Prediction of the Area of High-Turbidity Water in the Yatsushiro Sea, Japan, Using Machine Learning with Satellite, Meteorological, and Oceanographic Data

Abstract

Turbid water is known to affect aquatic ecosystems. If the spread of turbid water can be predicted, it is expected to lead to the prediction of damage caused by turbid water in rich aquatic ecosystems and aquaculture farms, and to countermeasures against turbid water. In this study, we developed a method for predicting the area of high-turbidity water using machine learning with satellite-observed total suspended solids (TSS) product and relatively readily available meteorological and oceanographic data (rainfall, wind direction and speed, atmospheric pressure, and tide level) in the past and evaluated it for the Kuma River estuary of the Yatsushiro Sea in Japan. The results showed that the highest accuracy was obtained using random forest regression, with a coefficient of determination of 0.552, when the area of high-turbidity water based on the previous day’s TSS product and hourly meteorological and oceanographic data from the previous day were used as inputs. The most important factor for the prediction was the area of high-turbidity water, followed by wind, and tide level, but the effect of rainfall was small, which was probably due to the flood-control function of the river. Our future work will be to evaluate the applicability of the method to other areas, improve the accuracy, and predict the distribution area.