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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.