Predicting Bio-indicators of Aquatic Ecosystems Using the Support Vector Machine Model in the Taizi River, China

Juntao Fan, Jin Wu, Weijing Kong, Yizhang Zhang, Mengdi Li, Yuan Zhang, Wei Meng and And Mengheng Zhang
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Juntao Fan: College of Water Science, Beijing Normal University, Beijing 100875, China
Jin Wu: College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China
Weijing Kong: College of Water Science, Beijing Normal University, Beijing 100875, China
Yizhang Zhang: College of Water Science, Beijing Normal University, Beijing 100875, China
Mengdi Li: College of Water Science, Beijing Normal University, Beijing 100875, China
Yuan Zhang: College of Water Science, Beijing Normal University, Beijing 100875, China
Wei Meng: College of Water Science, Beijing Normal University, Beijing 100875, China
And Mengheng Zhang: College of Water Science, Beijing Normal University, Beijing 100875, China

Sustainability, 2017, vol. 9, issue 6, 1-11

Abstract: Numerous studies have sought to clarify the link between biological communities and environmental factors in freshwater, but an appropriate model is still needed to predict the effect of water quality and hydromorphology improvement on biological communities and to provide useful information for ecological restoration planning. In this study, a support vector machine (SVM) was used to predict the bio-indicators of an aquatic ecosystem (i.e., macroinvertebrates, fish, algae communities) in the Taizi River, northeast China. Environmental factors, including physico-chemical (i.e., dissolved oxygen (DO), electricity conductivity (EC), ammonia nitrogen (NH 3 -N), chemical oxygen demand (COD), biological oxygen demand in five days (BOD 5 ), total phosphorus (TP), total nitrogen (TN)) and hydromorphology parameters (i.e., water quantity, channel change, morphology diversity) were used as the input variables to train and validate the SVM model. The sensitivity of the input variables for the prediction was examined by removing a variable from the SVM model. Results revealed that the SVM model reproduced the variation in bio-indicators of fish and algae communities well, based on the input variables. The sensitivity for the input variables applied in SVM showed that in the Taizi River the most sensitive variables for predicting macroinvertebrate and algae communities were channel change, DO, TN, and TP, while the most sensitive variables for predicting fish communities were DO and BOD 5 . This study proposed an effective method for predicting biological communities, which will improve freshwater quality and hydromorphology management schemes. The outputs can guide the decision-making process in river basin management, support the prioritization of actions and resource allocation, and help to monitor and evaluate the effectiveness of interventions.

Keywords: support vector machine; modeling; environmental indicator; freshwater biology (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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