Estimation of Ocean Water Chlorophyll-a Concentration Using Computational Intelligence

In this paper, we present a computational method to estimate chlorophyll a (Chl a) concentration from remotely sensed reflectance (Rrs) measurements. The proposed method integrates two computational intelligence paradigms, a fuzzy c-means (FCM) cluster analysis and an ensemble of artificial neural networks (ANNs). This approach will be particularly useful in estimating Chl a concentration from Rrs measured at various locations representing heterogeneous water types. The performance of the proposed method is compared with the traditional approach, where a single ANN is used for all water types. We showed that the cluster-based approach has the potential to build a more global Chl a prediction model