Sensitivity analysis and automatic calibration of a rainfall-runoff model using multi-objectives

Authors:
Fan Sun;Yang Liu
Affiliations:
School of Management and Language & School of the Built Environment, Heriot-Watt University, Edinburgh, UK;School of Management and Language & School of the Built Environment, Heriot-Watt University, Edinburgh, UK
Venue:
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Year:
2010

Citing 5
Cited 0

Factorial sampling plans for preliminary computational experiments

Technometrics
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Automatic calibration of a rainfall-runoff model using a fast and elitist multi-objective particle swarm algorithm

Expert Systems with Applications: An International Journal
Solving rotated multi-objective optimization problems using differential evolution

AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

The practical experience with sensitivity analysis suggests that no single-objective function is adequate to measure the ways in which the model fails to match the important characteristics of the observed data. In order to successfully measure parameter sensitivity of a numerical model, multiple criteria should be considered. Sensitivity analysis of a rainfall-runoff model is performed using the local sensitivity method (Morris method) and multiple objective analysis. Formulation of SA strategy for the MIKE/NAM rainfall-runoff model is outline. The SA is given as a set of Pareto ranks from a multi-objective viewpoint. The Nondominated Sorting Differential Evolution (NSDE) was used to calibrate the rainfall-runoff model. The method has been applied for calibration of a test catchment and compared on validation data. The simulations show that the NSDE method possesses the ability to finding the optimal Pareto front.