We developed data-driven models that heavily rely on readily available remote sensing datasets, meteorologic, and field data to accomplish the following for the major water bodies within Charlotte County and along its coastlines and their extensions to the north and south of the county: (1) identify the factors controlling the occurrences of the blooms, and (2) forecast algal bloom occurrences on the extracted probabilities of archived algal bloom occurrences. The proposed research activities build on our previous experiences investigating the blue-green algae in Lakes Erie and Ontario and the red tides in Kuwait Bay. The former was funded (2004-2008) by NOAA and the latter was funded (2012-2014) by the Kuwait Institute for Scientific Research (KISR). A five-fold methodology was adopted. First, we generated a database that incorporates relevant co-registered remote sensing datasets and derived products. The generated datasets will be compiled and hosted in a web-based geographical information system (GIS) for archiving, spatial data analysis, and data distribution (Step I). The compiled datasets were essential for the implementation of the remaining steps. Second, we compiled an inventory of known locations of algal blooms that were reported in the field and were clearly identified from satellite imagery (Step II). Third, we constructed and validated a multivariate regression (MR) model and an artificial neural network (ANN) model to simulate algal bloom occurrences in the study area (Step III). Fourth, the model outputs enabled the identification of the factors controlling the occurrence of the identified bloom events (Step IV). Fifth, we experimented with a two-step modeling approach, a temporal model followed by a spatial model, to improve the prediction accuracy for each of the adopted conventional models (MR and ANN) (Step V). All the generated models were constructed using a randomly selected subset (80%) of the algal bloom inventory, whereas the remaining 20% were used for cross-validation using the receiver operating characteristics (ROC) test.