(ii) Modeling of Water Resources Systems Introduction and Basic Components D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2.

Slides:



Advertisements
Similar presentations
Quantitative Techniques An Introduction
Advertisements

Organization Example - PWA
Linear Programming Problem. Introduction Linear Programming was developed by George B Dantzing in 1947 for solving military logistic operations.
INTRODUCTION TO MODELING
Chapter 1 Introduction to Modeling DECISION MODELING WITH MICROSOFT EXCEL Copyright 2001 Prentice Hall.
Substantive environmental provisions Prof. Gyula Bándi.
Geog 409: Advanced Spatial Analysis & Modelling © J.M. Piwowar1Principles of Spatial Modelling.
Decision Making: An Introduction 1. 2 Decision Making Decision Making is a process of choosing among two or more alternative courses of action for the.
FIN 685: Risk Management Topic 5: Simulation Larry Schrenk, Instructor.
D Nagesh Kumar, IIScOptimization Methods: M1L1 1 Introduction and Basic Concepts (i) Historical Development and Model Building.
Computational Methods for Management and Economics Carla Gomes Module 3 OR Modeling Approach.
Relational Data Mining in Finance Haonan Zhang CFWin /04/2003.
Knowledge Acquisitioning. Definition The transfer and transformation of potential problem solving expertise from some knowledge source to a program.
Introduction and Basic Concepts
Chapter 1 INTRODUCTION TO MANAGEMENT AND ORGANIZATIONS
AAEC 3315 Agricultural Price Theory
1 Enviromatics Decision support systems Decision support systems Вонр. проф. д-р Александар Маркоски Технички факултет – Битола 2008 год.
Feedback Control Systems (FCS)
Software Life Cycle Model
Systems Approach.
The Context of Forest Management & Economics, Modeling Fundamentals Lecture 1 (03/30/2015)
SYSTEM ANALYSIS AND DESIGN
By Saparila Worokinasih
SEMINAR ON :. ORGANISATION Organizations are formal social units devoted to attainment of specific goals. Organizations use certain resources to produce.
DSS Modeling Current trends – Multidimensional analysis (modeling) A modeling method that involves data analysis in several dimensions – Influence diagram.
Business Analysis and Essential Competencies
Modeling & Simulation: An Introduction Some slides in this presentation have been copyrighted to Dr. Amr Elmougy.
D Nagesh Kumar, IIScOptimization Methods: M1L3 1 Introduction and Basic Concepts (iii) Classification of Optimization Problems.
An Introduction to Programming and Algorithms. Course Objectives A basic understanding of engineering problem solving process. A basic understanding of.
Managerial Decision Making and Problem Solving
Introduction A GENERAL MODEL OF SYSTEM OPTIMIZATION.
Decision Making.
Major objective of this course is: Design and analysis of modern algorithms Different variants Accuracy Efficiency Comparing efficiencies Motivation thinking.
Indicators to Measure Progress and Performance IWRM Training Course for the Mekong July 20-31, 2009.
Stewart L. Tubbs McGraw-Hill© 2007 The McGraw-Hill Companies, Inc. All rights reserved. 1 C H A P T E R 1 What is Small Group Interaction?
How are decisions made in organizations?
THIS PRESENTATION IS INTENDED AS ONE COMPLETE PRESENTATION. HOWEVER, IT IS DIVIDED INTO 3 PARTS IN ORDER TO FACILITATE EASIER DOWNLOADING AND VIEWING,
Accounting Information System. System A system is a set of parts coordinated to accomplish a set of goals. It is also an organized set of interrelated.
D Nagesh Kumar, IIScOptimization Methods: M4L4 1 Linear Programming Applications Structural & Water Resources Problems.
Foundations of Technology The Systems Model
Topics Covered:  System System  Sub system Sub system  Characteristics of System Characteristics of System  Elements of Systems Elements of Systems.
OPERATING SYSTEMS CS 3530 Summer 2014 Systems and Models Chapter 03.
(i) System Components, Planning and Management Introduction and Basic Components D. Nagesh Kumar, IISc Water Resources Systems Planning and Management:
Multi-objective Optimization
Linear Programming and Applications
Water Resources System Modeling
Stochastic Optimization
Development of a community-based participatory network for integrated solid waste management By: Y.P. Cai, G.H. Huang, Q. Tan & G.C. Li EVSE, Faculty of.
© 2014 Cengage Learning. All Rights Reserved. May not be copied, scanned, or duplicated, in whole or in part, except for use as permitted in a license.
D Nagesh Kumar, IIScOptimization Methods: M5L1 1 Dynamic Programming Introduction.
Multi-objective Optimization
Introduction and River Basin Simulation D Nagesh Kumar, IISc Water Resources Planning and Management: M7L1 Simulation.
Uncertainty and Reliability Analysis D Nagesh Kumar, IISc Water Resources Planning and Management: M6L2 Stochastic Optimization.
D Nagesh Kumar, IIScOptimization Methods: M8L6 1 Advanced Topics in Optimization Applications in Civil Engineering.
GIS-based Hydrologic Modeling Jan Boll and Erin Brooks Biological and Agricultural Engineering University of Idaho.
Introduction and Preliminaries D Nagesh Kumar, IISc Water Resources Planning and Management: M4L1 Dynamic Programming and Applications.
Optimization Techniques for Natural Resources SEFS 540 / ESRM 490 B Lecture 1 (3/30/2016)
Modelling & Simulation of Semiconductor Devices Lecture 1 & 2 Introduction to Modelling & Simulation.
Traffic Simulation L2 – Introduction to simulation Ing. Ondřej Přibyl, Ph.D.
Advanced Algorithms Analysis and Design
OPERATING SYSTEMS CS 3502 Fall 2017
OVERVIEW Impact of Modelling and simulation in Mechatronics system
Strategic Planning for Learning Organizations
Software Design Mr. Manoj Kumar Kar.
Software Engineering (CSI 321)
Optimization Techniques for Natural Resources SEFS 540 / ESRM 490 B
Objective of This Course
Planning process in river basin management
Dr. Arslan Ornek MATHEMATICAL MODELS
Presentation transcript:

(ii) Modeling of Water Resources Systems Introduction and Basic Components D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2

Objectives D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2 Concept of a system Classification of systems Modeling of water resources systems 2

Concept of a system D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2 System: Set of objects which interact in a regular, interdependent manner A collection of various factors arranged in an ordered form with some purpose or goal A system is characterised by: A system boundary: Rule that determines whether an element is a part of the system or the environment Statement of input and output interactions with the environment Statement of interrelationships between various elements of the system called feedback State of the system : Conditions or indicates the activity in the system at a given time e.g.. water level in a reservoir, depth of flow. System analysis : Arriving at the management decisions based on the systematic and efficient organization and analysis of relevant information 3

Classification of systems D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2 Physical system: One that exists in the real world Sequential system: A physical system with input, working medium and output Static system: Output depends only on current input Dynamical system: Output depends only on current and previous inputs Time-varying system: Kernel changes with time Time-invariant system: Kernel does not change Deterministic system: Kernel and inputs are known Stochastic system: Kernel and inputs are not exactly known Continuous-time systems: Inputs, outputs and kernel vary continuously with time Discrete time systems: Inputs, outputs and kernel are known at discrete times 4

Hydrologic System D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2 Hydrologic systems are distributed in time and space Systems are divided into subsystems for the purpose of solution Hydrologic system: A physical, sequential and dynamic system The input-output relationship can be expressed as: y(t) = Φ [x(t)] where x(t) and y(t) are time functions of input and output respectively; Φ[] is the transfer function or the operation performed to transform input to output For a catchment system, the input is water or energy of various forms and the transfer function may be the unit hydrograph 5

System Analysis D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2 An optimal plan is selected through a systematic search and evaluation of various alternatives that meet the objectives and constraints System analysis consists of five steps: Defining the problem Identifying the system and its elements Defining the objectives and constraints Identify feasible alternatives that satisfy the above said constraints Identify the most efficient alternative that best meets the objectives 6

