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International Journal of Remote Sensing

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Detection of potential fishing zones for neonflying squid based on remote-sensing data in theNorthwest Pacific Ocean using an artificial neuralnetwork

Jintao Wang, Wei Yu, Xinjun Chen, Lin Lei & Yong Chen

To cite this article: Jintao Wang, Wei Yu, Xinjun Chen, Lin Lei & Yong Chen (2015) Detection ofpotential fishing zones for neon flying squid based on remote-sensing data in the NorthwestPacific Ocean using an artificial neural network, International Journal of Remote Sensing, 36:13,3317-3330, DOI: 10.1080/01431161.2015.1042121

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Detection of potential fishing zones for neon flying squid based onremote-sensing data in the Northwest Pacific Ocean using an artificial

neural network

Jintao Wanga,b,c,d, Wei Yua,b, Xinjun Chena,b,c,d*, Lin Leia,b,c,d, and Yong Chenb,e

aCollege of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China; bCollaborativeInnovation Center for Distant-water Fisheries, Shanghai 201306, China; cNational EngineeringResearch Centre for Oceanic Fisheries, Shanghai Ocean University, Shanghai 201306, China; dKeyLaboratory of Sustainable Exploitation of Oceanic Fisheries Resources, Ministry of Education,Shanghai Ocean University, Shanghai 201306, China; eSchool of Marine Sciences, University of

Maine, Orono, ME 04469, USA

(Received 20 September 2014; accepted 8 March 2015)

Ommastrephes bartramii is a short-lived species of squid and reacts rapidly to changesin the regional environmental conditions of the fishing ground. Understanding thepreferred range of key environmental variables and predicting potential resourcedistributions are critical to conserve and manage its resources. Commercial fisherydata for the western winter–spring cohort of O. bartramii from Chinese squid-jiggingvessels during 2003–2013 were used to evaluate a suitable range of three key environ-mental variables, sea surface temperature (SST), sea surface height (SSH), and chlor-ophyll-a (chl-a) concentration, and to explore potential fishing zones (PFZs) using anartificial neural network. The neural interpretation diagram and independent variablerelevance analysis indicate that month, latitude, and SST had significant influences onthe PFZ distribution of O. bartramii, yielding 21.78%, 23.91%, and 26.04% ofcontribution rates, respectively. Based on the sensitivity analyses, a high abundanceof O. bartramii mainly occurred in the waters between 150°–165° E and 37°–42° Nduring July to August. Suitable ranges of environmental variables for O. bartramiiwere 11–18°C for SST, −10 to 60 cm for SSH, and 0.1–1.7 mg/m3 for chl-a concen-tration, respectively. The back-propagation network model was well developed andcould be used to predict the PFZ with 80% accuracy. The actual fishing groundscoincided with the predicted PFZ, suggesting that the established model of PFZ iseffective in forecasting the potential habitat of O. bartramii in the Northwest PacificOcean.

1. Introduction

In the Northwest Pacific Ocean, the Kuroshio and Oyashio currents create a transitionalzone between the subtropical and subarctic boundaries (Roden 1991), yielding a highlyproductive habitat for various economically important species such as the Pacific saury(Cololabis saira), anchovy (Engraulis japonicus), albacore (Thunnus alalunga), Japanesecommon squid (Todarodes pacificus), and neon flying squid (Ommastrephes bartramii)(Zainuddin et al. 2006; Chen, Tian, and Guan 2014). This region provides one of the mostcomplex physical oceanographic structures in the world, with fluctuating meanderingeddies, fronts, and streamers, as well as variable biological environmental conditions(Sassa, Moser, and Kawaguchi 2002). Physical and biological environments in the

*Corresponding author. Email: xjchen@shou.edu.cn

International Journal of Remote Sensing, 2015Vol. 36, No. 13, 3317–3330, http://dx.doi.org/10.1080/01431161.2015.1042121

© 2015 Taylor & Francis

Kuroshio–Oyashio transitional area dominate the climate and ecosystem in the westernNorth Pacific Ocean, which also greatly influences fish stock abundance and distribution(Yatsu et al. 2013).

