1. Classification of models
Prepared by
M .U. Kale
Assistant professor
Irrigation & Drainage Engineering
Dr. P.D.K.V. Akola

2. 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.

3. Disadvantages physically based model:
Excessive data requirements
Large computational demand
Over parameterization effect
Parameter redundancy effect

4. 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.

5. 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.

6. 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

7. 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

8. Artificial intelligence applications
Artificial Neural Networks (ANN)
An expert system
Genetic Algorithms
Speech recognition
Understanding natural language
Computer vision

9. 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.

10. 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

11. 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

12. Thank you!