scholarly journals Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules – the caRamel algorithm

2020 ◽  
Vol 24 (6) ◽  
pp. 3189-3209
Author(s):  
Céline Monteil ◽  
Fabrice Zaoui ◽  
Nicolas Le Moine ◽  
Frédéric Hendrickx
Keyword(s):  
R Package ◽  
Calibration Procedure ◽  
Automatic Calibration ◽  
Hydrological Models ◽  
Environmental Modelling ◽  
Nsga Ii ◽  
Multi Objective ◽  
Constrained Problem ◽  
Stochastic Rules

Abstract. Environmental modelling is complex, and models often require the calibration of several parameters that are not able to be directly evaluated from a physical quantity or field measurement. Multi-objective calibration has many advantages such as adding constraints in a poorly constrained problem or finding a compromise between different objectives by defining a set of optimal parameters. The caRamel optimizer has been developed to meet the requirement for an automatic calibration procedure that delivers not just one but a family of parameter sets that are optimal with regard to a multi-objective target. The idea behind caRamel is to rely on stochastic rules while also allowing more “local” mechanisms, such as the extrapolation along vectors in the parameter space. The caRamel algorithm is a hybrid of the multi-objective evolutionary annealing simplex (MEAS) method and the non-dominated sorting genetic algorithm II (ε-NSGA-II). It was initially developed for calibrating hydrological models but can be used for any environmental model. The caRamel algorithm is well adapted to complex modelling. The comparison with other optimizers in hydrological case studies (i.e. NSGA-II and MEAS) confirms the quality of the algorithm. An R package, caRamel, has been designed to easily implement this multi-objective algorithm optimizer in the R environment.

scholarly journals Technical note: the caRamel R package for Automatic Calibration by Evolutionary Multi Objective Algorithm

2019 ◽  
Author(s):  
Céline Monteil ◽  
Fabrice Zaoui ◽  
Nicolas Le Moine ◽  
Frédéric Hendrickx
Keyword(s):  
Stable Solution ◽  
R Package ◽  
Technical Note ◽  
Main Function ◽  
Nsga Ii ◽  
Multiple Criteria Optimization ◽  
Multi Objective ◽  
Study Case ◽  
Nondominated Sorting

Abstract. Environmental modelling is complex, and models often require the calibration of several parameters that are not directly evaluable from a physical quantity or a field measurement. The R package caRamel has been designed to easily implement a multi-objective optimizer in the R environment to calibrate these parameters. A multiobjective calibration allows to find a compromise between different goals by defining a set of optimal parameters. The algorithm is a hybrid of the Multiobjective Evolutionary Annealing Simplex method (MEAS) and the Nondominated Sorting Genetic Algorithm II (ε-NSGA-II algorithm). The optimizer was initially developed for the calibration of hydrological models but can be used for any environmental model. The main function of the package, caRamel(), requires to define a multi-objective calibration function as well as bounds on the variation of the underlying parameters to optimize. CaRamel is well adapted to complex modelling. As an example, caRamel converges quickly and has a stable solution after 5,000 model evaluations with robust results for a real study case of a hydrological problem with 8 parameters and 3 objectives of calibration. The comparison with another well-known optimizer (i.e. MCO, for Multiple Criteria Optimization) confirms the quality of the algorithm.

Applying Evolutionary Many-Objective Optimization Algorithms to the Quality-Driven Web Service Composition Problem

2016 ◽  
pp. 170-194
Author(s):  
Arion de Campos Jr. ◽  
Aurora T. R. Pozo ◽  
Silvia R. Vergilio
Keyword(s):  
Web Services ◽  
Web Service ◽  
Service Composition ◽  
Optimization Algorithms ◽  
Web Service Composition ◽  
Nsga Ii ◽  
Preference Relations ◽  
Multi Objective ◽  
Composition Problem

The Web service composition refers to the aggregation of Web services to meet customers' needs in the construction of complex applications. The selection among a large number of Web services that provide the desired functionalities for the composition is generally driven by QoS (Quality of Service) attributes, and formulated as a constrained multi-objective optimization problem. However, many equally important QoS attributes exist and in this situation the performance of the multi-objective algorithms can be degraded. To deal properly with this problem we investigate in this chapter a solution based in many-objective optimization algorithms. We conduct an empirical analysis to measure the performance of the proposed solution with the following preference relations: Controlling the Dominance Area of Solutions, Maximum Ranking and Average Ranking. These preference relations are implemented with NSGA-II using five objectives. A set of performance measures is used to investigate how these techniques affect convergence and diversity of the search in the WSC context.