O. bartramii is a short-lived species of squid (Yatsu et al. 1997) and has beencommercially exploited by Japan since 1974 and later by South Korea and China, includingthe Taiwan Province (Wang and Chen 2005). This squid undertakes seasonal migrationfrom the subtropical Kuroshio Current in winter to the Subarctic Oyashio Current insummer (Gong, Kim, and An 1991; Seki 1993; Murata and Nakamura 1998). An autumncohort and a winter–spring cohort for O. bartramii have been inferred in the North Pacificbased on the analyses of mantle length distribution and rates of infection by helminthicparasites (Bower and Ichii 2005). During the main fishing seasons during August toNovember, Chinese squid-jigging fleets mostly target the western winter–spring cohort ofO. bartramii in the traditional fishing ground between 39°–45° N and 150°–165° E,accounting for a significant portion of total catches in the Northwest Pacific (Chen et al.2008a).

As an ecological opportunist, O. bartramii tends to be highly susceptible to environ-mental changes in the spawning and feeding grounds in the western North Pacific (Yatsuet al. 2000; Anderson and Rodhouse 2001). Spatial distributions of squid abundance aretypically related to oceanographic conditions, such as sea surface temperature (SST), seasurface height (SSH), and chlorophyll-a (chl-a) concentration, that can be remotelymonitored using satellites (Chen, Cao, et al. 2010; Chen et al. 2011). Previous studieshave employed various approaches to evaluate the relationship between the environmentalvariables and the abundance distribution of O. bartramii. For example, the surface watertemperature in the spawning and fishing grounds plays an important role in regulatingpopulation dynamics and spatial distribution of O. bartramii, especially under abnormalclimatic events such as the El Niño event (Chen, Zhao, and Chen 2007; Cao, Chen, andChen 2009; Yi and Chen 2012). A positive relationship was identified between the catchper unit effort (CPUE) of the winter–spring cohort of O. bartramii and food availabilityfeatured by chl-a (Nishikawa et al. 2014). The inter-annual variability in the CPUE of O.bartramii could be explained by the fluctuating feeding environments of the spawningground, transitional region, and fishing grounds, all of which had significant impacts ondifferent life-history stages of O. bartramii, including paralarvae, juveniles, and adults(Ichii et al. 2009; Wang et al. 2010; Nishikawa et al. 2014). Furthermore, the SSH wasconsidered to be a crucial marine environmental factor in exploring the potential fishingzones (PFZs), used in the habitat modelling of O. bartramii (Chen, Tian, et al. 2010).

Many methods have been developed to predict PFZ distributions such as the habitatsuitability index (HSI) model, generalized linear model (GLM), and generalized additivemodel (GAM). Chen et al. (2009) established an integrated HSI model for the winter–spring cohort of O. bartramii based on the SST and SST with a horizontal gradient, andfound that the arithmetic mean model could accurately forecast squid fishing grounds inthe Northwest Pacific. Tian et al. (2009) used the GAM method to evaluate the non-linearrelationship between the CPUE of O. bartramii and the environmental variables on thefishing ground and concluded that the spatial pattern of squid abundance could be wellpredicted. General statistical models including the linear model, piecewise-linear model,polynomial regression, exponential regression, and quantile regression have been com-monly used in the analysis of the relationships between fishing ground distribution andenvironmental conditions (Chen et al. 2013). However, the prediction of fishing groundfor a short-lived species such as O. bartramii tends to be more difficult due to unknownmechanisms in the interactions with complex biophysical environments based on these

