Use of random forests and support vector machines to improve annual egg production estimation

Li, Zengguang; Wu, Rong; Ye, Zhenjiang; Chen, Yong; Ren, Yiping; Liu, Hong; Jiang, Yunshan

doi:10.1007/s12562-016-1033-5

Cited by 8 publications

(5 citation statements)

References 40 publications

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“…SST; Tiedemann, Kloppmann, Ulleweit, Gröger, & Hagen, ). The marked spatial heterogeneity and temporal variability in terms of LFAs help support a mechanistic understanding of the findings of our previous studies (Li et al., , ), which suggest that spring and summer are the main spawning seasons for most fish species in Haizhou Bay.…”

Section: Discussionsupporting

confidence: 79%

“…Our previous mesoscale study (Li et al, 2015) in Haizhou Bay revealed a stable geographic distinction based on a clear bathymetric separation of LFAs at 20 m. The coastal waters (<20 m) are considered an important spawning and nursery ground for many economically and ecologically important species in the area (e.g. Larimichthys polyactis, Liza haematocheilus and Konosirus punctatus), especially in spring and summer (Li et al, 2017), making it F I G U R E 1 Study area with the general circulation scheme along the west coast of the Yellow Sea during the field survey in 2013. The Yellow Sea Coastal Current (green arrow) water flows southward along the coast and extends southeastward into the East China Sea (Quan et al, 2013) during spring and summer an ideal system for studying small-scale spatiotemporal variability of LFAs.…”

Section: Haizhoumentioning

confidence: 97%

“…Our previous mesoscale study (Li et al., ) in Haizhou Bay revealed a stable geographic distinction based on a clear bathymetric separation of LFAs at 20 m. The coastal waters (<20 m) are considered an important spawning and nursery ground for many economically and ecologically important species in the area (e.g. Larimichthys polyactis , Liza haematocheilus and Konosirus punctatus ), especially in spring and summer (Li et al., ), making it an ideal system for studying small‐scale spatiotemporal variability of LFAs.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the relationship between spatial heterogeneity and temporal variability of larval fish assemblages in a coastal marine ecosystem (Haizhou Bay, China)

et al. 2017

Marine Ecology

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In coastal marine ecosystems, spatial patterns of larval fish assemblages (LFAs) tend to exhibit geographic stability because of relatively stable spawning site selection and predictable oceanographic phenomena such as eddies. To evaluate the relationship between spatial heterogeneity and temporal variability of LFAs, we conducted a high spatiotemporal resolution ichthyoplankton survey from April to July in 2013 in the shallow waters (<20 m) of Haizhou Bay, China. Our analysis indicated three distinct assemblages, which were stable geographically but exhibited a gradual and directional change of species composition and abundance over our study period. Sea surface temperature was the most important environmental co‐variate for determining temporal variability of LFAs, likely owing to temperature effects of species composition and spawning period selection of adult fish, along with known temperature‐dependent survival rates of larval fish. Study of LFA spatiotemporal dynamics is essential for improved understanding of adult fish spawning behavior, and has potential to inform design and implementation of conservation and management measures (e.g. marine protected areas) in coastal systems.

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Section: Discussionsupporting

confidence: 79%

Section: Haizhoumentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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Evaluating the relationship between spatial heterogeneity and temporal variability of larval fish assemblages in a coastal marine ecosystem (Haizhou Bay, China)

et al. 2017

Marine Ecology

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“…In addition, GAMs and TGAMs were compared, and the best model was also determined by minimizing the REML score. The Tweedie distribution was used for the GAMs and TGAMs models, considering that egg abundance was characterized by an inflation of zero values (Li et al, 2016). Analyses were conducted using the "mgcv" package in R (R Core Team, 2018).…”

Section: Discussionmentioning

confidence: 99%

Spawning overlap of Japanese anchovy Engraulis japonicus and Japanese Spanish mackerel Scomberomorus niphonius in the coastal Yellow Sea: A prey–predator interaction

