scholarly journals Multiobjective sensitivity analysis and optimization of distributed hydrologic model MOBIDIC

2014 ◽  
Vol 18 (10) ◽  
pp. 4101-4112 ◽  
Author(s):  
J. Yang ◽  
F. Castelli ◽  
Y. Chen
Keyword(s):  
Sensitivity Analysis ◽  
Water Balance ◽  
Surface Runoff ◽  
Hydrologic Model ◽  
Optimization Approach ◽  
Objective Functions ◽  
Analysis Techniques ◽  
Distributed Hydrologic Model ◽  
Conflicting Objectives ◽  
Initial Starting

Abstract. Calibration of distributed hydrologic models usually involves how to deal with the large number of distributed parameters and optimization problems with multiple but often conflicting objectives that arise in a natural fashion. This study presents a multiobjective sensitivity and optimization approach to handle these problems for the MOBIDIC (MOdello di Bilancio Idrologico DIstribuito e Continuo) distributed hydrologic model, which combines two sensitivity analysis techniques (the Morris method and the state-dependent parameter (SDP) method) with multiobjective optimization (MOO) approach ε-NSGAII (Non-dominated Sorting Genetic Algorithm-II). This approach was implemented to calibrate MOBIDIC with its application to the Davidson watershed, North Carolina, with three objective functions, i.e., the standardized root mean square error (SRMSE) of logarithmic transformed discharge, the water balance index, and the mean absolute error of the logarithmic transformed flow duration curve, and its results were compared with those of a single objective optimization (SOO) with the traditional Nelder–Mead simplex algorithm used in MOBIDIC by taking the objective function as the Euclidean norm of these three objectives. Results show that (1) the two sensitivity analysis techniques are effective and efficient for determining the sensitive processes and insensitive parameters: surface runoff and evaporation are very sensitive processes to all three objective functions, while groundwater recession and soil hydraulic conductivity are not sensitive and were excluded in the optimization. (2) Both MOO and SOO lead to acceptable simulations; e.g., for MOO, the average Nash–Sutcliffe value is 0.75 in the calibration period and 0.70 in the validation period. (3) Evaporation and surface runoff show similar importance for watershed water balance, while the contribution of baseflow can be ignored. (4) Compared to SOO, which was dependent on the initial starting location, MOO provides more insight into parameter sensitivity and the conflicting characteristics of these objective functions. Multiobjective sensitivity analysis and optimization provide an alternative way for future MOBIDIC modeling.

scholarly journals Multiobjective sensitivity analysis and optimization of a distributed hydrologic model MOBIDIC

2014 ◽  
Vol 11 (3) ◽  
pp. 3505-3539 ◽  
Author(s):  
J. Yang ◽  
F. Castelli ◽  
Y. Chen
Keyword(s):  
Sensitivity Analysis ◽  
Water Balance ◽  
Surface Runoff ◽  
Hydrologic Model ◽  
Objective Functions ◽  
Analysis Techniques ◽  
Distributed Hydrologic Model ◽  
Conflicting Objectives ◽  
State Dependent Parameter Method ◽  
Initial Starting

Abstract. Calibration of distributed hydrologic models usually involves how to deal with the large number of distributed parameters and optimization problems with multiple but often conflicting objectives which arise in a natural fashion. This study presents a multiobjective sensitivity and optimization approach to handle these problems for a distributed hydrologic model MOBIDIC, which combines two sensitivity analysis techniques (Morris method and State Dependent Parameter method) with a multiobjective optimization (MOO) approach ϵ-NSGAII. This approach was implemented to calibrate MOBIDIC with its application to the Davidson watershed, North Carolina with three objective functions, i.e., standardized root mean square error of logarithmic transformed discharge, water balance index, and mean absolute error of logarithmic transformed flow duration curve, and its results were compared with those with a single objective optimization (SOO) with the traditional Nelder–Mead Simplex algorithm used in MOBIDIC by taking the objective function as the Euclidean norm of these three objectives. Results show: (1) the two sensitivity analysis techniques are effective and efficient to determine the sensitive processes and insensitive parameters: surface runoff and evaporation are very sensitive processes to all three objective functions, while groundwater recession and soil hydraulic conductivity are not sensitive and were excluded in the optimization; (2) both MOO and SOO lead to acceptable simulations, e.g., for MOO, average Nash–Sutcliffe is 0.75 in the calibration period and 0.70 in the validation period; (3) evaporation and surface runoff shows similar importance to watershed water balance while the contribution of baseflow can be ignored; (4) compared to SOO which was dependent of initial starting location, MOO provides more insight on parameter sensitivity and conflicting characteristics of these objective functions. Multiobjective sensitivity analysis and optimization provides an alternative way for future MOBIDIC modelling.

