Time series and neural network to forecast water quality parameters using satellite data

Complex and highly variable parameters of water quality are difficult to predict over time and space. In most cases, predictions of surface chlorophyll-a (Chl-a) and sea surface temperature (SST) are based on coupled ecological-hydrodynamic modeling, which is driven by oceanic and climatic conditions. Since these models are complex, simpler and efficient methods of predictions are required. The objective of this study is to evaluate the performance of stochastic time series models - Seasonal Auto Regressive Integrated Moving Average (SARIMA) and nonlinear neural network (NN) - to predict Chl-a and SST in coastal areas. Additionally, this study examines the sensitivity of the aforementioned models with respect to variation in depth and changes in turbidity. The data used in this study are satellite-derived Chl-a and SST collected for the Arabian Gulf region for a period of 10 years. This region has been selected for the study due to pronounced variability in depth and changes in turbidity in its waters. As a result of this study, it has been shown that SARIMA and NN are able to forecast Chl-a and SST. In the deep & less turbid water, SARIMA and NN models (SARIMA/NN) produced Chl-a R²:RMSE values of (0.5:0.6)/(0.37:0.53) and SST R²:RMSE (0.9:1.36)/(0.8:1.43). However, the NN has performed better than SARIMA in predicting Chl-a in turbid & shallow waters, especially when Chl-a concentrations are high (>2 mgm⁻³). Based on our analysis, we conclude that SARIMA and NN are capable of forecasting the future values of Chl-a and SST in regions with pre-existing knowledge of the water depth and turbidity.