Comparison of Multiple Linear Regression and Artificial Neural Network Models in retrieving Water Quality Parameters using Remotely Sensed Data: Lake Victoria (Tanzanian) Water

Water is an essential resource for the survival and well-being of humans and ecosystems; hence, the quality of water is also crucial. To determine the quality of surface water, water quality parameters are traditionally measured by using in-situ measurements. However, accessing such measurements is time-consuming and labor-intensive. Furthermore, it is almost impossible to obtain measurements of the entire waterbody through this method. Therefore, in this study, we compare the capability of Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models in retrieving water quality parameters from remotely sensed data in inland waterbodies in the case of Lake Victoria, in Tanzania. The models are commonly used for retrieving remote sensing-based water parameters. The performance of MLR and ANN in retrieving Turbidity and Total Dissolved Solids (TDS) data is evaluated. Surface reflectance values from Landsat 8 Operational Land Imager (OLI) sensor images and in-situ data are used to find reliable relationships between Turbidity and Total Dissolved Solids (TDS). The results indicate that the ANN model performs better than MLR in retrieving Turbidity and TDS data: ANN had an accuracy (R2) of 88.73% and 83.36%, respectively, while MLR had an accuracy (R2) of 66.66% and 78.42%, respectively. Other criteria that were used for comparison include the standard error (SE), root mean square error (RMSE) and mean absolute error (MAE) which indicated that ANN performed better than MLR. The general distribution of Turbidity and TDS data, as mapped in Lake Victoria, shows that the water quality of the lake, as described by World Health Organization (WHO) standards, is good and could, therefore, be used for human consumption. Based on the results for Turbidity and TDS obtained in this study, we recommend that ANNs and Landsat 8 OLI satellite images be used for water quality parameter modeling.