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

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