Multi-objective automatic calibration of SWAT using NSGA-II

2007 ◽  
Vol 341 (3-4) ◽  
pp. 165-176 ◽  
Author(s):  
Elias G. Bekele ◽  
John W. Nicklow
Keyword(s):  
Automatic Calibration ◽  
Nsga Ii ◽  
Multi Objective

scholarly journals Multi-objective automatic calibration of hydrodynamic models utilizing inundation maps and gauge data

2010 ◽  
Vol 7 (6) ◽  
pp. 9173-9218 ◽  
Author(s):  
N. V. Dung ◽  
B. Merz ◽  
A. Bárdossy ◽  
T. D. Thang ◽  
H. Apel
Keyword(s):  
Spatial Resolution ◽  
Large Scale ◽  
Mekong Delta ◽  
Calibration Procedure ◽  
Automatic Calibration ◽  
Hydrodynamic Models ◽  
Calibration Process ◽  
Multi Objective ◽  
Inundation Maps ◽  
In Situ Data

Abstract. Calibration of hydrodynamic models is – compared to other disciplines like e.g. hydrology – still underdeveloped. This has mainly two reasons: the lack of appropriate data and the large computational demand in terms of CPU-time. Both aspects are aggravated in large-scale applications. However, there are recent developments that improve the situation on both the data and computing side. Remote sensing, especially radar-based techniques proved to provide highly valuable information on flood extents, and in case high precision DEMs are present, also on spatially distributed inundation depths. On the computing side the use of parallelization techniques brought significant performance gains. In the presented study we build on these developments by calibrating a large-scale 1-D hydrodynamic model of the whole Mekong Delta downstream of Kratie in Cambodia: we combined in-situ data from a network of river gauging stations, i.e. data with high temporal but low spatial resolution, with a series of inundation maps derived from ENVISAT Advanced Synthetic Aperture Radar (ASAR) satellite images, i.e. data with low temporal but high spatial resolution, in an multi-objective automatic calibration process. It is shown that an automatic, multi-objective calibration of hydrodynamic models, even of such complexity and on a large scale and complex as a model for the Mekong Delta, is possible. Furthermore, the calibration process revealed model deficiencies in the model structure, i.e. the representation of the dike system in Vietnam, which would have been difficult to detect by a standard manual calibration procedure.

The Multi-Objective Optimization Design of a Forklift Steering Mechanism

2014 ◽  
Vol 1049-1050 ◽  
pp. 884-887
Author(s):  
Qin Man Fan ◽  
Yong Hai Wu
Keyword(s):  
Hydraulic System ◽  
Optimization Design ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Oil Pump ◽  
System Pressure ◽  
Steering Mechanism ◽  
Steering Torque

The design and quality of steering mechanism is directly related to forklift traction, mobility, steering stability and safe operation. A multi-objective optimization model of the forklift steering mechanism is established in this paper. The objective function is minimum oil cylinder stroke difference and the minimum power oil pump. Steering torque, geometrical angles, geometry size and the hydraulic system pressure are used as constraint conditions. We use non dominated sorting genetic algorithm (NSGA II) based on the Pareto optimal concept to optimize and calculate model and get the optimal design of steering mechanism.

scholarly journals Latest developments of the airGR rainfall-runoff modelling R-package: inclusion of an interception store in the hourly model

2020 ◽  
Author(s):  
Guillaume Thirel ◽  
Olivier Delaigue ◽  
Andrea Ficchi
Keyword(s):  
R Package ◽  
Snow Accumulation ◽  
Recent Model ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Environmental Modelling ◽  
Model Improvement ◽  
Improved Model ◽  
The Impact ◽  
Daily Time