3318 J. Wang et al.

conventional techniques. In fact, the mechanism of forming a fishing ground is complexand the dynamic interactions between fish distribution and environments tend to be non-linear. With the development of machine learning and artificial intelligence, novel meth-ods, such as expert systems, genetic algorithms, and fuzzy reasoning, were developed andhave been increasingly used to explore PFZ (Chen et al. 2013). An artificial neuralnetwork with functions of self-learning, strong generalizations, and fault tolerance pro-vides an approach to evaluate complex non-linear relationships (Hush and Horne 1993;Moody and Antsaklis 1996). The artificial neural network models can be established withfew assumptions on fishery and environmental data and, thus, differs from conventionalstatistical models and also tends to be more suitable for forecasting fishing grounds(Suryanarayana et al. 2008). Artificial neural networks have been used for predictingfish distribution (Suryanarayana et al. 2008), such as distributions of capelin (Mallotusvillosus) in Barents Sea (Huse 2001), the European eel (Anguilla Anguilla) (Laffaille et al.2003, 2004), and the Eurasian perch (Perca fluviatilis) (Brosse and Lek 2002).

This study aims to improve the accuracy of predicting PFZ for O. bartramii based onremote-sensing data using an artificial neural network. We attempt to construct anartificial neural network model to detect PFZ for the western winter–spring cohort of O.bartramii using remote-sensing environmental data and fishery data from the Chinesesquid-jigging fleets in the Northwest Pacific. A neural interpretation diagram, independentvariable relevance, and sensitivity analysis are used to evaluate the weights of the modelvariables and examine the influences of environmental factors on PFZ distribution. Asimilar approach is also applicable to other species with a similar life history.

2. Materials and methods

2.1. Fishery data

Daily O. bartramii fishery data were obtained from the Chinese Squid-Jigging TechnologyGroup of Shanghai Ocean University from July to December during 2003–2013. The datawere digitized from fishing logs of the Chinese commercial squid fishery operating on thetraditional fishing ground between 35°–45° N and 145°–165° E in the Northwest Pacific.Data comprised fishing dates (year and month), fishing locations (latitude and longitude),daily catch (tonnes), and effort (days fished).

Most of the catches were from the western stocks of the winter–spring cohort ofO. bartramii and there was no bycatch in the squid fishery (Chen, Chen, et al. 2008).Fishing vessels and their operations were almost identical. Therefore, the CPUE tends tobe a reliable indicator of local stock abundance (Chen, Chen, et al. 2008). In this study, wedefined one unit of fishing area as 0.5° latitude by 0.5° longitude. The monthly nominalCPUE in one fishing unit of 0.5° × 0.5° was calculated as follows:

CPUEymi ¼ CymiFymi ; (1)

where CPUEymi, Cymi, and Fymi are the monthly nominal CPUE, the total catch for all thefishing vessels within a fishing grid, and the number of fishing vessels within one fishinggrid, respectively, at i fishing unit in month m and year y. Based on the range of observedO. bartramii CPUE data and our knowledge of the fishery, five classification levels weredefined for the quality of fishing grounds (Table 1).

International Journal of Remote Sensing 3319

2.2. Remotely sensed environmental data

Monthly remotely sensed data, comprising SST, SSH, and chl-a concentration, for thefishing ground between 35°–46° N and 145°–165° E were obtained from the Live AccessServer of the National Oceanic and Atmospheric Administration Ocean Watch from 2003to 2013 (http://oceanwatch.pifsc.noaa.gov/las/servlets/dataset). The spatial resolutionswere 0.1° × 0.1°, 0.25° × 0.25°, and 0.05° × 0.05° for the SST, SSH, and chl-a,respectively. All environmental data were converted into a 0.5° × 0.5° grid for eachmonth to correspond to the spatiotemporal resolution of the fishery data. For example,SSTs in a grid with the spatial resolution of 0.5° × 0.5° were averaged by 25 point valueswithin the original resolution of 0.1° × 0.1°. The location (longitude and latitude) and time(year and month) were used as indicator variables for gridding fishery data and convertingenvironmental data for the same spatiotemporal scales using a computer program devel-oped in Java.

