Optimizing biodiversity prediction from abiotic parameters
We propose an integrated framework for optimising diversity prediction. Prediction was based on 3 machine learning algorithms using 4 abiotic parameters. IBk algorithm achieved the best prediction with high predictive power. Prediction was optimized using noise-free simulated phytoplankton assemblages. PREPHYB software is provided for diversity prediction in E. Mediterranean waters.
machine learning phytoplankton diversity indices simulated assemblages instance based learning