MODIFIED REDFIELD RATIO COMBINED WITH ARTIFICIAL NEURAL NETWORK SIMULATION TO ESTIMATE THE ALGAL BLOOM PATTERN

EP.
2018;
: pp. 29-36
1
Biofluids and Biosystems Modeling Lab (BBML) Department of Engineering, Faculty of Agriculture, Dalhousie University
2
Biofluids and Biosystems Modeling Lab (BBML) Faculty of Agriculture, Dalhousie University, Canada
3
Biofluids and Biosystems Modeling Lab (BBML) Department of Engineering, Faculty of Agriculture, Dalhousie University
4
Biofluids and Biosystems Modeling Lab (BBML) Department of Engineering, Faculty of Agriculture, Dalhousie University

In previous work (Hushchyna and Nguyen-Quang, 2017), we have introduced the Modified Redfield Ratio (MRR) to estimate algal blooms occurring in Mattatall Lake, Nova Scotia (Canada). The goals of this paper are to test this modified index based on nutrient level to estimate bloom patterns via field experimental data and by the mathematical simulation with a supervised learning model Artificial Neural Network. Although the original Redfield ratio, a molar index applied in marine water based on the C:N:P ratio, is still discussed, the MRR based on Nitrates/Phosphates ratio we suggested herein seems to have many practical aspects for fresh water to evaluate the onset of bloom patterns.

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