Choosing various likelihood functions on uncertainty assessment in groundwater simulation-optimization model

Choosing the observational likelihood in state-space stock assessment models

Data used in stock assessment models result from combinations of biological, ecological, fishery, and sampling processes. Since different types of errors propagate through these processes it can be difficult to identify a particular family of distributions for modelling errors on observations a priori. By implementing several observational likelihoods, modelling both numbersand proportions-at-a...

A Stochastic Optimization Model for Groundwater Management

Structural uncertainty assessment in a discharge simulation model

A major goal in hydrological modelling is to identify and quantify different sources of uncertainty in the modelling process. This paper analyses the structural uncertainty in a streamflow modelling system by investigating a set of models with increasing model structure complexity. The models are applied to two basins: Kielstau in Germany and XitaoXi in China. The results show that the model st...

A Stochastic Optimization Model for Groundwater Management

Design of an Augmented Automatic Choosing Control by Lyapunov Functions Using Gradient Optimization Automatic Choosing Functions

In this paper we consider a nonlinear feedback control called augmented automatic choosing control (AACC) using the gradient optimization automatic choosing functions for nonlinear systems. Constant terms which arise from sectionwise linearization of a given nonlinear system are treated as coefficients of a stable zero dynamics. Parameters included in the control are suboptimally selected by ex...