Development of a Coupled Spatiotemporal Algal Bloom Model for Coastal Areas: A Remote Sensing and Data Mining-Based Approach

We developed and successfully applied data-driven models that heavily rely on readily available remote sensing datasets to investigate probabilities of algal bloom occurrences in Kuwait Bay. An artificial neural network (ANN) model, a multivariate regression (MR) model, and a spatiotemporal hybrid model were constructed, optimized, and validated. Temporal and spatial submodels were coupled in a hybrid modeling framework to improve on the predictive powers of conventional ANN and MR generic models. Sixteen variables (sea surface temperature [SST], chlorophyll a OC3M, chlorophyll a Generalized Inherent Optical Property (GIOP), chlorophyll a Garver-Siegel-Maritorena (GSM), precipitation, CDOM, turbidity index, PAR, euphotic depth, Secchi depth, wind direction, wind speed, bathymetry, distance to nearest river outlet, distance to shore, and distance to aquaculture) were used as inputs for the spatial submodel; all of these, with the exception of bathymetry, distance to nearest river outlet, distance to shore, and distance to aquaculture were used for the temporal sub-model as well. Findings include: 1) the ANN model performance exceeded that of the MR model and 2) the hybrid models improved the model performance significantly; 3) the temporal variables most indicative of the timing of bloom propagation are sea surface temperature, Secchi disk depth, wind direction, chlorophyll a (OC3M), and wind speed; and 4) the spatial variables most indicative of algal bloom distribution are the ocean chlorophyll from OC3M, GSM, and the GIOP products; distance to shore; and SST. The adopted methodologies are reliable, cost-effective and could be used to forecast algal bloom occurrences in data-scarce regions.