2.3. Establishment of a forecasting model based on the artificial neural network

The artificial neural network model was constructed using fishery and remote-sensingsatellite data. In this model, a feed-forward artificial neural network with back-propa-gation algorithm, known as a multilayer perceptron, was used to train the data topredict fishing ground levels. The feed-forward neural network contained intercon-nected units (also called nodes or perceptrons) which were arranged in layers(Figure 1). The neural network model was created, trained, and run usingthe Artificial Neural Network Toolbox (https://www.mathworks.com/help/pdf_doc/nnet/nnet_ug.pdf).

The first layer consisted of inputs or independent variables which included the month,latitude, longitude, SST, SSH, and chl-a. The final layer was the output layer and had onevariable, which was the PFZ level (0–4). The layers between the input and output layerswere defined as hidden layers. We used one hidden layer with six hidden nodes in thismodel (Figure 1). The input variables at each node were normalized from −1 to 1 beforeinclusion in the model, and then were summed. The symmetric logistic and linearfunctions were used as the activation function in the hidden nodes and the output unit(Beale et al. 2010), respectively.

In total, there were 6938 data samples from 2003 to 2012, 70% of which wererandomly assigned to train, whereas the remaining 30% were used to cross-validate themodel. The maximum error and maximum number of iterations for stopping the iterationprocess were set to 0.001 and 1000, respectively. Other parameters such as the learningrate and momentum were set to default (Beale et al. 2010). Additionally, the 899 datasamples in 2013 were used to examine the accuracy of model forecasting. Accuracy was

Table 1. Classification levels of fishing grounds.

Code CPUE range (t day–1) Fishing grounds levels

0 6 Excellent

3320 J. Wang et al.

http://oceanwatch.pifsc.noaa.gov/las/servlets/datasethttps://www.mathworks.com/help/pdf_doc/nnet/nnet_ug.pdfhttps://www.mathworks.com/help/pdf_doc/nnet/nnet_ug.pdf

calculated by the number of correct forecasting CPUE levels divided by the number of alldata samples.

Some ecologists have used sensitivity analysis to quantify the contribution of eachindependent variable (Lek et al. 1996; Scardi 1996; Recknagel et al. 1997) to avoidtreating neural networks as a black box and to understand the relative importance ofindependent variables. Given a sufficient understanding of the fishing ground forma-tion mechanism of this squid, we found that it was possible to interpret the results of aneural network PFZ model. After the selection of the final model, we examined theinput variable’s relevance and conducted sensitivity analysis for each variable todetermine how a favourable PFZ chosen by the squid might vary with differentvariables.

Relevance analysis is a method to compare the contribution of variables to the PFZ.The relevance of each input variable was simply the sum square of weights for that inputvariable divided by the sum square of weights for all input variables (Özesmi and Özesmi1999).

The sensitivity analyses were conducted by the following procedures: (1) calculate themean, median, minimum, and maximum values of each variable; (2) set all inputindependent variables except one to each of these values in turn; and (3) change valuesof the independent variable left out in Step (2) from its minimum to maximum values andplot the fishing ground level. In this study, we chose the median value to represent ageneral view because maximum data samples of median value of each variable wereavailable (Özesmi and Özesmi 1999).

2.4. Forecasting fishing ground levels overlapped with the remote-sensing images

The PFZ of O. bartramii were characterized by meandering eddies and frontal zones(Chen 1995, 1997) that could be visualized from the SST and OceanColor images. Thepredicted fishing ground levels were then overlapped with the remote-sensing images tomore accurately detect PFZ.

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International Journal of Remote Sensing 3321

3. Results

3.1. Performances of the artificial neural network forecasting model

The artificial neural network forecasting model was trained based on sampling data. Theweights of models changed, multiplied by momentum from the input to output layers inone iteration, and the best trained model was selected with the least mean squared error(MSE) after 35 model iterations. The MSEs of training, validating, and testing were 0.75,0.79, and 0.62, respectively (Table 2). In addition, the model was tested by the fisherydata in 2013, and its forecasting accuracy was approximately 80% (Table 2).

