Modelling, Forecasting, Artificial Neural Network and Expert System in Fisheries and Aquaculture

Transcription

1 Modelling, Forecasting, Artificial Neural Network and Expert System in Fisheries and Aquaculture Editors Ajit Kumar Roy Niranjan Sarangi 2009 DAYA PUBLISHING HOUSE Delhi

2 iv 2009 AJIT KUMAR ROY (b ) NIRANJAN SARANGI (b ) ISBN ISBN Technical Assistance Mrs. N. Panda Sri D. P. Rath All rights reserved, including the right to translate or to reproduce this book or parts thereof except for brief quotations in critical reviews. (The fact, figures and views presented in this document are those of respective authors). Published by : Daya Publishing House 1123/74, Deva Ram Park Tri Nagar, Delhi Phone: (011) Fax: (011) dayabooks@vsnl.com website : Showroom : /23, Ansari Road, Darya Ganj, New Delhi Phone: , Laser Typesetting : Classic Computer Services Delhi Printed at : Chawla Offset Printers Delhi PRINTED IN INDIA

3 Mk-,l v ;Iiu miegkfuns'kd (erl;) DR. S. AYYAPPAN Deputy Director General (Fisheries) Hkkjrh; d`f"k vuqla/kku ifj"kn d`f"k vuqla?kku HkoUk&2] iwlk] ubz fnyyh & INDIAN COUNCIL OF AGRICULTURAL RESEARCH KRISHI ANUSANDHAN BHAVAN-II, PUSA, NEW DELHI Foreword With a bountiful biophysical resource base India is well-positioned to be a more significant player of aquaculture production as it has established itself as one of the fastest growing food producing sector. It contributes about 4.2% of global aquaculture production of 45.5 million tones. Apart from supplying cheap protein, this sector is contributing significantly in poverty alleviation, food security and income generation for rural population. Demand from aquaculture sector is increasing because production from capture fisheries has leveled off. In recent past Government of India has given due attention for all round development of aquaculture sector through establishment of National Fisheries Development Board to augment the productivity further on a sustained basis, through capacity building in the form of training of manpower in adoption and application of viable technologies across the country. To support the government initiative reliable, accurate and timely statistics on future demand and supply is needed. Under the situation prediction and forecasting of likely population, production, productiveness of different component of fish production system involving energy and water requirement, whether, climate disease and paste, price and market demand and supply for different fishery production is essential for planners. I am extremely happy to learn that a book entitled Modelling, Forecasting, Artificial Neural Network and Expert System in Fisheries and Aquaculture has been compiled and edited with up-to-date and interesting articles contributed by the eminent professionals of the national institutes. Application of various forecasting Ph: (O) , (R) ; Fax: ayyappans@icar1.nic.in &

4 vi methods provides an accessible and comprehensive account of recent developments in statistical and economic forecasting as this enables unraveling of underlying relationship among crucial variables in fisheries research. Each of the sixteen chapters of this book contributed by experts working in the field deals not only with methodological aspects of estimation of fish stock, modelling, forecasting, neural network, fuzzy logic, soft computing, productivity analysis, quantitative and qualitative analysis but also application on real data with illustrated examples using statistical and econometric packages. Some of the chapters will give insight for planners about the future scenario of fish production which in turn help taking appropriate decisions. It is expected that this publication will be useful for research workers and planners engaged in fisheries research equally that may serve as a unique source of reference material on the application latest tools and techniques on modelling and forecasting. I wish this maiden endeavor a grand success. S. Ayyappan

5 Preface Most challenging task to the planners of fisheries and aquaculture is to produce food and nutritional security to the present population as well as to plan for supply of food to the future population. Therefore, planners should have clear idea about the possible future population and accordingly their food and nutrition requirement. Prediction and forecasting of likely population, production, likely prediction of productiveness of different component of fish production system. Forecasting of energy and water requirement, whether, climate, disease and paste, price and market demand and supply for different fishery production is essential for sustainable fish production. Keeping these points in view the book has been edited that provides an accessible and comprehensive account of recent developments in modelling and forecasting in fisheries and aquaculture. Each chapter has been specially written by an expert in the field, bringing together in a single volume a range of approaches and views. Uniquely, modelling and forecasting in a single volume, the book provides a comprehensive account of the leading approaches and modelling strategies that are routinely used. This book is basically the result of twelve years of teaching the material to state officials and students during conducting yearly workshops. This book further aims to bring the principles, techniques for modelling and forecasting of fish production for both capture and culture fisheries. The book contains the following sixteen chapters namely 1.Statistical modelling in Fisheries Research: An Overview 2. Is Fisheries Sector in India Changing its Gear? 3. Non Linear Models for Forecasting Fish Production from Ponds 4. Statistical Models and their Application in Fishery Science Research 5. Methods of Forecasting and their Application in Fishery Science 6. Modelling in Aquaculture 7. Modelling of Multiobjective Freshwater Aquaculture 8. A Revisit to Building up Models in Fishery Research 9. Quantitative Techniques for Fish Stock Assessment 10. Time Series