Design of Optimal Operational Parameters for Steam-Alternating-Solvent Processes in Heterogeneous Reservoirs - A Multi-Objective Optimization Approach

2021 ◽  
Author(s):  
Israel Mayo-Molina ◽  
Juliana Y. Leung
Keyword(s):  
Sensitivity Analysis ◽  
Computational Time ◽  
Optimization Approach ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Operational Parameters ◽  
Multi Objective ◽  
Bottom Hole ◽  
Decision Variables ◽  
Conflicting Objectives

Abstract The Steam Alternating Solvent (SAS) process has been proposed and studied in recent years as a new auspicious alternative to the conventional thermal (steam-based) bitumen recovery process. The SAS process incorporates steam and solvent (e.g. propane) cycles injected alternatively using the same configuration as the Steam-Assisted Gravity-Drainage (SAGD) process. The SAS process offers many advantages, including lower capital and operational cost, as well as a reduction in water usage and lower Greenhouse Gas (GHG) Emissions. On the other hand, one of the main challenges of this relatively new process is the influence of uncertain reservoir heterogeneity distribution, such as shale barriers, on production behaviour. Many complex physical mechanisms, including heat transfer, fluid flows, and mass transfer, must be coupled. A proper design and selection of the operational parameters must consider several conflicting objectives. This work aims to develop a hybrid multi-objective optimization (MOO) framework for determining a set of Pareto-optimal SAS operational parameters under a variety of heterogeneity scenarios. First, a 2-D homogeneous reservoir model is constructed based on typical Cold lake reservoir properties in Alberta, Canada. The homogeneous model is used to establish a base scenario. Second, different shale barrier configurations with varying proportions, lengths, and locations are incorporated. Third, a detailed sensitivity analysis is performed to determine the most impactful parameters or decision variables. Based on the results of the sensitivity analysis, several objective functions are formulated (e.g., minimizing energy and solvent usage). Fourth, Response Surface Methodology (RSM) is applied to generate a set of proxy models to approximate the non-linear relationship between the decision variables and the objective functions and to reduce the overall computational time. Finally, three Multi-Objective Evolutionary Algorithms (MOEAs) are applied to search and compare the optimal sets of decision parameters. The study showed that the SAS process is sensitive to the shale barrier distribution, and that impact is strongly dependent on the location and length of a specific shale barrier. When a shale barrier is located near the injector well, pressure and temperature may build up in the near-well area, preventing additional steam and solvent be injected and, consequently, reducing the oil production. Operational constraints, such as bottom-hole pressure, steam trap criterion, and bottom-hole gas rate in the producer, are among various critical decision variables examined in this study. A key conclusion is that the optimal operating strategy should depend on the underlying heterogeneity. Although this notion has been alluded to in other previous steam- or solvent-based studies, this paper is the first to utilize a MOO framework for systematically determining a specific optimal strategy for each heterogeneity scenario. With the advancement of continuous downhole fibre-optic monitoring, the outcomes can potentially be integrated into other real-time reservoir characterization and optimization work-flows.

