Practical uses of non-parametric methods in fisheries assessment modelling

2012 ◽  
Vol 63 (7) ◽  
pp. 606 ◽  
Author(s):  
R. M. Hillary
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stock Assessment ◽  
Maximum Sustainable Yield ◽  
Sustainable Yield ◽  
Data Sets ◽  
Scientific Advice ◽  
Parametric Methods ◽  
Stock Status ◽  
Statistical Framework

The vast majority of fisheries stock assessment modelling is parametric, where specific models are assumed and fitted to data, the results of which are used to assess stock status and provide scientific advice. Often, the assumed models may not acceptably explain the data, or the data are not informative enough to estimate the parameters of even the most simple models. Using a fully inferential statistical framework, artificial neural networks were fitted to example data sets (stock-recruit, catch and relative abundance) and key assessment quantities such as maximum sustainable yield and relative biomass depletion were estimated. The combination of flexibility and statistical rigor suggests there is an as yet under-utilised role for such approaches in stock assessment, and not just in data-poor scenarios.

scholarly journals Simulation of flood flow in a river system using artificial neural networks

2005 ◽  
Vol 9 (4) ◽  
pp. 313-321 ◽  
Author(s):  
R. R. Shrestha ◽  
S. Theobald ◽  
F. Nestmann
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Flow Simulation ◽  
Simulation Models ◽  
River System ◽  
Training Data ◽  
Data Sets ◽  
Network Training ◽  
Artificial Neural ◽  
Flood Flow

Abstract. Artificial neural networks (ANNs) provide a quick and flexible means of developing flood flow simulation models. An important criterion for the wider applicability of the ANNs is the ability to generalise the events outside the range of training data sets. With respect to flood flow simulation, the ability to extrapolate beyond the range of calibrated data sets is of crucial importance. This study explores methods for improving generalisation of the ANNs using three different flood events data sets from the Neckar River in Germany. An ANN-based model is formulated to simulate flows at certain locations in the river reach, based on the flows at upstream locations. Network training data sets consist of time series of flows from observation stations. Simulated flows from a one-dimensional hydrodynamic numerical model are integrated for network training and validation, at a river section where no measurements are available. Network structures with different activation functions are considered for improving generalisation. The training algorithm involved backpropagation with the Levenberg-Marquardt approximation. The ability of the trained networks to extrapolate is assessed using flow data beyond the range of the training data sets. The results of this study indicate that the ANN in a suitable configuration can extend forecasting capability to a certain extent beyond the range of calibrated data sets.

scholarly journals Artificial Neural Networks Application in Modal Analysis of Tires

2013 ◽  
Vol 13 (5) ◽  
pp. 273-278 ◽  
Author(s):  
P. Koštial ◽  
Z. Jančíková ◽  
D. Bakošová ◽  
J. Valíček ◽  
M. Harničárová ◽  
...  
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Modal Analysis ◽  
Speckle Pattern ◽  
Point Of View ◽  
Electronic Speckle Pattern Interferometry ◽  
Data Sets ◽  
Artificial Neural

Abstract The paper deals with the application of artificial neural networks (ANN) to tires’ own frequency (OF) prediction depending on a tire construction. Experimental data of OF were obtained by electronic speckle pattern interferometry (ESPI). A very good conformity of both experimental and predicted data sets is presented here. The presented ANN method applied to ESPI experimental data can effectively help designers to optimize dimensions of tires from the point of view of their noise.

scholarly journals Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

2017 ◽  
Vol 43 (4) ◽  
pp. 26-32 ◽  
Author(s):  
Sinan Mehmet Turp
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Data Sets ◽  
Adsorption Efficiency ◽  
Back Propagation Algorithm ◽  
Artificial Neural

AbstractThis study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.

scholarly journals Delay in fishery management: diminished yield, longer rebuilding, and increased probability of stock collapse1

2006 ◽  
Vol 64 (1) ◽  
pp. 149-159 ◽  
Author(s):  
Kyle W. Shertzer ◽  
Michael H. Prager
Keyword(s):  
Fishery Management ◽  
Economic Loss ◽  
Maximum Sustainable Yield ◽  
Sustainable Yield ◽  
Short Term ◽  
Stock Status ◽  
Stock Biomass ◽  
Spawning Stock ◽  
Stock Recruitment ◽  
Stock Collapse

