Using a neural network approach and time series data from an international monitoring station in the Yellow Sea for modeling marine ecosystems
محفوظ في:
| الحاوية / القاعدة: | Environmental Monitoring and Assessment vol. 186, no. 1 (Jan 2014), p. 515 |
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| المؤلف الرئيسي: | |
| مؤلفون آخرون: | , , , , |
| منشور في: |
Springer Nature B.V.
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| الموضوعات: | |
| الوصول للمادة أونلاين: | Citation/Abstract Full Text Full Text - PDF |
| الوسوم: |
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| 100 | 1 | |a Zhang, Yingying | |
| 245 | 1 | |a Using a neural network approach and time series data from an international monitoring station in the Yellow Sea for modeling marine ecosystems | |
| 260 | |b Springer Nature B.V. |c Jan 2014 | ||
| 513 | |a Feature Journal Article | ||
| 520 | 3 | |a The international marine ecological safety monitoring demonstration station in the Yellow Sea was developed as a collaborative project between China and Russia. It is a nonprofit technical workstation designed as a facility for marine scientific research for public welfare. By undertaking long-term monitoring of the marine environment and automatic data collection, this station will provide valuable information for marine ecological protection and disaster prevention and reduction. The results of some initial research by scientists at the research station into predictive modeling of marine ecological environments and early warning are described in this paper. Marine ecological processes are influenced by many factors including hydrological and meteorological conditions, biological factors, and human activities. Consequently, it is very difficult to incorporate all these influences and their interactions in a deterministic or analysis model. A prediction model integrating a time series prediction approach with neural network nonlinear modeling is proposed for marine ecological parameters. The model explores the natural fluctuations in marine ecological parameters by learning from the latest observed data automatically, and then predicting future values of the parameter. The model is updated in a "rolling" fashion with new observed data from the monitoring station. Prediction experiments results showed that the neural network prediction model based on time series data is effective for marine ecological prediction and can be used for the development of early warning systems.[PUBLICATION ABSTRACT] The international marine ecological safety monitoring demonstration station in the Yellow Sea was developed as a collaborative project between China and Russia. It is a nonprofit technical workstation designed as a facility for marine scientific research for public welfare. By undertaking long-term monitoring of the marine environment and automatic data collection, this station will provide valuable information for marine ecological protection and disaster prevention and reduction. The results of some initial research by scientists at the research station into predictive modeling of marine ecological environments and early warning are described in this paper. Marine ecological processes are influenced by many factors including hydrological and meteorological conditions, biological factors, and human activities. Consequently, it is very difficult to incorporate all these influences and their interactions in a deterministic or analysis model. A prediction model integrating a time series prediction approach with neural network nonlinear modeling is proposed for marine ecological parameters. The model explores the natural fluctuations in marine ecological parameters by learning from the latest observed data automatically, and then predicting future values of the parameter. The model is updated in a "rolling" fashion with new observed data from the monitoring station. Prediction experiments results showed that the neural network prediction model based on time series data is effective for marine ecological prediction and can be used for the development of early warning systems. | |
| 650 | 2 | 2 | |a China |
| 650 | 2 | 2 | |a Ecosystem |
| 650 | 1 | 2 | |a Environmental Monitoring |x methods |
| 650 | 1 | 2 | |a Neural Networks (Computer) |
| 650 | 2 | 2 | |a Oceans & Seas |
| 650 | 2 | 2 | |a Time |
| 650 | 1 | 2 | |a Water Pollution |x statistics & numerical data |
| 651 | 4 | |a Yellow Sea | |
| 653 | |a Marine ecology | ||
| 653 | |a Time series | ||
| 653 | |a Neural networks | ||
| 653 | |a Analysis | ||
| 653 | |a Studies | ||
| 653 | |a Computer centers | ||
| 653 | |a Automation | ||
| 653 | |a Water quality | ||
| 653 | |a Work stations | ||
| 653 | |a Scientists | ||
| 653 | |a Collaboration | ||
| 653 | |a Information storage | ||
| 653 | |a Chemical oxygen demand | ||
| 653 | |a Emergency communications systems | ||
| 653 | |a Hydrology | ||
| 653 | |a Ecosystems | ||
| 653 | |a Climate change | ||
| 653 | |a Ecological research | ||
| 653 | |a Testing laboratories | ||
| 653 | |a Warning systems | ||
| 653 | |a Marine environment | ||
| 653 | |a Emergency preparedness | ||
| 653 | |a Marine ecosystems | ||
| 653 | |a Data collection | ||
| 653 | |a Prediction models | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Wang, Juncheng | |
| 700 | 1 | |a Vorontsov, A M | |
| 700 | 1 | |a Hou, Guangli | |
| 700 | 1 | |a Nikanorova, M N | |
| 700 | 1 | |a Wang, Hongliang | |
| 773 | 0 | |t Environmental Monitoring and Assessment |g vol. 186, no. 1 (Jan 2014), p. 515 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
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