3.2. The use of an artificial neural network prediction model for interpreting PFZ

The predicted neural interpretation diagrams showed non-linear relationships between theinput variables (month, latitude, longitude, SST, SSH, and chl-a) and classification levelsof fishing grounds (Figure 1). The weights of each unit in the input variables and hiddenlayers had positive and negative impacts synchronously on the fishing ground level. Themonth, latitude, and SST exhibited more significant influences on the classification levelsof PFZ compared to longitude, SSH, and chl-a (Figure 1).

The relevance of the six variables in the forecasting model was listed in Table 3. Themonth, latitude, and SST were the most important variables in the forecasting model,constituting 21.78%, 23.91%, and 26.04% of the variance, respectively.

Table 2. Performance of the back-propagation artificial neural networkmodel in the prediction of fishing ground classification levels.

Measures Result

Training sample 4857Validating sample 2081Testing sample 899Training MSE 0.75Validating MSE 0.79Testing MSE 0.62Percentage of correct prediction of fishing groundclassification level using a training sample

3739/4857 = 0.77

Percentage of correct prediction of fishing groundlevel using a validating sample

1560/2081 = 0.75

Percentage of correct prediction of fishing groundlevel using a testing sample

720/899 = 0.8

Table 3. Variable relevance of prediction modelusing an artificial neural network.

Variable Contribution (%)

Month 21.78Longitude 11.91Latitude 23.91SST 26.04SSH 5.54Chl-a 10.79

3322 J. Wang et al.

According to the sensitivity analysis, significant spatiotemporal changes were shownin the PFZ. High levels of PFZ of O. bartramii mainly occurred in August, September,and October (Figure 2(a)). The areas between 150°–165° E and 37°–42° N yielded themost productive squid abundance (Figures 2(b) and (c)). The preferred ranges of environ-mental variables for O. bartramii were 11–18°C for SST (Figure 2(d)), −15 to 60 cm forSSH (Figure 2(e)), and 0.1–1.7 mg/m3 for chl-a (Figure 2(f)), respectively. However, chl-ayielded limited influences on the PFZ, based on the sensitivity analysis (Figure 2(f)).

3.3. Forecasting PFZ during July to November in 2013

The established artificial neural network model was used to forecast the monthly PFZduring July to November in 2013 (Figure 3). In July, the levels of PFZ were

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Figure 2. Sensitivity analyses for the six variables. In each panel of the figure, all variables wereset to their median values except for the variable being evaluated. (a) Sensitivity analyses for thevariable ‘month’; (b) sensitivity analyses for the variable ‘longitude’; (c) Sensitivity analyses forthe variable ‘latitude’; (d) Sensitivity analyses for the variable ‘SST’; (e) Sensitivity analyses forthe variable ‘SSH’, and (f) Sensitivity analyses for the variable ‘chl-a’.

International Journal of Remote Sensing 3323

Figure 3. The actual and forecasting fishing ground classification levels using satellite remote-sensing data from July to November in 2013. The left column represents actual fishing locations andits fishing ground classification level. The right column represents the forecasted fishing groundclassification levels overlaid on chl-a images.

3324 J. Wang et al.

predominantly less than level 2. A few high levels of PFZ were located in the waters of152°–153° E and 39°–41° N as well as 162°–164° E and 39°–40° N, in which the averageCPUE was 1.5–2.0 t day–1. In August, PFZ with level 2 or 3 classification accounted formore than 50%, and the high PFZ with a level 3 classification was mainly concentrated inthe waters of 151°–153° E and 40°–43° N near the front of the 20°C isotherm, and theaverage CPUE reached 3.0–4.0 t day–1. In September, PFZs with levels 2–4 classificationyielded more than 60% of the total fishing ground. High PFZ with levels 3 and 4classification were mainly concentrated in the waters of 150°–153° E and around 43° Nand 157°–158° E and 41°–43° N near the front of the 20°C isotherm, and the averageCPUE reached 3.0–6.0 t day–1. In October, the classification levels 2 and 3 of PFZ fell to30% of the total fishing ground. High PFZs of level 2 or 3 classification were mainlydistributed in the regions between 154°–159° E and 41°–44° N near the front of the 15°Cisotherm. In November, the levels of PFZ (2–3) increased to 51% of the total catch,whereas PFZs of levels 2 or 3 classification were mainly concentrated in the waters of155°–159° E and 40°–42° N as well as 148°–150° E and 40°–44° N near the front of the15°C isotherm (Figure 3).

