Explore the relationship between fish community and environmental factors by machine learning techniques
Journal
Environmental Research
Journal Volume
184
Date Issued
2020
Author(s)
Abstract
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 (NO3-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.
Type
journal article