scholarly journals Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework

2019 ◽  
Vol 23 (8) ◽  
pp. 3405-3421 ◽  
Author(s):  
Zhengke Pan ◽  
Pan Liu ◽  
Shida Gao ◽  
Jun Xia ◽  
Jie Chen ◽  
...  
Keyword(s):  
Spatial Coherence ◽  
Model Performance ◽  
Climatic Conditions ◽  
Bayesian Regression ◽  
Model Parameters ◽  
Time Varying ◽  
Hierarchical Bayesian ◽  
Hydrological Models ◽  
Varying Parameters ◽  
Time Varying Parameters

Abstract. Understanding the projection performance of hydrological models under contrasting climatic conditions supports robust decision making, which highlights the need to adopt time-varying parameters in hydrological modeling to reduce performance degradation. Many existing studies model the time-varying parameters as functions of physically based covariates; however, a major challenge remains in finding effective information to control the large uncertainties that are linked to the additional parameters within the functions. This paper formulated the time-varying parameters for a lumped hydrological model as explicit functions of temporal covariates and used a hierarchical Bayesian (HB) framework to incorporate the spatial coherence of adjacent catchments to improve the robustness of the projection performance. Four modeling scenarios with different spatial coherence schemes and one scenario with a stationary scheme for model parameters were used to explore the transferability of hydrological models under contrasting climatic conditions. Three spatially adjacent catchments in southeast Australia were selected as case studies to examine the validity of the proposed method. Results showed that (1) the time-varying function improved the model performance but also amplified the projection uncertainty compared with the stationary setting of model parameters, (2) the proposed HB method successfully reduced the projection uncertainty and improved the robustness of model performance, and (3) model parameters calibrated over dry years were not suitable for predicting runoff over wet years because of a large degradation in projection performance. This study improves our understanding of the spatial coherence of time-varying parameters, which will help improve the projection performance under differing climatic conditions.

scholarly journals Improving hydrological projection performance under contrasting climatic conditions using spatial coherence through a hierarchical Bayesian regression framework

2019 ◽  
Author(s):  
Zhengke Pan ◽  
Pan Liu ◽  
Shida Gao ◽  
Jun Xia ◽  
Jie Chen ◽  
...  
Keyword(s):  
Spatial Coherence ◽  
Model Performance ◽  
Climatic Conditions ◽  
Bayesian Regression ◽  
Model Parameters ◽  
Time Varying ◽  
Hierarchical Bayesian ◽  
Hydrological Models ◽  
Varying Parameters ◽  
Time Varying Parameters

Abstract. Understanding the projection performance of hydrological models under contrasting climatic conditions supports robust decision making, which highlights the need to adopt time-varying parameters in hydrological modeling to reduce the performance degradation. Many existing literatures model the time-varying parameters as functions of physically-based covariates; however, a major challenge remains finding effective information to control the large uncertainties that are linked to the additional parameters within the functions. This paper formulated the time-varying parameters for a lumped hydrological model as explicit functions of temporal covariates and used a hierarchical Bayesian (HB) framework to incorporate the spatial coherence of adjacent catchments to improve the robustness of the projection performance. Four modeling scenarios with different spatial coherence schemes, and one scenario with a stationary scheme for model parameters, were used to explore the transferability of hydrological models under contrasting climatic conditions. Three spatially adjacent catchments in southeast Australia were selected as case studies to examine validity of the proposed method. Results showed that (1) the time-varying function improved the model performance but also amplified the projection uncertainty compared with stationary setting of model parameters; (2) the proposed HB method successfully reduced the projection uncertainty and improved the robustness of model performance; and (3) model parameters calibrated over dry periods were not suitable for predicting runoff over wet periods because of a large degradation in projection performance. This study improves our understanding of the spatial coherence of time-varying parameters, which will help improve the projection performance under differing climatic conditions.

Using Sliding Mode Control to Adjust Drum Level of a Boiler Unit With Time Varying Parameters

2010 ◽  
Author(s):  
Hamed Moradi ◽  
Firooz Bakhtiari-Nejad ◽  
Majid Saffar-Avval ◽  
Aria Alasty
Keyword(s):  
Water Level ◽  
Transfer Functions ◽  
Sliding Mode ◽  
Operating Conditions ◽  
Model Parameters ◽  
Feed Water ◽  
Time Varying ◽  
Boiler Unit ◽  
Varying Parameters ◽  
Time Varying Parameters

Stable control of water level of drum is of great importance for economic operation of power plant steam generator systems. In this paper, a linear model of the boiler unit with time varying parameters is used for simulation. Two transfer functions between drum water level (output variable) and feed-water and steam mass rates (input variables) are considered. Variation of model parameters may be arisen from disturbances affecting water level of drum, model uncertainties and parameter mismatch due to the variant operating conditions. To achieve a perfect tracking of the desired drum water level, two sliding mode controllers are designed separately. Results show that the designed controllers result in bounded values of control signals, satisfying the actuators constraints.

