Multiobjective Automatic Parameter Calibration of a Hydrological Model

Jung, Donghwi; Choi, Young Hwan; Kim, Joong Hoon

doi:10.3390/w9030187

Cited by 25 publications

(12 citation statements)

References 69 publications

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“…Because all three models are regarded as semi-distributed models that operate on sub-basins with distributed rainfall data, differences in simulated runoff between them is likely derived from model complexity and the rationality of parameterization. The general model precision and uncertainty performance in scenario 20_C is consistent with multiple studies [52,[61][62][63][64].…”

Section: Comparing Different Model Structuressupporting

confidence: 78%

Comparing the Hydrological Responses of Conceptual and Process-Based Models with Varying Rain Gauge Density and Distribution

et al. 2018

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Precipitation provides the most crucial input for hydrological modeling. However, rain gauge networks, the most common precipitation measurement mechanisms, are sometimes sparse and inadequately distributed in practice, resulting in an imperfect representation of rainfall spatial variability. The objective of this study is to analyze the sensitivity of different model structures to the different density and distribution of rain gauges and evaluate their reliability and robustness. Based on a rain gauge network of 20 gauges in the Jinjiang River Basin, south-eastern China, this study compared the performance of two conceptual models (the hydrologic model (HYMOD) and Xinanjiang) and one process-based distributed model (the water and energy transfer between soil, plants and atmosphere model (WetSpa)) with different rain gauge distributions. The results show that the average accuracy for the three models is generally stable as the number of rain gauges decreases but is sensitive to changes in the network distribution. HYMOD has the highest calibration uncertainty, followed by Xinanjiang and WetSpa. Differing model responses are consistent with changes in network distribution, while calibration uncertainties are more related to model structures.

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Section: Comparing Different Model Structuressupporting

confidence: 78%

Comparing the Hydrological Responses of Conceptual and Process-Based Models with Varying Rain Gauge Density and Distribution

et al. 2018

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“…More recently, several variable balancing approaches for the exploration and exploitation of the NSGA-II, in the automatic parameter calibration of a HYdrological MODel (HYMOD) were evaluated in Reference [31]. These balancing approaches were compared with traditional static balancing methods (the two values are fixed during optimization) in a benchmark hydrological calibration problem for the Leaf River (1950 km 2 ) near Collins, Mississippi.…”

Section: Related Workmentioning

confidence: 99%

Qom—A New Hydrologic Prediction Model Enhanced with Multi-Objective Optimization

Zavala¹,

García-Nieto

Nebro

2019

Applied Sciences

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The efficient calibration of hydrologic models allows experts to evaluate past events in river basins, as well as to describe new scenarios and predict possible future floodings. A difficulty in this context is the need to adjust a large number of parameters in the model to reduce prediction errors. In this work, we address this issue with two complementary contributions. First, we propose a new lumped rainfall-runoff hydrologic model-called Qom-which is featured by a limited set of continuous decision variables associated with soil moisture and direct runoff. Qom allows to separate and quantify the volume of losses and excesses of the rainwater falling in a hydrographic basin, while a Clark's model is used to determine output hydrograms. Second, we apply a multi-objective optimization approach to find accurate calibrations of the model in a systematic and automatic way. The idea is to formulate the process as a bi-objective optimization problem where the Nash-Sutcliffe Efficiency coefficient and percent bias have to be minimized, and to combine the results found by a set of metaheuristics used to solve it. For validation purposes, we apply our proposal in six hydrographic scenarios, comprising river basins located in Spain, USA, Brazil and Argentina. The proposed approach is shown to minimize prediction errors of simulated streamflows with regards to those observed in these real-world basins.

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“…The genetic operators for RAP differ from the simulated binary crossover and polynomial mutation [24] for the multiple float variables in traditional genetic algorithms.…”

Section: Multi-objective Genetic Algorithm For Rapmentioning

confidence: 99%

Integer Encoding Genetic Algorithm for Optimizing Redundancy Allocation of Series-parallel Systems

Cheng¹,

An²,

Zhang³

2019

JESTR

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Reliability redundancy allocation is a combinatorial optimization problem, and numerous intelligent evolutionary algorithms (e.g., genetic algorithm and ant colony optimization) have been proposed to solve it. However, various shortcomings, such as problem specificity and high complexity, hinder their applications. An integer encoding genetic algorithm, namely, integer matrix chromosome encoding scheme, was proposed to improve the effectiveness and computational efficiency of redundancy allocation for series-parallel systems and represent the component mixing in subsystems with integers. The related crossover with a binary window and mutation using a matrix with random float numbers was developed to perform combinatorial evolution. The adjusting operator was designed to guarantee the feasibility of chromosomes, combined with the non-dominated sorting genetic algorithm (NSGA-II) in which a constraint Pareto dominance was introduced to handle design constraints without external coefficients. Numerical and engineering examples of an agricultural Internet of Things for greenhouse planting were provided to illustrate the effectiveness of the proposed algorithm. Results show that the proposed novel algorithm can solve a typical model for reliability redundancy allocation, i.e., a non-maintained bi-state series-parallel system with active redundancy and component mixing strategy. The constraint Pareto dominance is introduced on the basis of the traditional NSGA-II to avoid the complexity and instability of penalty function approaches. The constructed three-objective redundancy allocation problem model can measure the trade-off relationship among three objectives, namely, system reliability, cost, and weight. The improved NSGA-II has the best stability when the optimized value for crossover probability is 0.98 and the mutation probability is set to a small value. Advantages of the presented model and method include its convenience and suitability for different genetic evolutionary platforms.

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Multiobjective Automatic Parameter Calibration of a Hydrological Model

Cited by 25 publications

References 69 publications

Comparing the Hydrological Responses of Conceptual and Process-Based Models with Varying Rain Gauge Density and Distribution

Comparing the Hydrological Responses of Conceptual and Process-Based Models with Varying Rain Gauge Density and Distribution

Qom—A New Hydrologic Prediction Model Enhanced with Multi-Objective Optimization

Integer Encoding Genetic Algorithm for Optimizing Redundancy Allocation of Series-parallel Systems

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