4. Discussion

The impacts of environmental variations on fish abundance and fishing ground distribu-tion are well recognized (Chen 2004). Understanding how fish species react to climatechange and variations in the regional/local environments and predicting the dynamics ofthe fish population are essential for the effective management of marine resources(Botsford, Castilla, and Peterson 1997; McCann, Botsford, and Hasting 2003). This articlepresents a neural network approach for evaluating the sensitivity of western stocks of thewinter–spring cohort of O. bartramii to spatiotemporal changes in environmental factorsand for predicting PFZ of O. bartramii in the Northwest Pacific Ocean. Artificial neuralnetworks are considered ‘black box’ models with the recognition of their ability foreffective prediction (Paruelo and Tomasel 1997) although it is difficult to obtain a goodunderstanding of the underlying mechanisms in the models (Anderson 1995; Brey, Jarre-Teichmann, and Borlich 1996). Some methods have been developed to understand howthe ‘black box’ works, including a neural interpretation diagram, independent variablerelevance, and sensitivity analyses (Lek et al. 1996; Scardi 1996; Recknagel et al. 1997;Özesmi and Özesmi 1999). In this study, the neural network structure suggests that theprocess for exploring PFZ was complicated. The relationship between the independentvariables and classification levels of fishing grounds was non-linear. The BP forecastingmodel not only predicted PFZ accurately, but also provided a critical evaluation ofsuitable ranges of environmental variables for O. bartramii through neural interpretationdiagrams, relevance, and sensitivity analyses. Based on these analyses, PFZs, combinedwith the surrounding oceanographic features, were further analysed by overlappingremote-sensing images on the fishing ground. For example, in September, the fishingground between 42°–44° N and150°–160° E had relatively higher levels of PFZ predictedby the model; this area on the remote-sensing image also had high chl-a concentration andlow SST (but a higher SST gradient) (Figure 3).

Chen and Chiu (1999) suggested that the distribution and abundance of O. bartramiiwere strongly affected by environmental conditions such as SST. Chen, Zhao, and Chen(2007) found that El Niño/La Niña events had a significant effect on the spatial distribu-tion of fishing grounds. When the feeding area was affected by a La Niña event, the SSTgenerally increased, the subarctic front moved north, and the high-yield fishing grounds

International Journal of Remote Sensing 3325

were located farther north; if the feeding grounds were influenced by an El Niño event,the SST generally decreased, the subarctic front moved south, and the fishing groundsmoved southward and were also more aggregated. This shift in the distribution of fishingground for O. bartramii was closely related to SST. In this article, according to neuralinterpretation diagrams and the relevances (Figure 1; Table 3), we found that the SST wasthe most important environmental factor in the formation of fishing grounds and it had thegreatest influence on the prediction model, suggesting that SST could be used as anindicator to explore PFZ. The favourable range of SST for O. bartramii was 11–18°C.These results are generally consistent with those reported by previous studies (Chen 1997;Chen and Liu 2006). Many studies showed that optimal SSTs for squid varied with fishingmonths and areas; it appeared that the optimal SST gradually decreased from west to east(Chen, Liu, and Chen 2008). In the waters of 140°–150° E, the monthly optimal SSTswere 17–19°C, 18–22°C, 17–19°C, 13–18°C, and 10–14°C, respectively, during July toNovember (Chen 1995, 1997; Shen, Fan, and Cui 2004; Chen and Tian 2005). In thewaters of 150°–165° E, the monthly optimum SSTs were 12–14°C, 14–17°C, 15–19°C,14–18°C, 10–13°C, and 12–15°C, respectively, for the months of June to November(Chen 1997, 1999; Wang et al. 2003; Shen, Fan, and Cui 2004; Chen and Tian 2005;Chen and Liu 2006).

