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Title: Downward approach for streamflow estimation, forecasting for small-scale to large-scale catchments: Learning from data.
Keywords: Rainfall-runoff modelling, Data driven models, Model-attributed errors, Forecasting accuracy, Data time interval, Adjacent differencing
Issue Date: 30-Apr-2012
Citation: BASNAYAKE MUDIYANSELAGE LEKHANGANI ARUNODA BASNAYAKE (2012-04-30). Downward approach for streamflow estimation, forecasting for small-scale to large-scale catchments: Learning from data.. ScholarBank@NUS Repository.
Abstract: Data driven models (DDMs) are recognized as models that offer computationally fast yet sufficiently accurate solutions for modelling rainfall-runoff (R-R) process. In so doing, DDMs are used in operational management systems. In the present state, most of the DDMs on R-R process modelling are limited to approximating a single input-output relationship. These models are rather general and not specific enough to capture the temporal and spatial variation of R-R process. From the operational perspective, it is highly imperative to find out the means of improving R-R process representation of DDMs and other influential factors on forecasting accuracy. This research examined the potential reduction of model-attributed errors by improving the R-R process representation. The effect of data time interval on forecasting accuracy was investigated. It was shown that the errors could be reduced in lump catchment models by identifying and representing the temporally dominant processes. Adjacent differenced discharge data was effective and provided improved forecasts at multi-step-ahead. Furthermore, an approach for extending the lump catchment models to capture the intra-catchment variation of hydrological processes was demonstrated. The proposed approach was found to improve the forecasts over longer prediction horizon.
Appears in Collections:Ph.D Theses (Open)

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