6 viii Modelling and its Applications in Fisheries 11. Tropho-Dynamic Modelling for Ecosystem Based Fisheries Management 12. Forecasting of Fish Production of India by culture environment and species 13. Expert Systems and Simulation Studies in Agriculture/Aquaculture 14. Recent Trends in Artificial Neural Network (ANN) and Fuzzy Logic and their Application in Aquaculture 15. Soft Computing Techniques for Data Analysis and Modelling in Fisheries 16. An Econometric Model for Productivity Analysis of Aquaculture Farms. The publication is likely to cater to the need of a wide audience of students, researchers, scientists, professionals, policy makers and in fact anyone who works in aquaculture and fisheries. The material owes its existence to many conversations and collaborations with many resource persons delivering invited talks. I am very grateful to them for their patience and tolerance as it progressed from crude notes to its present form. The endeavor could not have been materialized without the active support in terms of contributing articles by the dedicated subject matter specialists, professionals from various National Institutes of repute. All out support, guidance, suggestion and encouragements from Dr. M.K. Bhan, Secretary, DBT and Dr. T.Madhan Mohan, Advisor, DBT are thankfully acknowledged. Heart felt thanks are due to Dr. S. Ayyappan, DDG (Fy.), ICAR for constant encouragement to make the programme a success. Invitation to resource persons, conducting the workshop and subsequent publication of this book would not have been possible without the financial support of Bioinformatics Division of Dept. of Biotechnology, Ministry of Science & Technology, and Government of India. Ajit Kumar Roy Niranjan Sarangi

7 Contents Foreword Preface List of Contributors v vii xi 1. Statistical Modelling in Fisheries Research: An Overview 1 Prajneshu 2. Is Fisheries Sector in India Changing its Gear? 13 B. Ganesh Kumar 3. Non-Linear Models for Forecasting Fish Production from Ponds 27 Lalmohan Bhar 4. Statistical Models and their Application in Fishery Science Research 37 R. Venugopalan and Prajneshu 5. Methods of Forecasting and their Application in Fishery Science 47 P.K. Sahu 6. Modelling in Aquaculture 67 A.K. Roy 7. Modelling of Multiobjective Freshwater Aquaculture 87 Pratap K.J. Mohapatra, Santosh K. Prusty and C.K. Mukherjee

8 8. A Revisit to Building up Models in Fishery Research 102 Satyabrata Pal and Subhabaha Pal 9. Quantitative Techniques for Fish Stock Assessment 112 R.S. Biradar 10. Time Series Modelling and its Applications in Fisheries 124 S. Ravichandran 11. Tropho-Dynamic Modelling for Ecosystem Based Fisheries Management 148 Preetha Panikkar and M. Feroz Khan 12. Forecasting of Fish Production of India by Culture Environment and Species 155 A.K. Roy and Nirupama Panda 13. Expert Systems and Simulation Studies in Agriculture/Aquaculture 179 J. Panduranga Rao 14. Recent Trends in Artificial Neural Network (ANN) and Fuzzy Logic and their Application in Aquaculture 193 M. Balakrishnan and R.C. Srivastava 15. Soft Computing Techniques for Data Analysis and Modelling in Fisheries 206 H.C. Verma 16. An Econometric Model for Productivity Analysis of Aquaculture Farms 220 A.K. Roy, G.S. Saha, N. Sarangi and Nibedita Jena x Index 235

9 List of Contributors 1. Statistical Modelling in Fisheries Research: An Overview Prajneshu Head, Biometrics Division Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi Is Fisheries Sector in India Changing its Gear? B. Ganesh Kumar Senior Scientist National Centre for Agricultural Economics & Policy Research, P.B.No.11305, DPS Marg, Pusa, New Delhi Non Linear Models for Forecasting Fish Production from Ponds Lalmohan Bhar Senior Scientist, Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi Statistical Models and their Application in Fishery Science Research R. Venugopalan Senior Scientist Agricultural Statistics, Indian Institute of Horticultural Research (ICAR), Bangalore Prajneshu Head, Biometrics Division Indian Agricultural Statistics Research Institute, Library Avenue, New Delhi

