multiaxial, and cyclic/ratcheting) is considered to demonstrate the scope and effectiveness of the approach.