Need for System Approach D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2 Water resource projects are large-scale and most of the times permanent ones They have huge impact on both society and economy They need the participation from various fields simultaneously Require heavy capital investments and hence have a major effect on economy Even a small improvement over traditional solution is thus desirable Adoption of systems approach is better than conventional techniques based only on experience, to achieve an improved overall project output 7

Advantages of System Approach D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2 Focuses on definite goals and objectives Systematic searching for alternatives Provides with modern technology to analyse the system scientifically and objectively Forces the user to identify the known and not readily known elements of the system Regularly provides feedback from each step thus providing flexibility for correction and modification Deal with highly complex multi-objective multi-constraint problems 8

Disadvantages of System Approach D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2 System approach is not suitable when there is a lack of proper and full understanding of water resources systems and its conflicting objectives Most of the decisions are irreversible in nature and hence hazardous if used without recognizing and integrating the quantitative and non-quantitative dimensions of the system (physical, social, economic, political etc) Practical difficulties due to the gap between the theory and the practice Transfer of technological advances to practical on field use requires professionals with both theoretical background and practical experience Most water resources systems are complex thereby demanding difficult mathematical computations Unavailability or high cost of software and also unavailability of a part of data required Dealing with intangibles. Systems are not that simple to be fully expressed in mathematical terms 9

Modelling of Water Resources Systems (WRS) D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2 Models are simplified representations of actual real-world systems through assumptions and approximations Physical models: Design physical components of a project Mathematical models: Evaluate consequences of alternative plans Analyze the various physical processes through arithmetical and logical statements Features to be included and to be excluded will be decided by the modeller Models are not very expensive and convenient 10

Modeling of Water Resources Systems… D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2 Accuracy of the model depends on the skill of the modeller and his/ her understanding of the real system and decision making process and also on the time and money available Models produce information - but not decisions. They aid planners and managers to improve their understanding and provide various alternatives that help in the decision making process 11

Modeling of Water Resources Systems… D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2 Example: An example of system management is water quality problem in which the objective is to maintain certain quality standards throughout the stream Stakeholders involved are Pollution control Agency (PCA) and the dischargers (municipal and industrial). Goal of PCA is to improve the water quality while that of the dischargers is to reduce the water treatment cost Necessitates the need for a waste load allocation model with multiple objectives and conflicting constraints 12

Modeling of Water Resources Systems… D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2 Modeling is not suitable for each and every water resources planning and management problem. It is appropriate when: Objectives are well defined and beneficiaries (organizations and individuals) are identified Best decision is not obvious among various alternatives that satisfy the stated objectives System and objectives can be expressed by reasonably through mathematical representations The impacts (hydrological, economic, environmental and ecological) resulting from the decision can be better estimated through the use of models The data is readily available for the estimation of parameters of the model

Modeling of Water Resources Systems… D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2 The main challenges for any planners and managers are to: identify creative alternative solutions find out the interest of each group involved in order to reach an understanding of the issues and a consensus on what to do develop and use models and reach a common or shared understanding and agreement that is consistent with the individual values by presenting the results make decisions and implement them duly taking care of the differences in opinions, social values and objectives 14

System Decomposition D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2 A real WRS is a complex system with Multiple objectives and decision makers Numerous variables and parameters Large spatial and temporal data base Numerous subsystems Nonlinear complex relationships among variables Hierarchial approach by Haimes (1977) – Model the complex large-scale systems first decomposing into independent sub- systems Concepts of levels and layers 15

System Decomposition… D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2 Four major decompositions Temporal: Planning horizon of WRS projects span large periods System conditions drastically change with time Planning is done by segmenting the time periods Plans should be compatible and coordinated with each other Physical – hydrological: Water resources program may consider several river basins Each basin can be divided into sub-basins 16

System Decomposition… D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2 Political - geographical: Basin may cover two or more national territories Different political or administrative units Decomposed considering either political or natural boundaries Goal oriented or functional: Analysis with respect to economic and functional goals is done E.g.: Demand – Supply models, Irrigation, hydroelectric models 17

D. Nagesh Kumar, IISc Water Resources Systems Planning and Management: M1L2 Thank You