10 5. Methods of Forecasting and their Application in Fishery Science P.K. Sahu Reader Department of Agricultural Statistics, Bidhan Chandra Krishi Viswa Vidyalaya, Mohanpur, Nadia, West Bengal 6. Modelling in Aquaculture A.K. Roy Principal Scientist & Head, Social Science Section, Central Institute of Freshwater Aquaculture (CIFA), Bhubaneswar Modelling of Multiobjective Freshwater Aquaculture Pratap K.J. Mohapatra Head, Industrial Engineering Department, Indian Institute of Technology, Kharagpur Santosh K. Prusty Research Fellow Indian Institute of Technology, Kharagpur C. K. Mukherjee Professor, Department of Agriculture & Food Engineering Indian Institute of Technology, Kharagpur A Revisit to Building up Models in Fishery Research Satyabrata Pal Ex-Dean, Bidhan Chandra Krishi Viswa Vidyalaya Mohanpur, Nadia, West Bengal Subhabaha Pal xii Researcher, Bidhan Chandra Krishi Viswa Vidyalaya Mohanpur, Nadia, West Bengal 9. Quantitative Techniques for Fish Stock Assessment R.S. Biradar Principal Scientist & Head, Central Institute of Fisheries Education, Deemed University, Fishery University Road, Seven Bungalows, Versova, Mumbai

11 10. Time Series Modelling and its Applications in Fisheries S. Ravichandran Sr. Scientist (Statistics), Directorate of Rice Research, Rajendranagar, Hyderabad Tropho-Dynamic Modelling for Ecosystem Based Fisheries Management Preetha Panikkar Scientist, Reservoir Division, Central Inland Fisheries Research Institute, Hessarghatta Lake Post, Bangalore M. Feroz Khan Sr. Scientist, Reservoir Division, Central Inland Fisheries Research Institute, Hessarghatta Lake Post, Bangalore Forecasting of Fish Production of India by Culture Environment and Species A. K. Roy Principal Scientist & Head, Social Science Section, Central Institute of Freshwater Aquaculture (CIFA), Bhubaneswar Nirupama Panda Technical Officer (T-6), Social Science Section, Central Institute of Freshwater Aquaculture (CIFA), Bhubaneswar Expert Systems and Simulation Studies in Agriculture/Aquaculture J. Panduranga Rao Consultant, Agricultural Finance Corporation, Sirisuban Apartments, Somajigoda Hyderabad 14. Recent Trends in Artificial Neural Network (Ann) and Fuzzy Logic and their Application in Aquaculture M. Balakrishnan Co-ordinator, BTIS, Central Agricultural Research Institute, Port Blair , Andaman & Nicobar Islands R.C. Srivastava xiii Director, Central Agricultural Research Institute, Port Blair , Andaman & Nicobar Islands

12 15. Soft Computing Techniques for Data Analysis and Modelling in Fisheries H.C. Verma Scientist (SS), Water Technology Centre for Eastern Region (ICAR), Chandrasekharpur, Bhubaneswar An Econometric Model for Productivity Analysis of Aquaculture Farms A.K. Roy Principal Scientist & Head, Social Science Section, Central Institute of Freshwater Aquaculture (CIFA), Bhubaneswar G. S. Saha Sr. Scientist, Social Science Section, Central Institute of Freshwater Aquaculture (CIFA), Bhubaneswar N. Sarangi Director, Central Institute of Freshwater Aquaculture (CIFA), Bhubaneswar Nibedita Jena xiv Economic Investigator, Directorate of Economic Research, Khadi & Village Industry Commission, Gramodaya, 3, Irla Road, Vile Parle (West), Mumbai

13 Modelling, Forecasting, Artificial Neural Network Pages 1 12 and Expert System in Fisheries Editors: Ajit Kumar Roy & Niranjan Sarangi Published by: DAYA PUBLISHING HOUSE (2009) Chapter 1 Statistical Modelling in Fisheries Research: An Overview Prajneshu Introduction Statistical modelling enables unraveling of underlying relationships among crucial variables in fisheries research. A statistical model is generally represented by an equation or a set of equations for describing the behaviour of a system. Some examples are: Length-weight relationship, Age-length relationship, Input-output relationship, Computation of compound growth rate, Catch-effort relationship, and Production over time. A statistical model can be either linear or nonlinear. A linear model is one in which all the parameters appear linearly. Some examples of linear model are: Multiple Linear Regression Model If Y is response variable, X i are predictors, and e is error term, then the model is Y = a 0 + a 1 X a p X p + e, Polynomial Models Y = a + b X + e Y = a + b X + c X 2 + e (First-order model) (Second-order or Quadratic or Curvilinear model). Method of least squares is generally employed for estimation of parameters of above models. It is well recognized that any type of statistical inquiry in which principles from some body of knowledge enter seriously into the analysis is likely to lead to a Nonlinear model (Seber and Wild, 2003). A nonlinear model is one in which at