## Abstract The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes
Error identification and decomposition in large stochastic rainfall-runoff models
✍ Scribed by Carlos E. Puente; Rafael L. Bras
- Publisher
- Elsevier Science
- Year
- 1987
- Tongue
- English
- Weight
- 707 KB
- Volume
- 23
- Category
- Article
- ISSN
- 0005-1098
No coin nor oath required. For personal study only.
✦ Synopsis
An approximate maximum likelihood procedure estimates the error statistics of a very large and complicated model of river basins while reducing the computational burden by decoupling soil states of the various subbasins forming the system and limiting stochastic dependence to the channel network connectivity.
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This paper considers the consequences of the stochastic error process in large non-linear forecasting models. As such models are non-linear, the deterministic forecast is neither the mean nor the mode of the density function of the endogenous variables. Under a specific assumption as to the class of
## Abstract The intention of the presented study is to gain a better understanding of the mechanisms that caused the bimodal rainfall–runoff responses which occurred up to the mid‐1970s regularly in the Schäfertal catchment and vanished after the onset of mining activities. Understanding this proce