...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Environmental research >Explore the relationship between fish community and environmental factors by machine learning techniques
【24h】

Explore the relationship between fish community and environmental factors by machine learning techniques

机译：机器学习技术探索鱼群社区与环境因素之间的关系

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In the face of multiple habitat alterations originating from both natural and anthropogenic factors, the fast-changing environments pose significant challenges for maintaining ecosystem integrity. Machine learning is a powerful tool for modeling complex non-linear systems through exploratory data analysis. This study aims at exploring a machine learning-based approach to relate environmental factors with fish community for achieving sustainable riverine ecosystem management. A large number of datasets upon a wide variety of eco-environ-mental variables including river flow, water quality, and species composition were collected at various monitoring stations along the Xindian River of Taiwan during 2005 and 2012. Then the complicated relationship and scientific essences of these heterogonous datasets are extracted using machine learning techniques to have a more holistic consideration in searching a guiding reference useful for maintaining river-ecosystem integrity. We evaluate and select critical environmental variables by the analysis of variance (ANOVA) and the Gamma test (GT), and then we apply the adaptive network-based fuzzy inference system (ANFIS) for an estimation of fish bio-diversity using the Shannon Index (SI). The results show that the correlation between model estimation and the biodiversity index is higher than 0.75. The GT results demonstrate that biochemical oxygen demand (BOD), water temperature, total phosphorus (TP), and nitrate-nitrogen (NO_3-N) are important variables for biodiversity modeling. The ANFIS results further indicate lower BOD, higher TP, and larger habitat (flow regimes) would generally provide a more suitable environment for the survival of fish species. The proposed methodology not only possesses a robust estimation capacity but also can explore the impacts of environmental variables on fish biodiversity. This study also demonstrates that machine learning is a promising avenue toward sustainable environmental management in river-ecosystem integrity.
机译：面对源自天然和人为因素的多种栖息地改变，快速变化的环境对维持生态系统完整性构成了重大挑战。机器学习是通过探索数据分析来建立复杂非线性系统的强大工具。本研究旨在探索基于机器学习的方法，以使渔民社区的环境因素达到可持续河流生态系统管理。在2005年和2012年台湾新天河的各种监测站内收集了各种生态环境变量，包括河流流动，水质和物种组成的各种数据集。然后，在2005年和2012年期间的各种监测站。然后是复杂的关系和科学精华在这些异质数据集中，利用机器学习技术提取，以便在寻找可用于维护河流生态系统完整性的指导参考方面具有更全面的考虑因素。通过分析方差（ANOVA）和伽玛测试（GT）来评估和选择关键环境变量，然后使用Shannon指数应用基于自适应网络的模糊推理系统（ANFIS）进行鱼生物多样性（SI）。结果表明，模型估计与生物多样性指数之间的相关性高于0.75。 GT结果表明，生物化学需氧量（BOD），水温，总磷（TP）和硝酸盐 - 氮（NO_3-N）是生物多样性建模的重要变量。 ANFIS结果进一步表明较低的BOD，更高的TP和更大的栖息地（流动制度）通常为鱼类存活提供更合适的环境。所提出的方法不仅具有稳健的估计能力，而且还可以探索环境变量对鱼生物多样性的影响。本研究还表明，机器学习是河流生态系统诚信中可持续环境管理的承诺大道。

著录项

  • 来源
    《Environmental research》 |2020年第5期|109262.1-109262.11|共11页
  • 作者

    Jia-Hao Hu; Wen-Ping Tsai; Su-Ting Cheng; Fi-John Chang;

  • 作者单位

    Department of Bioenvironmental Systems Engineering National Taiwan University No. 1 Roosevelt Rd. Taipei 10617 Taiwan ROC;

    Department of Bioenvironmental Systems Engineering National Taiwan University No. 1 Roosevelt Rd. Taipei 10617 Taiwan ROC Department of Civil and Environmental Engineering The Pennsylvania State University University Park PA 16802-1408 USA;

    School of Forestry and Resource Conservation National Taiwan University No. 1 Roosevelt Rd. Taipei 10617 Taiwan ROC;

    Department of Bioenvironmental Systems Engineering National Taiwan University No. 1 Roosevelt Rd. Taipei 10617 Taiwan ROC;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Sustainable environmental management; Water quality; Flow regime; Adaptive network-based fuzzy inference system (ANFIS);

    机译：可持续环境管理;水质;流动制度;自适应网络的模糊推理系统（ANFIS）;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号