Integrated machine learning reveals aquatic biological integrity patterns in semi-arid watersheds

Lina Li; Rui Xia; Ming Dou; Kai Zhang; Yan Chen; Ruining Jia; Xiaoxuan Li; Jinghui Dou; Xiang Li; Qiang Hu; Hui Zhang; Nixi Zhong; Chao Yan

doi:10.1016/j.jenvman.2024.121054

Integrated machine learning reveals aquatic biological integrity patterns in semi-arid watersheds

J Environ Manage. 2024 May:359:121054. doi: 10.1016/j.jenvman.2024.121054. Epub 2024 May 9.

Authors

Lina Li¹, Rui Xia², Ming Dou³, Kai Zhang⁴, Yan Chen⁵, Ruining Jia⁶, Xiaoxuan Li⁴, Jinghui Dou⁶, Xiang Li⁴, Qiang Hu⁴, Hui Zhang⁷, Nixi Zhong⁶, Chao Yan⁶

Affiliations

¹ State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; School of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou, 450001, China.
² State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China. Electronic address: xiarui@craes.org.cn.
³ School of Water Conservancy Science and Engineering, Zhengzhou University, Zhengzhou, 450001, China.
⁴ State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
⁵ State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China.
⁶ State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; Northwest University College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, China.
⁷ State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; School of Information Technology & Management, University of International Business and Economics, 100029, China.

PMID: 38728982
DOI: 10.1016/j.jenvman.2024.121054

Abstract

Semi-arid regions present unique challenges for maintaining aquatic biological integrity due to their complex evolutionary mechanisms. Uncovering the spatial patterns of aquatic biological integrity in these areas is a challenging research task, especially under the compound environmental stress. Our goal is to address this issue with a scientifically rigorous approach. This study aims to explore the spatial analysis and diagnosis method of aquatic biological based on the combination of machine learning and statistical analysis, so as to reveal the spatial differentiation patterns and causes of changes of aquatic biological integrity in semi-arid regions. To this end, we have introduced an innovative approach that combines XGBoost-SHAP and Fuzzy C-means clustering (FCM), we successfully identified and diagnosed the spatial variations of aquatic biological integrity in the Wei River Basin (WRB). The study reveals significant spatial variations in species number, diversity, and aquatic biological integrity of phytoplankton, serving as a testament to the multifaceted responses of biological communities under the intricate tapestry of environmental gradients. Delving into the depths of the XGBoost-SHAP algorithm, we discerned that Annual average Temperature (AT) stands as the pivotal driver steering the spatial divergence of the Phytoplankton Integrity Index (P-IBI), casting a positive influence on P-IBI when AT is below 11.8 °C. The intricate interactions between hydrological variables (VF and RW) and AT, as well as between water quality parameters (WT, NO₃-N, TP, COD) and AT, collectively sculpt the spatial distribution of P-IBI. The fusion of XGBoost-SHAP with FCM unveils pronounced north-south gradient disparities in aquatic biological integrity across the watershed, segmenting the region into four distinct zones. This establishes scientific boundary conditions for the conservation strategies and management practices of aquatic ecosystems in the region, and its flexibility is applicable to the analysis of spatial heterogeneity in other complex environmental contexts.

Keywords: Aquatic biological integrity; Fuzzy C-Means clustering; Spatial analysis; Wei river basin; XGBoost-SHAP algorithm.

MeSH terms

Algorithms
Environmental Monitoring / methods
Machine Learning*
Phytoplankton
Rivers