A combined multimetric trophic index (TRIX) and the Random Forest (RF) model were used to characterize the trophic status of Bizerte Lagoon. The RF model was used to build a predictive model of chlorophyll a using physicochemical variables (nitrite, nitrate, ammonium, phosphate, oxygen, temperature and salinity) as predictors. The approach is based on physicochemical and biological parameters measured in samples collected twice weekly from January to December 2012 at one representative sampling station located at the lagoon center. The observed TRIX values vary from 5.18 to 6.12, reflecting waters ranging from moderate to poor quality with a high trophic level. The results of the RF model show that the most important predictor of chlorophyll a variation appears to be dissolved oxygen, followed by nitrate and temperature, suggesting a reduced model for this variable. The link between chlorophyll a and observed variables was also studied using numerical models, analyzing the linear statistical correlations and a recently proposed non linear model, the Random Forest. Both methods highlight a high correlation between chlorophyll a and dissolved oxygen as opposed to chlorophyll a and nitrate. (C) 2016 Elsevier Ltd. All rights reserved.

Random Forest model and TRIX used in combination to assess and diagnose the trophic status of Bizerte Lagoon, southern Mediterranean

Solidoro C;
2016

Abstract

A combined multimetric trophic index (TRIX) and the Random Forest (RF) model were used to characterize the trophic status of Bizerte Lagoon. The RF model was used to build a predictive model of chlorophyll a using physicochemical variables (nitrite, nitrate, ammonium, phosphate, oxygen, temperature and salinity) as predictors. The approach is based on physicochemical and biological parameters measured in samples collected twice weekly from January to December 2012 at one representative sampling station located at the lagoon center. The observed TRIX values vary from 5.18 to 6.12, reflecting waters ranging from moderate to poor quality with a high trophic level. The results of the RF model show that the most important predictor of chlorophyll a variation appears to be dissolved oxygen, followed by nitrate and temperature, suggesting a reduced model for this variable. The link between chlorophyll a and observed variables was also studied using numerical models, analyzing the linear statistical correlations and a recently proposed non linear model, the Random Forest. Both methods highlight a high correlation between chlorophyll a and dissolved oxygen as opposed to chlorophyll a and nitrate. (C) 2016 Elsevier Ltd. All rights reserved.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.14083/3037
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