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Role of Calibration and Validation
in modeling
Submitted by
M .U. Kale
Assistant professor
Irrigation & Drainage Engineering,
Dr. P.D.K.V., Akola
Role of Calibration and Validation
Model:
An assembly of concepts in the form of a mathematical equation
that portrays understanding of a natural phenomenon.
Verification:
Examination of the numerical technique in the computer code to
ascertain that it truly represents the conceptual mode l and that
there are no inherent numerical problems with obtaining a
solution.
Calibration:
A test of a model with known input and output information that is
used to adjust or estimate factors for which data are not available.
Validation :
Comparison of model results wit h numerical data independently
derived from experiments or observations of the environment.
Schematization and discretization
 Schematizations:
 Modeling can be explained as schematization of real world
particularly true in 1 D modeling.
 Most important aspect of modeling .
 Discretization:
 Concerns the process of transferring continuous models and
equations into discrete counterparts.
 This process is usually carried out as a first step toward making
them suitable for numerical evaluation and implementation on
digital computers.
Modeling process
Data Collection
Phase I
Model input preparation
Parameter evaluation
Calibration
Phase II
Model Testing
Validation
Post audit
Phase III
Analysis of alternatives
Model calibration:
The procedure of adjustment of parameter values
of a model to reproduce the response of a catchment
under study within the range of accuracy specified in the
performance criteria.
Model validation:
Substantiation that a model within its domain of
applicability possesses a satisfactory range of accuracy
consistent with the intended application of the model.
Hydrologic Modeling
Drainage Systems – Watersheds - Catchments
Synthesis - projection of climatological records
Simulation - mathematical representation of
the hydrologic response
“ART” - visualization of the system
Science - understanding physical processes
Purpose of Hydrologic Models:
1. Characterize storm runoff
(peaks/volumes/quality)
2. Determine the effects of basin changes
3. Determine the effects of control options
4. Perform hydraulic designs
5. Perform frequency analysis
6. Provide input to other models (water surface
profile, economic, and receiving water quality)
Hydrologic Model Conceptualization:
• Inputs (forcing function)
• Outputs (response function)
• Storage and transfer (process functions)
INPUTS
Storage
and
Transfer
OUTPUTS
Classification of Hydrologic model
 Conceptual Models –
Idealization of processes as stores, buckets, parameterizations
– simplified equations representing mass, momentum,
energy.
 Physically-based Models –
“rigorous numerical solution of partial differential equations
governing flow through porous media, overland and channel
flows. “
 Hybrid models:
Hybrid metric-conceptual models have been developed to
combine the strengths of data-based and conceptual models.
Other Types of Models:
Lumped model
Distributed model
Lumped models treat the catchment
as a single unit, with state variables
that represent averages over the
catchment area.
Distributed models make predictions
that are distributed in space, with state
variables that represent local averages, by
discretising the catchment into a number
of elements (or grid squares).
In general a lumped model is expressed
by differential or empirical algebraic
equations, taking no account of spatial
variability
of
processes,
inputs,
boundary
conditions and system (catchment)
geometric characteristics
Distributed models hence are capable to
some extent of taking into account
spatial variability in processes, inputs,
boundary conditions, and catchment
characteristics.
Deterministic model
Stochastic model
1. Models can be classified as
deterministic when the results are
uniquely determined through known
relationships between the states and
data.
1. Stochastic models use random
variables to represent process
uncertainty and generate different
results from one set of input data
and parameter values when they run
under “externally seen” identical
conditions
2. Deterministic models produce a
single result from a simulation with a
single set of input data and parameter
values, and a given input will always
produce the same output, if the
parameter values are kept constant.
2. This allows some randomness or
uncertainty in the possible outcome
due to uncertainty in input variables,
boundary conditions or model
parameters.
Introduction to Popular Hydrologic Models
MIKE11 - General description:
 Software package developed by Danish Hydraulic Institute
(DHI) for simulation of flow, sediment transport and water
quality in estuaries, river, irrigation system and similar water
bodies
 User - friendly tool for design, management and operation of
river basins and channel networks.
What is MIKE 11…..
MIKE11 is among the most recognised models in river
hydraulics. The model was originally developed for river
hydrodynamics and through continued development it today
offers various wave models and a large number of
sophisticated features for hydraulic analysis, flooding,
hydraulic structures etc.
Advantages of MIKE 11 :
 Easy to analyse and extract results.
 Flow paths must be known beforehand.
 MIKE 11 suitable for projects With many complex structures.
 Where short simulation time is important .
 Used in hundreds of consulting and research projects around
the world Wetland restoration.
Source water protection.
Water Resources Management.
Flood forecasting.
The modelling process
 Understand the problem
• reason to model a system ( e.g. what if a dam is built?)
• collect and analyse data
 Choosing variables
 Set up mathematical model
• describe situation
• write mathematical explanation using variables
 Assumptions about the system
 Construction of the mathematical model
 Computer simulation
• computer program
• input data and runs
• validation
 Simulation experiments
• interpret the solution, test outcomes
• improve the model
MIKE SHE:
 It is an integrated modelling framework for simulating all
components of the land phase of hydrologic cycle.
 That is, with MIKE SHE you can simulate evapotranspiration,
overland runoff, channel flow, unsaturated infiltration, and
saturated groundwater flow including the interactions between all
these processes.
Application of MIKE SHE:
 River basin management and planning
 Water supply design, management and optimization
 Irrigation and drainage
 Soil and water management
 Surface water impact from groundwater withdrawal
 Conjunctive use of groundwater and surface water
 Wetland management and restoration
 Ecological evaluations
 Groundwater management
 Environmental impact assessments
 Aquifer vulnerability mapping
 Contamination from waste disposal
 Surface water and groundwater quality remediation
 Floodplain studies
 Impact of land use and climate change
 Impact of agriculture (irrigation, drainage, nutrients and
pesticides, etc.)
Artificial neural network (ANN)
Artificial neural network (ANN) is an information processing system that roughly
replicates the behaviour of a human brain by emulating the operations and
connectivity of biological neurons.
From a mathematical point of view ANN is a complex non-linear function with many
parameters that are adjusted (calibrated, or trained) in such a way that the ANN
output becomes similar to the measured output on a known data set.
Basic elements in neural network structure
The ANN performs fundamentally like a human brain
Applications of ANN: ANNs have seen many successful application of neural
computing in commercial, academic and military
applications.
 Presently being used to solve a variety of problems such as
detection,
estimation,
discrimination,
classification,
optimization, prediction, interpolation, extrapolation,
clustering, or some combination of these problems in many
scientific and engineering applications.
 Typically hydrological applications that ANNs have the
capability for is to rainfall-runoff, stream flow,
groundwater management, water quality simulation and
precipitation phenomena.