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Classification of models
Prepared by
M .U. Kale
Assistant professor
Irrigation & Drainage Engineering
Dr. P.D.K.V. Akola
Physically based model
 It also called knowledge driven model
 It aims to reproduce the real world hydrological model
 It’s behavior in a physically realistic manner
 It is based on detailed description of the system
 They take into account the physical law that underline
the processes.
Disadvantages physically based model:

Excessive data requirements

Large computational demand

Over parameterization effect

Parameter redundancy effect
Data driven models
 It is based on the analysis of all the data characterizing
the system under study.
 It define on the basis of connection between the system
state variables with only limited number of assumption
about the physically behavior of the system.
 It is developed with the contribution ot artificial
intelligence, data mining, computational intelligence.
 Data driven model are based on pure relationships between
input (X) and output (Y) data.
 It is a modeling approach, which focuses on
using
machine learning methods in building models of physical
processes.
 More complex data driven models are highly non-linear.
Artificial intelligence
 John McCarthy, one of the founders of artificial
intelligence research, once defined the field as
“getting a computer to do things which, when done by
people, are said to involve intelligence.”
 “Artificial Intelligence, or AI for short, is a combination
of computer science, physiology, and philosophy
Artificial Intelligence Approach
 Acting humanly: The Turing Test approach
 Thinking humanly: The cognitive modeling approach
 Thinking rationally: The "laws of thought" approach
 Acting rationally: The rational agent approach
Artificial intelligence applications
 Artificial Neural Networks (ANN)
 An expert system
 Genetic Algorithms
 Speech recognition
 Understanding natural language
 Computer vision
Artificial Neural Networks (ANN)
 It simulate neural networks found in nature, such as
the human brain. The term artificial is used to
distinguish ANNs from their biological counterparts.
 An ANN is trained through a learning process, and
knowledge is retained through synaptic weights.
Synaptic weights between nodes are adjusted based
on the desired output.
Advantages of ANN
 Able to simulate non-linearity in a system
 They can also effectively distinguish relevant data
from irrelevant data.
 It is non- parametric
 Self adjusting
 Flexible and compact
Expert system
 An
expert system is computer software that
embodies a significant portion of the specialized
knowledge of a human expert in a specific, narrow
domain, and emulates the decision-making ability of
the human expert
Thank you!