<p>airGR (Coron et al., 2017, 2019) is an R package that offers the possibility to use the GR rainfall-runoff models developed in the Hydrology Research Group at INRAE (formerly at Irstea), including the daily GR4J model as well as hourly, monthly and annual models. Recent model developments are regularly introduced in airGR.</p><p>Recently, an hourly model including an interception store was implemented in airGR. The additional interception store, developed by Ficchi et al. (2019), aims at better representing the impact of vegetation on evaporation fluxes. This improved model showed a better consistency of model fluxes across time and enhanced performance.</p><p>In addition, the possibility to run the hourly GR models together with the CemaNeige snow accumulation and melt module was added to airGR.</p><p> </p><p>References:</p><p>Coron L., Thirel G., Delaigue O., Perrin C., Andréassian V. (2017). The Suite of Lumped GR Hydrological Models in an R package, Environmental Modelling & Software, 94, 166-171. DOI: 10.1016/j.envsoft.2017.05.002.</p><p>Coron, L., Delaigue, O., Thirel, G., Perrin, C. and Michel, C. (2019). airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling. R   package version 1.4.3.30. URL: https://CRAN.R-project.org/package=airGR.</p><p>Ficchì, A., Perrin, C., and Andréassian, V., 2019. Hydrological modelling at multiple sub-daily time steps: model improvement via flux-matching, Journal of Hydrology, 575, 1308-1327, https://doi.org/10.1016/j.jhydrol.2019.05.084.</p>

scholarly journals Searching for an optimized single-objective function matching multiple objectives with automatic calibration of hydrological models

2016 ◽  
Author(s):  
Fuqiang Tian ◽  
Yu Sun ◽  
Hongchang Hu ◽  
Hongyi Li
Keyword(s):  
Objective Function ◽  
Power Function ◽  
Automatic Calibration ◽  
Efficiency Coefficient ◽  
Hydrological Models ◽  
Objective Functions ◽  
Multi Objective ◽  
Hydrological Variable ◽  
Flow Part ◽  
Single Objective

Abstract. In the calibration of hydrological models, evaluation criteria are explicitly and quantitatively defined as single- or multi-objective functions when utilizing automatic calibration approaches. In most previous studies, there is a general opinion that no single-objective function can represent all of the important characteristics of even one specific kind of hydrological variable (e.g., streamflow). Thus hydrologists must turn to multi-objective calibration. In this study, we demonstrated that an optimized single-objective function can compromise multi-response modes (i.e., multi-objective functions) of the hydrograph, which is defined as summation of a power function of the absolute error between observed and simulated streamflow with the exponent of power function optimized for specific watersheds. The new objective function was applied to 196 model parameter estimation experiment (MOPEX) watersheds across the eastern United States using the semi-distributed Xinanjiang hydrological model. The optimized exponent value for each watershed was obtained by targeting four popular objective functions focusing on peak flows, low flows, water balance, and flashiness, respectively. The results showed that the optimized single-objective function can achieve a better hydrograph simulation compared to the traditional single-objective function Nash-Sutcliffe efficiency coefficient for most watersheds, and balance high flow part and low flow part of the hydrograph without substantial differences compared to multi-objective calibration. The proposed optimal single-objective function can be practically adopted in the hydrological modeling if the optimal exponent value could be determined a priori according to hydrological/climatic/landscape characteristics in a specific watershed. This is, however, left for future study.

scholarly journals Multi-objective automatic calibration of hydrodynamic models utilizing inundation maps and gauge data

2011 ◽  
Vol 15 (4) ◽  
pp. 1339-1354 ◽  
Author(s):  
N. V. Dung ◽  
B. Merz ◽  
A. Bárdossy ◽  
T. D. Thang ◽  
H. Apel
Keyword(s):  
Spatial Resolution ◽  
Large Scale ◽  
Mekong Delta ◽  
Calibration Procedure ◽  
Automatic Calibration ◽  
Hydrodynamic Models ◽  
Calibration Process ◽  
Multi Objective ◽  
Inundation Maps ◽  
In Situ Data