Panel conditional and multinomial logit with time-varying parameters

2015 ◽  
Vol 19 (3) ◽  
Author(s):  
Myoung-jae Lee
Keyword(s):  
Panel Data ◽  
Dynamic Model ◽  
Time Constant ◽  
Unobserved Heterogeneity ◽  
Multinomial Logit ◽  
Model Parameters ◽  
Time Varying ◽  
Varying Parameters ◽  
Conditional Logit ◽  
Time Varying Parameters

AbstractPanel conditional logit estimators (PCLE) in the literature use mostly time-constant parameters. If the panel periods are volatile or long, however, the model parameters can change much. Hence this paper generalizes PCLE with time-constant parameters to PCLE with time-varying parameters; both static and dynamic PCLE are considered for this. The main finding is that time-varying parameters are fully allowed for static PCLE and the dynamic “pseudo” PCLE of [Bartolucci, F. and V. Nigro. 2010. “A Dynamic Model for Binary Panel Data with Unobserved Heterogeneity Admitting a

scholarly journals Improving Parameter Transferability of GR4J Model under Changing Environments Considering Nonstationarity

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2029
Author(s):  
Ling Zeng ◽  
Lihua Xiong ◽  
Dedi Liu ◽  
Jie Chen ◽  
Jong-Suk Kim
Keyword(s):  
Hydrological Model ◽  
Predictive Ability ◽  
Western China ◽  
Model Parameters ◽  
Time Varying ◽  
Varying Parameters ◽  
Changing Environments ◽  
Time Varying Parameter ◽  
Time Varying Parameters ◽  
Set Up

Hydrological nonstationarity has brought great challenges to the reliable application of conceptual hydrological models with time-invariant parameters. To cope with this, approaches have been proposed to consider time-varying model parameters, which can evolve in accordance with climate and watershed conditions. However, the temporal transferability of the time-varying parameter was rarely investigated. This paper aims to investigate the predictive ability and robustness of a hydrological model with time-varying parameter under changing environments. The conceptual hydrological model GR4J (Génie Rural à 4 paramètres Journalier) with only four parameters was chosen and the sensitive parameters were treated as functions of several external covariates that represent the variation of climate and watershed conditions. The investigation was carried out in Weihe Basin and Tuojiang Basin of Western China in the period from 1981 to 2010. Several sub-periods with different climate and watershed conditions were set up to test the temporal parameter transferability of the original GR4J model and the GR4J model with time-varying parameters. The results showed that the performance of streamflow simulation was improved when applying the time-varying parameters. Furthermore, in a series of split-sample tests, the GR4J model with time-varying parameters outperformed the original GR4J model by improving the model robustness. Further studies focus on more diversified model structures and watersheds conditions are necessary to verify the superiority of applying time-varying parameters.

scholarly journals Exploration on hydrological model calibration by considering the hydro-meteorological variability

2019 ◽  
Vol 51 (1) ◽  
pp. 30-46
Author(s):  
Binru Zhao ◽  
Jingqiao Mao ◽  
Qiang Dai ◽  
Dawei Han ◽  
Huichao Dai ◽  
...  
Keyword(s):  
Model Performance ◽  
Performance Model ◽  
Model Complexity ◽  
Time Varying ◽  
Key Factors ◽  
Cluster Number ◽  
Number Of Clusters ◽  
Varying Parameters ◽  
Hydrological Responses ◽  
Time Varying Parameters

Abstract The hydrological response is changeable for catchments with hydro-meteorological variations, which is neglected by the traditional calibration approach through using time-invariant parameters. This study aims to reproduce the variation of hydrological responses by allowing parameters to vary over clusters with hydro-meteorological similarities. The Fuzzy C-means algorithm is used to partition one-month periods into temperature-based and rainfall-based clusters. One-month periods are also classified based on seasons and random numbers for comparison. This study is carried out in three catchments in the UK, using the IHACRES rainfall-runoff model. Results show when using time-varying parameters to account for the variation of hydrological processes, it is important to identify the key factors that cause the change of hydrological responses, and the selection of the time-varying parameters should correspond to the identified key factors. In the study sites, temperature plays a more important role in controlling the change of hydrological responses than rainfall. It is found that the number of clusters has an effect on model performance, model performances for calibration period become better with the increase of cluster number; however, the increase of model complexity leads to poor predictive capabilities due to overfitting. It is important to select the appropriate number of clusters to achieve a balance between model complexity and model performance.

Modeling of adaptations to physical training by using a recursive least squares algorithm

1997 ◽  
Vol 82 (5) ◽  
pp. 1685-1693 ◽  
Author(s):  
Thierry Busso ◽  
Christian Denis ◽  
Régis Bonnefoy ◽  
André Geyssant ◽  
Jean-René Lacour
Keyword(s):  
Least Squares ◽  
Physical Training ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Time Varying ◽  
Varying Parameters ◽  
Recursive Least Squares Algorithm ◽  
Time Varying Parameters ◽  
Least Squares Algorithm ◽  
Time Invariant

Busso, Thierry, Christian Denis, Régis Bonnefoy, André Geyssant, and Jean-René Lacour. Modeling of adaptations to physical training by using a recursive least squares algorithm. J. Appl. Physiol. 82(5): 1685–1693, 1997.—The present study assesses the usefulness of a systems model with time-varying parameters for describing the responses of physical performance to training. Data for two subjects who undertook a 14-wk training on a cycle ergometer were used to test the proposed model, and the results were compared with a model with time-invariant parameters. Two 4-wk periods of intensive training were separated by a 2-wk period of reduced training and followed by a 4-wk period of reduced training. The systems input ascribed to the training doses was made up of interval exercises and computed in arbitrary units. The systems output was evaluated one to five times per week by using the endurance time at a constant workload. The time-invariant parameters were fitted from actual performances by using the least squares method. The time-varying parameters were fitted by using a recursive least squares algorithm. The coefficients of determination r 2 were 0.875 and 0.879 for the two subjects using the time-varying model, higher than the values of 0.682 and 0.666, respectively, obtained with the time-invariant model. The variations over time in the model parameters resulting from the expected reduction in the residuals appeared generally to account for changes in responses to training. Such a model would be useful for investigating the underlying mechanisms of adaptation and fatigue.

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