Abstract Shertzer, K. W., and Prager, M. H. 2007. Delay in fishery management: diminished yield, longer rebuilding, and increased probability of stock collapse. ICES Journal of Marine Science, 64: 149–159. When a stock is depleted, catch reductions are in order, but typically they are implemented only after considerable delay. Delay occurs because fishery management is political, and stricter management, which involves short-term economic loss, is unpopular. Informed of stock decline, managers often hesitate, perhaps pondering the uncertainty of scientific advice, perhaps hoping that a good year class will render action moot. However, management delay itself can have significant costs, when it exacerbates stock decline. To examine the biological consequences of delay, we simulated a spectrum of fisheries under various degrees of delay in management. Increased delay required larger catch reductions, for more years, to recover benchmark stock status (here, spawning-stock biomass at maximum sustainable yield). Management delay caused stock collapse most often under two conditions: (1) when the stock–recruitment relationship was depensatory, or (2) when catchability, unknown to the assessment, was density-dependent and fishing took juveniles. In contrast, prompt management resulted in quicker recoveries and higher cumulative yields from simulated fisheries. Benefits to stock biomass and fishery yield can be high from implementing management promptly.

Apply Artificial Neural Networks to Estimate Stress Distribution around Holes Edge in Composites

2012 ◽  
Vol 490-495 ◽  
pp. 3105-3108
Author(s):  
Kamran Pazand ◽  
Younes Alizadeh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Stress Distribution ◽  
Composite Laminates ◽  
Composite Plate ◽  
Input Data ◽  
Data Sets ◽  
Fast Determination ◽  
Artificial Neural

The purpose of this paper is to estimate the fast determination of stress distribution around a circular hole in symmetric composite laminates under in-plane loading. For this purpose calculation of stress values in the composite plate around edge holes in different plies position for a finite number of input data sets using the Lekhnitskii expressions and code program. The resulting data would then be used to train artificial neural networks (ANN) which would be able to predict –accurately enough- those quantities throughout the composite plate body for any given input value in any position ply and fore and stress that impose.

scholarly journals Characterization, Communication, and Management of Uncertainty in Tuna Fisheries

2020 ◽  
Vol 12 (19) ◽  
pp. 8245
Author(s):  
Gorka Merino ◽  
Hilario Murua ◽  
Josu Santiago ◽  
Haritz Arrizabalaga ◽  
Victor Restrepo
Keyword(s):  
Decision Rule ◽  
Fisheries Management ◽  
Stock Assessment ◽  
Maximum Sustainable Yield ◽  
Natural Systems ◽  
Stock Status ◽  
Management Procedures ◽  
Tuna Fisheries ◽  
Sustainability Challenges ◽  
Management Recommendations

Tunas sustain important fisheries that face sustainability challenges worldwide, including the uncertainty inherent to natural systems. The Kobe process aims at harmonizing the scientific advice and management recommendations in tuna regional fisheries management organizations (RFMOs) toward supporting the sustainable exploitation of tunas globally. In this context, we review the similarities and differences among tuna RFMOs, focusing on stock assessment methodologies, use of information, characterization of uncertainty and communication of advice. Also, under the Kobe process, tuna RFMOs have committed to a path of adopting harvest strategies (HSs), also known as management procedures (MPs), which are the series of actions undertaken to monitor the stock, make management decisions, and implement the management measures. The adoption of HSs for tuna stocks is supported by Management Strategy Evaluation (MSE), which is considered the most appropriate way to assess the consequences of uncertainty for achieving fisheries management goals. Overall, notable progress has been made in achieving some of the Kobe objectives, but there are still some aspects that are inconsistent and need to be agreed upon, due to their management implications. First, not all RFMOs report on stock status based on maximum sustainable yield (MSY) as a reference. Instead, some use depletion level to represent the available fish biomass. Also, the definition of overexploited is not common in all oceans. Finally, very few stock assessments characterize all major sources of uncertainty inherent to fisheries. With regards to HSs, two different approaches are being followed: One is designed to adopt an automatic decision rule once the stock status and management quantities have been agreed upon (harvest control rules (HCRs), not strictly an HS) and the other aims at adopting all the components of HSs (data, use of information and decision rule).

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