Abstract. Automatic and multi-objective calibration of hydrodynamic models is – compared to other disciplines like e.g. hydrology – still underdeveloped. This has mainly two reasons: the lack of appropriate data and the large computational demand in terms of CPU-time. Both aspects are aggravated in large-scale applications. However, there are recent developments that improve the situation on both the data and computing side. Remote sensing, especially radar-based techniques proved to provide highly valuable information on flood extents, and in case high precision DEMs are present, also on spatially distributed inundation depths. On the computing side the use of parallelization techniques brought significant performance gains. In the presented study we build on these developments by calibrating a large-scale 1-dimensional hydrodynamic model of the whole Mekong Delta downstream of Kratie in Cambodia: we combined in-situ data from a network of river gauging stations, i.e. data with high temporal but low spatial resolution, with a series of inundation maps derived from ENVISAT Advanced Synthetic Aperture Radar (ASAR) satellite images, i.e. data with low temporal but high spatial resolution, in an multi-objective automatic calibration process. It is shown that an automatic, multi-objective calibration of hydrodynamic models, even of such complexity and on a large scale and complex as a model for the Mekong Delta, is possible. Furthermore, the calibration process revealed model deficiencies in the model structure, i.e. the representation of the dike system in Vietnam, which would have been difficult to detect by a standard manual calibration procedure.

scholarly journals Calibrating a watershed simulation model involving human interference: an application of multi-objective genetic algorithms

2008 ◽  
Vol 10 (1) ◽  
pp. 97-111 ◽  
Author(s):  
Mohamad I. Hejazi ◽  
Ximing Cai ◽  
Deva K. Borah
Keyword(s):  
Error Term ◽  
Fitness Function ◽  
Calibration Procedure ◽  
Hydrologic Model ◽  
Storm Event ◽  
Model Parameters ◽  
Nsga Ii ◽  
Human Interference ◽  
Multi Objective ◽  
Reservoir Release

We calibrate a storm-event distributed hydrologic model to a watershed, in which runoff is significantly affected by reservoir storage and release, using a multi-objective genetic algorithm (NSGA-II). This paper addresses the following questions: What forms of the objective (fitness) function used in the optimization model will result in a better calibration? How does the error in reservoir release caused by neglected human interference or the imprecise storage–release function affect the calibration? Reservoir release is studied as a specific (and popular) form of human interference. Two procedures for handling reservoir releases are tested and compared: (1) treating reservoir releases to be solely determined by the hydraulic structure (predefined storage or stage-discharge relations) as if perfect, a procedure usually adopted in watershed model calibration; or (2) adding reservoir releases that are determined by the storage–discharge relation to an error term. The error term encompasses a time-variant human interference and a discharge function error, and is determined through an optimization-based calibration procedure. It is found that the calibration procedure with consideration of human interference not only results in a better match of modeled and observed hydrograph, but also more reasonable model parameters in terms of their spatial distribution and the robustness of the parameter values.

scholarly journals Balance of Cost, Time, and Quality Related to Construction Projects Regarding the Reinforced Concrete of Underground Structures using a Meta-Heuristic Algorithm

2017 ◽  
Vol 63 (4) ◽  
pp. 103-121 ◽  
Author(s):  
S. A. Hosseini ◽  
A. Akbarpour ◽  
H. Ahmadi ◽  
B. Aminnejad
Keyword(s):  
Reinforced Concrete ◽  
Construction Projects ◽  
Minimum Cost ◽  
Business Environment ◽  
Underground Structures ◽  
Nsga Ii ◽  
Multi Objective ◽  
Objective Model ◽  
Best Execution

AbstractUnderground spaces having features such as stability, resistance, and being undetected can play a key role in reducing vulnerability by relocating infrastructures and manpower. In recent years, the competitive business environment and limited resources have mostly focused on the importance of project management in order to achieve its objectives. In this research, in order to find the best balance among cost, time, and quality related to construction projects using reinforced concrete in underground structures, a multi-objective mathematical model is proposed. Several executive approaches have been considered for project activities and these approaches are analyzed via several factors. It is assumed that cost, time, and quality of activities in every defined approach can vary between compact and normal values, and the goal is to find the best execution for activities, achieving minimum cost and the maximum quality for the project. To solve the proposed multi-objective model, the genetic algorithm NSGA-II is used.

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