Forecasting Fish Stock Recruitment and Planning Optimal Harvesting Strategies by Using Neural Network

Sun, Lin; Xiao, Hongjun; Li, Shouju; Yang, Dixiong

doi:10.4304/jcp.4.11.1075-1082

Cited by 13 publications

(10 citation statements)

References 20 publications

(20 reference statements)

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“…The fact that the ANN provides a better model was highlighted by better predictions for lower values, the normality of the residuals and their independence from the predicted variable. Several authors have reported greater performances of ANNs compared to linear regressions (Sun, 2009). The advantage of ANNs over multiple linear regression (MLR) models is that ANNs can directly take into account any non-linear relationships between the dependent variables and each independent variable (LEK et al, 1996;SUN et al, 2009).…”

Section: Discussionmentioning

confidence: 99%

“…Several authors have reported greater performances of ANNs compared to linear regressions (Sun, 2009). The advantage of ANNs over multiple linear regression (MLR) models is that ANNs can directly take into account any non-linear relationships between the dependent variables and each independent variable (LEK et al, 1996;SUN et al, 2009). ANNs have another advantage in that the ANN modeling ap- Medit.…”

Section: Discussionmentioning

confidence: 99%

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Application of Artificial Neural Networks (ANNs) for Weight Predictions of Blue Crabs (Callinectes sapidus RATHBUN, 1896) Using Predictor Variables

2011

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An evaluation of the performance of artificial networks (ANNs) to estimate the weights of blue crab (Callinectes sapidus) catches in Yumurtalık Cove (Iskenderun Bay) that uses measured predictor variables is presented, including carapace width (CW), sex (male, female and female with eggs), and sampling month. Blue crabs (n=410) were collected each month between 15 September 1996 and 15 May 1998. Sex, CW, and sampling month were used and specified in the input layer of the network. The weights of the blue crabs were utilized in the output layer of the network. A multi-layer perception architecture model was used and was calibrated with the Levenberg Marguardt (LM) algorithm. Finally, the values were determined by the ANN model using the actual data. The mean square error (MSE) was measured as 3.3, and the best results had a correlation coefficient (R) of 0.93. We compared the predictive capacity of the general linear model (GLM) versus the Artificial Neural Network model (ANN) for the estimation of the weights of blue crabs from independent field data. The results indicated the higher performance capacity of the ANN to predict weights compared to the GLM (R=0.97 vs. R=0.95, raw variable) when evaluated against independent field data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Application of Artificial Neural Networks (ANNs) for Weight Predictions of Blue Crabs (Callinectes sapidus RATHBUN, 1896) Using Predictor Variables

2011

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“…Several authors reported greater performances of ANNs compared to linear regressions (Sun et al, 2009). ANNs have another advantage in that the ANN modelling approach is fast and flexible (Brosse et al, 1999).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

EVALUATION OF GROWTH IN PIKE (Esox lucius L., 1758) USING TRADITIONAL METHODS AND ARTIFICIAL NEURAL NETWORKS

Benzer¹

2016

Appl Ecol Env Res

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Abstract. The present study was performed to assess the population structure and growth of pike in Mogan Lake using length-weight relationships, von Bertalanffy equations and artificial neural networks between February 2013 and March 2014. The age of Esox lucius caught in Mogan Lake ranged between I to VII years. The fork length of the fish ranged from 27.5 cm to 70.0 cm, and the body weight of the fish ranged from 200 g to 2820 g. The von Bertalanffy growth lengths were 130.30 for females, 122.70 for males and 126.50 for all individuals. The von Bertalanffy growth weights were 18889 for females, 14881 for males and 16688 for all pike. The results obtained using the artificial neural networks and regression technique were compared to those obtained using the growth rate of the fish caught from the natural environment and von Bertalanffy growth model. Artificial neural networks are an alternative to von Bertalanffy growth models.

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“…They can generalize well if over-fitting is avoided, in other words they can fit and classify data they have not been trained on with accuracy. They also do not require the transformation of non-linear variables, unlike classical linearization techniques, which can "improve the results but have often failed to fit the data" (Sun et al, 2009). All these features make the application of ANNs "a powerful approach for exploring complex biological problems such as recruitment forecasting" (Chen and Ware, 1999).…”

Section: Introductionmentioning

confidence: 99%

Assessing the Role of Environmental Factors on Baltic Cod Recruitment, a Complex Adaptive System Emergent Property

2016

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For decades, fish recruitment has been a subject of intensive research with stock-recruitment models commonly used for recruitment prediction often only explaining a small fraction of the inter-annual recruitment variation. The use of environmental information to improve our ability to predict recruitment, could contribute considerably to fisheries management. However, the problem remains difficult because the mechanisms behind such complex relationships are often poorly understood; this in turn, makes it difficult to determine the forecast estimation robustness, leading to the failure of some relationships when new data become available. The utility of machine learning algorithms such as artificial neural networks (ANNs) for solving complex problems has been demonstrated in aquatic studies and has led many researchers to advocate ANNs as an attractive, non-linear alternative to traditional statistical methods. The goal of this study is to design a Baltic cod recruitment model (FishANN) that can account for complex ecosystem interactions. To this end, we (1) build a quantitative model representation of the conceptual understanding of the complex ecosystem interactions driving Baltic cod recruitment dynamics, and (2) apply the model to strengthen the current capability to project future changes in Baltic cod recruitment. FishANN is demonstrated to bring multiple stressors together into one model framework and estimate the relative importance of these stressors while interpreting the complex non-linear interactions between them. Additional requirements to further improve the current study in the future are also proposed.

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Forecasting Fish Stock Recruitment and Planning Optimal Harvesting Strategies by Using Neural Network

Cited by 13 publications

References 20 publications

Application of Artificial Neural Networks (ANNs) for Weight Predictions of Blue Crabs (Callinectes sapidus RATHBUN, 1896) Using Predictor Variables

Application of Artificial Neural Networks (ANNs) for Weight Predictions of Blue Crabs (Callinectes sapidus RATHBUN, 1896) Using Predictor Variables

EVALUATION OF GROWTH IN PIKE (Esox lucius L., 1758) USING TRADITIONAL METHODS AND ARTIFICIAL NEURAL NETWORKS

Assessing the Role of Environmental Factors on Baltic Cod Recruitment, a Complex Adaptive System Emergent Property

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