Poster
1
COMPARATIVE STUDY OF LOGISTIC AND MATHUSIAN GROWTH MODEL IN PREDICTING OF FISH HARVESTING IN TERENGGANU UNIVERSITI TEKNOLOGI MARA (TERENGGANU) KAMPUS DUNGUN PROBLEM STATEMENT CONCLUSION AND RECOMENDATION OBJECTIVE METHODOLOGY SCOPE RESULT AND DISCUSSION INTRODUCTION REFERENCES ABSTRACT Due to the growth of human population, people doubting whether they will get enough protein from fish since fish provide the main source of fat and protein as well as major sources for human diet. Recently, consumers have become more conscious does the fish will be distributed enough for them when the number of people continuously increasing. In order to optimise fish harvesting yield, effective strategies need to be implement. Since mathematical growth model is good in predicting and estimating, it can be used to predict the future value for fish harvesting yield. By predicting fish harvesting yield, fisheries management can prepared effective strategies to optimize yield. In addition to that, fisheries management also can take the initiative to control and manage fish harvesting strategies. Mathematical model can be used widely in many sectors including in fisheries area. In recent years, the use of mathematical models has been extended to agriculture sector especially in fish harvesting yield to ensure continuous and optimum supply (Faris et al. 2012). By using separable differential equations can give us a mathematical formulation that can be used as a model in population growth. In order to estimate the population of animals as well as human population, using of mathematical model is a way of prediction. The method that give optimize harvesting outcome may be the best method for the fish prediction through the years. There are two growth models have been used in mathematical model namely Malthusian growth model and logistic growth model. Fish is the major source of fat and protein for the human being. Since the human population are increase over the years, people doubt about the fish supply. The purposes of the study are to make prediction of the fish harvesting by using mathematical growth model. It helps to identify the preferable method between two growth models which are logistic growth model and Malthusian growth model on fish harvesting prediction. Malthusian and logistic growth model are apply to the data that was collected from department of fisheries to make prediction. The models help to examine how different harvest strategies affect mean annual yield. The growth models are appropriate for population growth to estimate the highest continuing yield from fish harvesting strategies implemented. Besides that, it helps to predict the optimum quantity for harvesting that can ensure the fish supply is continuous. Finally, the results for both models are compared. The preferable method for prediction of fish harvesting yield to provide a better strategies is Malthusian growth model. These findings can assist fisheries management improve the strategies and increase the supply to meet the fish demand. a) To make prediction of the fish harvesting by using mathematical growth model. b) To identify the preferable method between two growth models which are logistic growth model and Malthusian growth model on fish harvesting prediction. Fisheries have been conducted in some places in Malaysia for few years ago. Nevertheless, This project not involves the whole Malaysia but only focusing on fisheries department in Terengganu. The scope narrowed and conducted at Terengganu so that the data collected will be more accurate. Data was collected from 2010 to 2012 and selected monthly by using secondary data from Department Of Fisheries Malaysia official website 1. Model development 2. Solving differential equation 3. Calculate growth rate and P(t) for both model. 4. Fit the data into available model using Excel software. 5. Determine the model constants and coefficient 6. Calculate the statistical parameters and absolute error (abs E) Malthusian growth model Logistic growth model Error = Figure below shows that the population exponentially declines at any fixed negative value of k. Population size depends on the growth rate, with the population decline as P (t) becomes smaller. Malthusian and logistic growth model was compared and found that Malthusian model is more preferable to make a prediction for fish harvesting yield. The graph line for Malthusian growth model is closed to the actual values that provide minimum error which is 1409.84 compared to the logistic growth model which is 1414.5. Solution obtained helps provide a guideline to fisheries management to optimizes values for the harvest yield and provide a solution to the people who doubting about the source of fish as a protein and fat. ROHAYU BINTI MOHAMED 2012434738 SUPERVISOR: ASSO PROF HJ AHMAD HJ AZIZ Figure below shows that the comparison between actual and prediction value of fish harvesting. • Aanes,S., Engen, S., Sather, B.E., Willebrand, T., Marcstro, V. (2002). Sustainable harvesting strategies of willow ptarmigan in a fluctuating environment. Ecological Applications, 12(1), pp. 281–290. • Boutayeb, A., Lamlili, M., & Boutayeb, W. A competition and harvesting model applied to a fish population along the Moroccan cost • Mohamed Faris Laham, Ishtrinayagy s. Krishnarajah & Jamilah Mohd Shariff.(2012). Fish Harvesting Management Strategies Using Logistic Growth Model. Malaysia 41(2)(2012) 84 . 1409 36 50754.12 , = = E abs
Transcript of Poster
COMPARATIVE STUDY OF LOGISTIC AND MATHUSIAN GROWTH MODEL IN PREDICTING OF FISH HARVESTING IN TERENGGANU
UNIVERSITI TEKNOLOGI MARA (TERENGGANU) KAMPUS DUNGUN
PROBLEM STATEMENT
CONCLUSION AND RECOMENDATION
OBJECTIVE
METHODOLOGY
SCOPE
RESULT AND DISCUSSION
INTRODUCTION
REFERENCES
ABSTRACT
Due to the growth of human population, people doubting whether they will get enough protein from fish since fish provide the main source of fat and protein as well as major sources for human diet. Recently, consumers have become more conscious does the fish will be distributed enough for them when the number of people continuously increasing. In order to optimise fish harvesting yield, effective strategies need to be implement. Since mathematical growth model is good in predicting and estimating, it can be used to predict the future value for fish harvesting yield. By predicting fish harvesting yield, fisheries management can prepared effective strategies to optimize yield. In addition to that, fisheries management also can take the initiative to control and manage fish harvesting strategies.
