Prediction of Algal Bloom Occurrence in Laguna Lake, Philippines using Artificial Neural Networks (ANN)

Algal blooms pertain to an undesirable formation of unicellular freely-floating algal scum caused by the rapid growth of phytoplankton, which can become a hazard for the water body ecosystem. Laguna Lake serves as both a source of livelihood and water supply for the residents in the region and the risk of algal blooms should be detected for safe and efficient management. The research presents a method for predicting the amount of phytoplankton to alert the monitoring agencies of incidences of high phytoplankton as a scalable and inexpensive early-warning tool. The study focuses on the development of a prediction model based on water quality parameters measured by the Laguna Lake Development Authority (LLDA) from 2008 to 2018: nitrate, orthophosphate, water temperature, turbidity, chlorophyll-a, and phytoplankton counts. The system predicts the phytoplankton counts of the next month using three months of previous values of the water quality parameters, modeled through the multilayer perceptron neural network method. The research uses a walk-forward validation method to obtain the root-mean-square-error (RMSE) of the model. The model was used on three stations and these predicted values that had statistically less RMSE than the ordinary least square regression.