Ensemble Methodology Using Multistage Learning for Improved Detection of Harmful Algal Blooms

Abstract

The available empirical remote sensing techniques for harmful algal bloom (HAB) detection are reliant on prior observations and thresholds. These techniques tend to give high false alarm rate, as they are limited in spatiotemporal contextual information and decision combination techniques. We propose a multistage learning based ensemble methodology addressing the above constraints for performance improvement of HAB detection. Machine learning-based spatiotemporal data mining approach, along with empirical relationships, is used for HAB detection in the first stage of the ensemble, to exploit the potential benefits of each individual detection technique. The decision outputs from these detection techniques are fused in the second stage using nonlinear modeling-based combination techniques unlike conventional weighted averages. The proposed ensemble methodology outperforms all of the individual members and gave a significant overall performance improvement up to 0.8632 kappa accuracy. The performance is evaluated over tenfold cross validation average and compared against various ensemble methods and combination techniques.