Smarter water quality monitoring in reservoirs using interpretable deep learning models and feature importance analysis

Abstract

This study utilized datasets from an ongoing monitoring project conducted in Wadi Dayqah Dam, the largest reservoir in Oman. The dataset comprises information on ten water quality variables collected by AAQ-RINKO device for 20 field sampling stations, encompassing different depths within the water columns. First, an in-depth data analysis was conducted to process and characterize variations in key parameters. Then, four deep learning models were developed and evaluated, including the Gated Residual Variable Selection (GRVS), Deep and Cross (DC), Deep and Wide (DW), and Base models for estimating two important water quality parameters, namely dissolved oxygen (DO) and chlorophyll-a (Chl-a). To justify the performance of deep learning methods, they were compared to four traditional machine learning techniques: MLP, SVR, AdaBoost, and LR-Lasso models. The GRVS model emerged as the most effective method, achieving high accuracy with R2 and RMSE values of 0.98 and 0.38 for the DO parameter and 0.84, and 0.45 for the Chl-a parameter, respectively. Finally, two SHapley Additive exPlanations (SHAP) and Softmax layer model interpretation approaches were employed to highlight the influential factors affecting the predictions in the best model. Results from the SHAP analysis identified pH, depth, and temperature as the most significant variables, with mean SHAP values of 1.8, 0.75, and 0.36 for DO and 0.74, 0.37, and 0.3 for Chl-a, respectively. The data-driven framework implemented in this study holds promise for efficiently approximating hard-to-measure water quality indicators in monitoring projects using cost-effective inputs, which is particularly valuable in resource-constrained settings.