Other environmental variables (chl-a and SSH) also significantly affected the distribu-tion of O. bartramii. The chl-a concentration explained 10.79% of the total variance,whereas the SSH had the least impact – explaining only 5.54% of the total variance. Theexistence of plankton is a basic condition for the formation of squid fishing grounds(Chen 2004). The chl-a concentration is a good indicator of the food availability for squid.High chl-a concentrations yield good feeding environments, providing higher volumes ofnutrients for the phytoplankton and zooplankton as well as dissolved organic materialsthat are associated with food density and availability for the O. bartramii (Nishikawa et al.2014). Wang et al. (2003) reported that a skewed distribution function could be used todescribe the relationship between chl-a concentration and the squid catch in the waters of150°–165° E and 41°–45° N during August and October. The area with chl-a concentra-tions ranging from 0.15 to 3 mg/m3 produced 95% of the total catch. In the waters of 152°E–171° W and 39°–42° N during June and July, Xu, Cui, and Huang (2004) suggestedthat squid tended to aggregate near areas with the highest abundance (50–100 ind m–3) ofcrustaceans (mainly for Copepoda and Thaliacea).

The SSH field is often coupled with the dynamics (currents) and thermodynamics(heat balance) of the upper ocean. Convergences and divergences of mass transport in thesurface layer of the ocean result in positive and negative sea levels, respectively (Polito,Sato, and Liu 2000). This suggests that a satellite altimetry SSH map may be effective forpredicting a water mass front, which is a potential aggregating mechanism for planktonsas well as their predators, such as squid (Chen et al. 2011).

Levels of PFZ were high during August to October and low in July and November;this result coincided with the findings reported by Chen and Tian (2006). Thus, O.bartramii undertakes seasonal north–south migration. They tended to locate in the south-ern waters of the transitional zones in July, and then migrate into the traditional fishinggrounds during August to October, leading to high abundance during the main fishingseason. In November, mature squids begin to spawn and shifted southward, whenabundance would decline again.

In summary, the BP forecasting model on PFZ was well developed with approxi-mately 80% accuracy. The actual fishing grounds during July to November in 2013 wereconsistent with the predicted PFZ. These findings suggested that the rationality and

3326 J. Wang et al.

validity of employing the artificial neural network in this study are acceptable. Theestablished model of PFZ can be used for forecasting the potential habitat of O. bartramiiin the Northwest Pacific Ocean. Furthermore, it is important that the development of theneural network model is based on an in-depth understanding of the oceanography andhabitat of target fish species.

AcknowledgementsThe authors thank the Chinese Squid-Jigging Technology Group of Shanghai Ocean University forproviding the catch data and the National Oceanic and Atmospheric Administration for providingthe environmental data.

Disclosure statementNo potential conflict of interest was reported by the authors.

FundingThis study was financially supported by the National High-Tech R&D Programme (863 Programme)of China under grant number 2012AA092303, the National Key Technologies R&D Programme ofChina under grant number 2013BAD13B00, and the Shanghai Universities First-Class DisciplinesProject (Fisheries A). Y. Chen was supported by SHOU International Center for Marine Studies andthe Shanghai 1000 Talent Programme.

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Abstract1. Introduction2. Materials and methods2.1. Fishery data2.2. Remotely sensed environmental data2.3. Establishment of a forecasting model based on the artificial neural network2.4. Forecasting fishing ground levels overlapped with the remote-sensing images

3. Results3.1. Performances of the artificial neural network forecasting model3.2. The use of an artificial neural network prediction model for interpreting PFZ3.3. Forecasting PFZ during July to November in 2013

4. DiscussionAcknowledgementsDisclosure statementFundingReferences