Zhang

Tian

et al. 2022

Fisheries Oceanography

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Japanese Spanish mackerel Scomberomorus niphonius and Japanese anchovy Engraulis japonicus are typical predator and prey species, respectively, with high commercial values in the China seas. However, the interspecific interaction, particularly the relationship between the two species during early life stages, is unclear. Here, based on ichthyoplankton surveys in the coastal Yellow Sea during 2013–2018, we analyzed the relationship between the two species based on egg occurrence and abundance in the context of species interaction. Spawning optimal temperature were examined for anchovy (13.0–18.0°C) and mackerel (15.0–20.0°C) based on egg occurrences and abundances in relation to sea surface temperature. We confirmed the peak spawning periods (May and June) and primary spawning areas (Haizhou Bay) for both mackerel and anchovy based on spatiotemporal distributions of two species eggs. Mackerel showed a high spawning overlap with anchovy, and anchovy egg abundance were also higher in overlap areas. Anchovy egg abundance may be more important for mackerel spawning than environmental variables, suggesting the significance of prey for predators during early recruitment. The temperature also has important effects on mackerel spawning and could be a threshold affecting mackerel spawning activity. Mackerel might have evolved a spawning strategy of short‐term spawning timed to coincide with the peak spawning of anchovy in the coastal Yellow Sea. These results highlight the effects of interspecific relationships on spawning strategy of anchovy and mackerel in the coastal Yellow Sea and may provide some insights for early recruitment studies of piscivorous fishes in the context of the match–mismatch hypothesis.

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“…e application of artificial neural network for the macroeconomic early warning can get rid of the difficulties of traditional early warning models. It is easier to deal with complex algorithms, qualitative indicators, and quantitative indicators of highly nonlinear models [17]. In view of the early warning limits, coordination schemes, time-invariant characteristics, poor adaptive ability, slow self-learning, high consumption, and low efficiency of macroeconomic early warning, researchers have proposed a macroeconomic early warning system that optimizes the BP neural network with genetic algorithms.…”

Section: Macroeconomic Early Warning Methods Of Neuralmentioning

confidence: 99%

Macroeconomic Early Warning Method Based on Support Vector Machine under Multi-Sensor Data Fusion Technology

Tian

Shao

Yang

2022

Mobile Information Systems

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This article focuses on the macroeconomic early warning based on support vector machines under multi-sensor data fusion technology. The economic crisis has always been a topic of great concern to the entire world, and there has never been a lack of research and prevention of it in the development process. At present, the economic early warning system that is frequently mentioned includes statistical models and artificial intelligence models. Economic early warning is not just a topic for discussion on a national basis. In the personal field, a thorough enough understanding of economic early warning can also better control investment incomes such as stocks. Analyzing the direction of macroeconomic policies has also become an indispensable ability for investors. In order not to be affected by the economic crisis, early warning is a crucial link. Therefore, in this article, a multi-sensor data fusion macroeconomic early warning model based on support vectors is proposed. In addition, this article also discusses each subpart separately. First, this article mentions the multi-sensor data fusion model. It conducts reasonable collection and control of data information in multiple sensors and uses computers and smart devices to improve system performance and make data clearer. At the same time, this article shows the JDL model of data fusion. It can optimize signals and processes and targets and conduct situation assessment and effect assessment. Then, this article analyzes the support vector machine economic early warning system. It discusses the four parts of linear support vector machine, nonlinear support vector machine, SVC macroeconomic early warning principle, and support vector classification macroeconomic early warning system. By gradually analyzing the macroeconomic early warning process of support vector machines, this study uses the optimal hyperplane to optimize toward the direction of minimizing economic risks. Then, this article talked about the macroeconomic early warning method based on neural network. The difficult points of traditional early warning models can be discarded. It more easily handles the complex algorithms, qualitative indicators, and quantitative indicators of highly nonlinear models, and it demonstrates the macroeconomic early warning system that optimizes the BP neural network with genetic algorithms to solve the various shortcomings of the macroeconomic early warning process. Finally, this study conducts the multi-sensor data fusion macroeconomic early warning model experiment based on support vector machine. It is divided into three parts. In the first part, the model, design system, and application of 60 sets of sensors are compared with traditional weighted least-squares filtering, and it is concluded that the accuracy and trustworthiness prediction effect of this model is better. The second part uses the data of listed companies to conduct experiments, which verify the performance of the model with different data sets. It can be obtained that its prediction effect is better. The third part is to compare the performance with several traditional models, and it is concluded that the convergence effect is good and the error is small. Its average accuracy is 5.63% higher than the average accuracy of the highest precision warning model in the traditional model. This article discusses and concludes that this model has a good future in macroeconomic early warning.

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Use of random forests and support vector machines to improve annual egg production estimation

Cited by 8 publications

References 40 publications

Evaluating the relationship between spatial heterogeneity and temporal variability of larval fish assemblages in a coastal marine ecosystem (Haizhou Bay, China)

Evaluating the relationship between spatial heterogeneity and temporal variability of larval fish assemblages in a coastal marine ecosystem (Haizhou Bay, China)

Spawning overlap of Japanese anchovy Engraulis japonicus and Japanese Spanish mackerel Scomberomorus niphonius in the coastal Yellow Sea: A prey–predator interaction

Macroeconomic Early Warning Method Based on Support Vector Machine under Multi-Sensor Data Fusion Technology

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