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.
Environ Res. 2020 May;184:109262. doi: 10.1016/j.envres.2020.109262. Epub 2020 Feb 17.
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-environmental 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-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.
面对自然和人为因素导致的多种生境改变，快速变化的环境对维持生态系统完整性带来了重大挑战。机器学习是通过探索性数据分析来模拟复杂非线性系统的有力工具。本研究旨在探索一种基于机器学习的方法，将环境因素与鱼类群落联系起来，以实现可持续的河流生态系统管理。
在 2005 年至 2012 年期间，在台湾新店河的各个监测站收集了大量的数据集，这些数据集涉及广泛的生态环境变量，包括河流流量、水质和物种组成。然后，使用机器学习技术提取这些异构数据集的复杂关系和科学本质，以更全面地考虑寻找维护河流生态系统完整性的指导参考。我们通过方差分析（ANOVA）和伽马检验（GT）来评估和选择关键环境变量，然后应用自适应网络模糊推理系统（ANFIS）来使用香农指数（SI）估计鱼类生物多样性。
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，生化需氧量（BOD）、水温和总磷（TP）以及硝酸盐氮（NO-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、较高的 TP 和更大的栖息地（水流模式）通常为鱼类物种的生存提供更适宜的环境。
所提出的方法不仅具有强大的估计能力，还可以探索环境变量对鱼类生物多样性的影响。本研究还表明，机器学习是实现河流生态系统完整性可持续环境管理的一个有前途的途径。
