Committees Of Specialized Conceptual Hydrological Models: Comparative Study

Committee modelling approach is skillful prediction in the domain of hydrological modelling that allows explicitly to derive predictive model outputs. In this approach, the different individual models are optimally combined. Generally if a single hydrological model or the model calibrated by the single aggregated objective function it is hard to capture all facets of a complex process and to present the best possible model outputs. This model could be either capable for high flows or for low flows or not for both cases hence more flexible modelling architectures are required. Here the possibilities is building several specialized models each of which is responsible for a particular sub-process (high flows or low flows), and combining them using dynamic weights – thus forming a committee model. In this study we compare two different types of committee models: (i) the combine model based on fuzzy memberships function (Kayastha et al. 2013, Fenicia et al. 2007) and (ii) the combine model based on weights that calculated from hydrological states (Oudin et al. 2006). Before combining the models the individual hydrological models are calibrated by Adaptive Cluster Covering Algorithm (Solomatine 1999) for high and low flows with (different) suitable objective functions. The committee model based on fuzzy memberships does not generate additional water in the system (preserves water balance), however there is no guarantee for this in case of committees based on hydrological states. The relative performances of the two different committee models and their characteristics are illustrated, with an application to HBV hydrological models in Bagmati catchment in Nepal

Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

This study presents an approach to combine uncertainties of the hydrological model outputs predicted from a number of machine learning models. The machine learning based uncertainty prediction approach is very useful for estimation of hydrological models\u27 uncertainty in particular hydro-metrological situation in real-time application [1]. In this approach the hydrological model realizations from Monte Carlo simulations are used to build different machine learning uncertainty models to predict uncertainty (quantiles of pdf) of the a deterministic output from hydrological model . Uncertainty models are trained using antecedent precipitation and streamflows as inputs. The trained models are then employed to predict the model output uncertainty which is specific for the new input data. We used three machine learning models namely artificial neural networks, model tree, locally weighted regression to predict output uncertainties. These three models produce similar verification results, which can be improved by merging their outputs dynamically. We propose an approach to form a committee of the three models to combine their outputs. The approach is applied to estimate uncertainty of streamflows simulation from a conceptual hydrological model in the Brue catchment in UK and the Bagmati catchment in Nepal. The verification results show that merged output is better than an individual model output. [1] D. L. Shrestha, N. Kayastha, and D. P. Solomatine, and R. Price. Encapsulation of parameteric uncertainty statistics by various predictive machine learning models: MLUE method, Journal of Hydroinformatic, in press, 2013