Wu CL, Chau KW. The CE theory-based input determination methodology is applied to identifying suitable inputs of flood forecast models for case studies. 2005a). 2.1 DEFINITIONS IN FORECASTING One of the most important issues in forecasting is the Forecasting Lead Time (FLT), which can be defined as the minimum required time to successfully implement the actions aimed for reducing risk or appropriately manage the water Cybern.in Hydrology and Environmental Engineering. Despite its demonstrated advantages the use of this system is still limited: it has been installed on an experimental basis in France, Germany, Czech Republic and Hungary.To deal with the uncertainty in spatio-temporal distribution and prediction of rainfall for extreme events, especially through radar derived data, a promising approach has been to combine stochastic simulation and detailed knowledge of radar error structure (Germann et al. A bootstrap technique is employed in estimating the values of the mean and the standard deviation of ANN parameters, and is used to quantify the predictive uncertainty. J. Hydrol 133: 99-140distribution systems.
In this project, the humid region in South China is taken an example study area, and hydrological observed experiments will be conducted at the subtropics hydrological experimental station of Sun Yat-Sen University. Instead, it is recommended that the number of inputs be determined by a model-based optimization, where the IVS method is only used to rank input usefulness. On the above basis, a hydrologic simulation system based on Multiply Work Hypotheses will be developed to study the applicability of model structure and parameters for different climate and underlying surface conditions, so as to establish a scientific and reasonable watershed hydrological model in the study area. the existing prediction model (Bowden et al. 239.Kohonen T. (1982) Self-organized formation of topologically correct feature maps. The method is demonstrated through the case study of Upper White watershed located in the United States.
A partial mutual information (PMI) method was used to characterize the dependence of a potential model between its input and output variables. 2, PMI also can be described using the CE:Assume that a set of candidates will initially contain some proportion of redundantcandidates and defined as (Fernando et al. no. The CE theory permits to calculate mutual information (MI) and partial mutual information (PMI), which characterizes the dependence between potential model input and output variables directly instead of calculating the marginal and joint probability distributions. (10.7)The performance indexes are used to evaluate the established ANN model, and theone with the best performance is finally selected for the streamflow forecasting. The nearer projected state is weighted heavier than the remotely projected state, a reasonable approx imation in the phase-space. This study uses a simple method for quantifying predictive uncertainty of ANN model output through first order Taylor series expansion. Second, the relation between CE and PMI is discussed.
IAHS Publ. Hydrol Hecht-Nielsen R (1987) Counterpropagation networks. This method formed part of initiatives such as HEPEX (Hydrological Ensemble Prediction EXperiment) which investigated how best to produce, communicate and use hydrologic ensemble forecasts for short, medium and long-term predictions. Water Resour Res 31 (10), 2517–2530.symposium held at Davis.
They introduced the average shifted histograms (ASHs) as an alternativeto kernel-based methods for the estimation of mutual information (MI).calculation work.
We propose a methodology that reduces the data requirements. Besides, the research results will also provide science and technique support for flood control and disaster prevention, safe operation of the south-to-north water transfer project and sustainable development of water resources and social economy in the Hanjiang River basin.comprehensively evaluate the reasonableness and applicability of watershed hydrological model, and effectively reduce uncertainty in hydrological modeling and forecast. This study evaluates four IVS methods based on the following criteria: partial correlation (PC), partial © 2008-2020 ResearchGate GmbH.
Where hydrologically important areas (such as steep slopes) are unrepresented, the model may utilize an interpolation method (introducing another element of uncertainty) in order to estimate run-off volume and peak flows. Improving Flood Forecasting and Early Warning in Somalia Feasibility Study Technical Report NoW-10 June 2007 Somalia Water and Land Information Management Ngecha Road, Lake View. 1997). Based on the theory of Multiply Work Hypotheses, a modular watershed hydrological model based on COM technique and a multi-factor diagnosis method to evaluate hydrological modeling based on Bayesian theory will be established. Parallel Distributed Processing 1, 318-362.hydrologic data. The four models are given as follows. Therefore, One of the key steps in artificial neural networks (ANN) forecasting is the determination of significant input variables.
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