Mathematical model can be used widely in many sectors including in fisheries area. In recent years, the use of mathematical models has been extended to agriculture sector especially in fish harvesting yield to ensure continuous and optimum supply (Faris et al. 2012). By using separable differential equations can give us a mathematical formulation that can be used as a model in population growth. In order to estimate the population of animals as well as human population, using of mathematical model is a way of prediction. The method that give optimize harvesting outcome may be the best method for the fish prediction through the years. There are two growth models have been used in mathematical model namely Malthusian growth model and logistic growth model.
Fish is the major source of fat and protein for the human being. Since the human population are increase over the years, people doubt about the fish supply. The purposes of the study are to make prediction of the fish harvesting by using mathematical growth model. It helps to identify the preferable method between two growth models which are logistic growth model and Malthusian growth model on fish harvesting prediction. Malthusian and logistic growth model are apply to the data that was collected from department of fisheries to make prediction. The models help to examine how different harvest strategies affect mean annual yield. The growth models are appropriate for population growth to estimate the highest continuing yield from fish harvesting strategies implemented. Besides that, it helps to predict the optimum quantity for harvesting that can ensure the fish supply is continuous. Finally, the results for both models are compared. The preferable method for prediction of fish harvesting yield to provide a better strategies is Malthusian growth model. These findings can assist fisheries management improve the strategies and increase the supply to meet the fish demand.
a) To make prediction of the fish harvesting by using mathematical growth model. b) To identify the preferable method between two growth models which are logistic growth model and Malthusian growth model on fish harvesting prediction.
Fisheries have been conducted in some places in Malaysia for few years ago. Nevertheless, This project not involves the whole Malaysia but only focusing on fisheries department in Terengganu. The scope narrowed and conducted at Terengganu so that the data collected will be more accurate. Data was collected from 2010 to 2012 and selected monthly by using secondary data from Department Of Fisheries Malaysia official website
1. Model development2. Solving differential equation3. Calculate growth rate and P(t)
for both model.4. Fit the data into available
model using Excel software.5. Determine the model
constants and coefficient 6. Calculate the statistical
parameters and absolute error (abs E)
Malthusian growth model
Logistic growth model
Error =
Figure below shows that the population exponentially declines at any fixed negative value of k. Population size depends on the growth rate, with the population decline as P (t) becomes smaller.
Malthusian and logistic growth model was compared and found that Malthusian model is more preferable to make a prediction for fish harvesting yield. The graph line for Malthusian growth model is closed to the actual values that provide minimum error which is 1409.84 compared to the logistic growth model which is 1414.5. Solution obtained helps provide a guideline to fisheries management to optimizes values for the harvest yield and provide a solution to the people who doubting about the source of fish as a protein and fat.
ROHAYU BINTI MOHAMED2012434738
SUPERVISOR: ASSO PROF HJ AHMAD HJ AZIZ
Figure below shows that the comparison between actual and prediction value of fish harvesting.
• Aanes,S., Engen, S., Sather, B.E., Willebrand, T., Marcstro, V. (2002). Sustainable harvesting strategies of willow ptarmigan in a fluctuating environment. Ecological Applications, 12(1), pp. 281–290.
• Boutayeb, A., Lamlili, M., & Boutayeb, W. A competition and harvesting model applied to a fish population along the Moroccan cost
• Mohamed Faris Laham, Ishtrinayagy s. Krishnarajah & Jamilah Mohd Shariff.(2012). Fish Harvesting Management Strategies Using Logistic Growth Model. Malaysia 41(2)(2012)
84.140936
50754.12, ==Eabs
