A Remote Sensing and Machine Learning Based Framework for the Assessment of Spatiotemporal Water Quality Along the Middle Ganga Basin

Abstract

Understanding the changes in surface water quality over time and space necessitates an examination of spatiotemporal water quality data. This data can be used to identify pollution sources, monitor changes in water quality, and assess the effectiveness of management and conservation efforts. Furthermore, spatiotemporal surface water quality assessment can forecast future water quality trends, allowing for precise decision-making and conservation. Overall, spatiotemporal water quality assessment is critical in protecting and managing water resources. Various multivariate statistical and machine learning techniques are used in this study to determine the river water quality status and comprehend the spatiotemporal pattern along the Middle Ganga Basin in Uttar Pradesh. The study was carried out for 14 years (2005-2018), with 20 Water Quality Parameters (WQPs) collected monthly and covering spatially from up-stream to downstream Ankinghat to Chopan respectively (20 monitoring stations under Central Water Commission, Middle Ganga Basin). The temporal dissimilarity of river water quality is established by applying the Spearman non-parametric correlation coefficient test (Spearman r). A significant p-level (0.0000) is observed for temperature within the season with a Spearman r of -0.866. Besides that, the parameters EC, pH, TDS, T, Ca, Cl, HCO3, Mg, NO2+NO3, SiO2, and DO strongly correlated with the season (p < 0.05). The K-means clustering algorithm temporarily classified the 20 monitoring stations into four clusters based on the similarity and dissimilarity of WQPs. Box and Whisker plots were generated based on these clusters to study water quality trends along individual clusters in different seasons. PCA was applied to screen out the most dominating WQPs causing spatial and seasonal variations from a large data set. Seasonally, the three PCs chosen explained 75.69% and 75% of the variance in the data. With PCs >0.70, the variables EC, pH, Temp, TDS, NO 2+NO3, P-Tot, BOD, COD, and DO have been identified as the dominant pollutants. The applied RDA analysis revealed that LULC has a moderate to strong contribution to WQPs during the monsoon season but not during the non-monsoon season. Furthermore, dense vegetation is critical for keeping water clean, whereas agriculture, barren land and build-up area degrade water quality. Besides that, the findings suggest the relationship between WQPs and LULC differs at different spatial scales. The istacked ensemble regression model is applied to understand the model's predictive power across different clusters and scales. Overall, the results indicate that the riparian scale is more predictive than a watershed and reach scales. As a further part of this work, an integrated use of remote sensing, insitu measurements, and machine learning modelling is used better to understand the water quality status along the study region. In this context, a remote sensing framework based on the Extreme Gradient Boosting (XGBoost) and Multilayer Perceptron (MLP) regressor with optimized hyperparameters to quantify the concentrations of different WQPs from the Landsat-8 satellite imagery is developed. Six years of satellite data from upstream to downstream Ankinghat to Chopan (20 stations under Central Water Commission (CWC), Middle Ganga Basin) are analysed to characterise the trends of dominant physicochemical WQPs across the four identified clusters. A significant coefficient of determination (R2) in the range of 0.88- 0.98 for XGBoost and 0.72-0.97 for MLP was generated using the developed XGBoost and MLP regression models. The bands B1- B4 and their ratios are found to be more consistent with the WQPs. Meanwhile, the performance matrix RMSE for the parameters SiO2 and DO for all clusters for the XGBoost method is determined to be superior to MLP. Indeed, these findings show that a small number of insitu measurements is sufficient to develop reliable models for estimating the spatiotemporal variations of physicochemical and biological WQPs. As a result, Landsat-8 models could aid in the environmental, economic, and social management of any body of water.