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 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.

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 An Automatic Calibration Procedure of Driving Behaviour Parameters in the Presence of High Bus Volume

2019 ◽  
Vol 31 (5) ◽  
pp. 491-502 ◽  
Author(s):  
Nima Dadashzadeh ◽  
Murat Ergun ◽  
Sercan Kesten ◽  
Marijan Žura
Keyword(s):  
Calibration Procedure ◽  
Automatic Calibration ◽  
Traffic Data ◽  
Microscopic Traffic Simulation ◽  
Change Models ◽  
Calibration Process ◽  
Car Following ◽  
Driving Behaviour ◽  
Optimization Stage ◽  
Behaviour Models

Most of the microscopic traffic simulation programs used today incorporate car-following and lane-change models to simulate driving behaviour across a given area. The main goal of this study has been to develop an automatic calibration process for the parameters of driving behaviour models using metaheuristic algorithms. Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and a combination of GA and PSO (i.e. hybrid GAPSO and hybrid PSOGA) were used during the optimization stage. In order to verify our proposed methodology, a suitable study area with high bus volume on-ramp from the O-1 Highway in Istanbul has been modelled in VISSIM. Traffic data have been gathered through detectors. The calibration procedure has been coded using MATLAB and implemented via the VISSIM-MATLAB COM interface. Using the proposed methodology, the results of the calibrated model showed that hybrid GAPSO and hybrid PSOGA techniques outperformed the GA-only and PSO-only techniques during the calibration process. Thus, both are recommended for use in the calibration of microsimulation traffic models, rather than GA-only and PSO-only techniques.

scholarly journals Multi-Objective Optimization of Integrated Civilian-Military Scheduling of Medical Supplies for Epidemic Prevention and Control

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 126
Author(s):  
Hai-Feng Ling ◽  
Zheng-Lian Su ◽  
Xun-Lin Jiang ◽  
Yu-Jun Zheng
Keyword(s):  
Prevention And Control ◽  
Large Scale ◽  
Medical Services ◽  
The Novel ◽  
Multi Objective ◽  
Medical Supplies ◽  
Problem Instances ◽  
Novel Coronavirus ◽  
And Control ◽  
Minimum Ratio

In a large-scale epidemic, such as the novel coronavirus pneumonia (COVID-19), there is huge demand for a variety of medical supplies, such as medical masks, ventilators, and sickbeds. Resources from civilian medical services are often not sufficient for fully satisfying all of these demands. Resources from military medical services, which are normally reserved for military use, can be an effective supplement to these demands. In this paper, we formulate a problem of integrated civilian-military scheduling of medical supplies for epidemic prevention and control, the aim of which is to simultaneously maximize the overall satisfaction rate of the medical supplies and minimize the total scheduling cost, while keeping a minimum ratio of medical supplies reservation for military use. We propose a multi-objective water wave optimization (WWO) algorithm in order to efficiently solve this problem. Computational results on a set of problem instances constructed based on real COVID-19 data demonstrate the effectiveness of the proposed method.

scholarly journals Classification of Very-High-Spatial-Resolution Aerial Images Based on Multiscale Features with Limited Semantic Information

2021 ◽  
Vol 13 (3) ◽  
pp. 364
Author(s):  
Han Gao ◽  
Jinhui Guo ◽  
Peng Guo ◽  
Xiuwan Chen
Keyword(s):  
Deep Learning ◽  
Land Cover ◽  
Spatial Resolution ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Training Data ◽  
Aerial Images ◽  
Rural Landscapes ◽  
Feature Representations ◽  
Object Based

Recently, deep learning has become the most innovative trend for a variety of high-spatial-resolution remote sensing imaging applications. However, large-scale land cover classification via traditional convolutional neural networks (CNNs) with sliding windows is computationally expensive and produces coarse results. Additionally, although such supervised learning approaches have performed well, collecting and annotating datasets for every task are extremely laborious, especially for those fully supervised cases where the pixel-level ground-truth labels are dense. In this work, we propose a new object-oriented deep learning framework that leverages residual networks with different depths to learn adjacent feature representations by embedding a multibranch architecture in the deep learning pipeline. The idea is to exploit limited training data at different neighboring scales to make a tradeoff between weak semantics and strong feature representations for operational land cover mapping tasks. We draw from established geographic object-based image analysis (GEOBIA) as an auxiliary module to reduce the computational burden of spatial reasoning and optimize the classification boundaries. We evaluated the proposed approach on two subdecimeter-resolution datasets involving both urban and rural landscapes. It presented better classification accuracy (88.9%) compared to traditional object-based deep learning methods and achieves an excellent inference time (11.3 s/ha).

scholarly journals A novel encoding for separable large-scale multi-objective problems and its application to the optimisation of housing stock improvements

2020 ◽  
Vol 96 ◽  
pp. 106650
Author(s):  
Alexander E.I. Brownlee ◽  
Jonathan A. Wright ◽  
Miaomiao He ◽  
Timothy Lee ◽  
Paul McMenemy
Keyword(s):  
Large Scale ◽  
Housing Stock ◽  
Multi Objective

A multi-objective sparse evolutionary framework for large-scale weapon target assignment based on a reward strategy

2021 ◽  
Vol 40 (5) ◽  
pp. 10043-10061
Author(s):  
Xiaoping Shi ◽  
Shiqi Zou ◽  
Shenmin Song ◽  
Rui Guo
Keyword(s):  
Large Scale ◽  
The Novel ◽  
Genetic Operators ◽  
Multi Objective ◽  
Target Assignment ◽  
Solution Selection ◽  
Battlefield Environment ◽  
Sparse Problems ◽  
Novel Model ◽  
Error Parameter

 The asset-based weapon target assignment (ABWTA) problem is one of the important branches of the weapon target assignment (WTA) problem. Due to the current large-scale battlefield environment, the ABWTA problem is a multi-objective optimization problem (MOP) with strong constraints, large-scale and sparse properties. The novel model of the ABWTA problem with the operation error parameter is established. An evolutionary algorithm for large-scale sparse problems (SparseEA) is introduced as the main framework for solving large-scale sparse ABWTA problem. The proposed framework (SparseEA-ABWTA) mainly addresses the issue that problem-specific initialization method and genetic operators with a reward strategy can generate solutions efficiently considering the sparsity of variables and an improved non-dominated solution selection method is presented to handle the constraints. Under the premise of constructing large-scale cases by the specific case generator, two numerical experiments on four outstanding multi-objective evolutionary algorithms (MOEAs) show Runtime of SparseEA-ABWTA is faster nearly 50% than others under the same convergence and the gap between MOEAs improved by the mechanism of SparseEA-ABWTA and SparseEA-ABWTA is reduced to nearly 20% in the convergence and distribution.

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