scholarly journals Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model

2011 ◽  
Vol 15 (12) ◽  
pp. 3809-3827 ◽  
Author(s):  
A. Atencia ◽  
L. Mediero ◽  
M. C. Llasat ◽  
L. Garrote
Keyword(s):  
Sensitivity Analysis ◽  
Time Resolution ◽  
Heavy Rainfall ◽  
Flash Flood ◽  
Probabilistic Approach ◽  
Great Influence ◽  
Hydrologic Model ◽  
Radar Rainfall ◽  
Distributed Hydrologic Model ◽  
Correction Technique

Abstract. The performance of a hydrologic model depends on the rainfall input data, both spatially and temporally. As the spatial distribution of rainfall exerts a great influence on both runoff volumes and peak flows, the use of a distributed hydrologic model can improve the results in the case of convective rainfall in a basin where the storm area is smaller than the basin area. The aim of this study was to perform a sensitivity analysis of the rainfall time resolution on the results of a distributed hydrologic model in a flash-flood prone basin. Within such a catchment, floods are produced by heavy rainfall events with a large convective component. A second objective of the current paper is the proposal of a methodology that improves the radar rainfall estimation at a higher spatial and temporal resolution. Composite radar data from a network of three C-band radars with 6-min temporal and 2 × 2 km2 spatial resolution were used to feed the RIBS distributed hydrological model. A modification of the Window Probability Matching Method (gauge-adjustment method) was applied to four cases of heavy rainfall to improve the observed rainfall sub-estimation by computing new Z/R relationships for both convective and stratiform reflectivities. An advection correction technique based on the cross-correlation between two consecutive images was introduced to obtain several time resolutions from 1 min to 30 min. The RIBS hydrologic model was calibrated using a probabilistic approach based on a multiobjective methodology for each time resolution. A sensitivity analysis of rainfall time resolution was conducted to find the resolution that best represents the hydrological basin behaviour.

Robust Optimization of an Accelerometer Considering Fabrication Errors

2009 ◽  
Vol 628-629 ◽  
pp. 353-356 ◽  
Author(s):  
Guang Jun Liu ◽  
Tao Jiang ◽  
An Lin Wang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Sensitivity Analysis ◽  
Robust Optimization ◽  
Optimization Problem ◽  
Design Space ◽  
Optimization Approach ◽  
Objective Functions ◽  
Micro Fabrication ◽  
Fabrication Errors

A robust optimization approach of an accelerometer is presented to minimize the effect of variations from micro fabrication. The sensitivity analysis technology is employed to reduce design space and to find the key parameters that have greatest influence on the accelerometer. And then, the constraint conditions and objective functions for robust optimization and the corresponding mathematical model are presented. The optimization problem is solved by the Multiple-island Genetic Algorithm and the results show that an accelerometer with better performance is obtained.

scholarly journals Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

2018 ◽  
Vol 22 (2) ◽  
pp. 1299-1315 ◽  
Author(s):  
Mehmet C. Demirel ◽  
Juliane Mai ◽  
Gorka Mendiguren ◽  
Julian Koch ◽  
Luis Samaniego ◽  
...  
Keyword(s):  
Water Balance ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Spatial Model ◽  
Hydrologic Model ◽  
Actual Evapotranspiration ◽  
Parameter Distribution ◽  
Objective Functions ◽  
Performance Metric ◽  
Spatial Performance

Abstract. Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimiser. The calibration results reveal a limited trade-off between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow-only calibration were included. Since the overall water balance is usually a crucial goal in hydrologic modelling, spatial-pattern-oriented optimisation should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.

Enhanced identification of a hydrologic model using streamflow and satellite water storage data: A multicriteria sensitivity analysis and optimization approach

2017 ◽  
Vol 31 (19) ◽  
pp. 3320-3333 ◽  
Author(s):  
Fuad Yassin ◽  
Saman Razavi ◽  
Howard Wheater ◽  
Gonzalo Sapriza-Azuri ◽  
Bruce Davison ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Water Storage ◽  
Hydrologic Model ◽  
Optimization Approach
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