Performance assessment of data driven water models using water quality parameters of Wangchu river, Bhutan

Abstract

Multifarious anthropogenic activities triggered by rapid urbanization has led to contamination of water sources at unprecedented rate, with less surveillance, investigation and mitigation. The use of artificial intelligence (AI) in tracking and predicting water quality parameters has surpassed the use of other conventional methods. This study presents the assessment of three main models: adaptive neuro fuzzy inference system (ANFIS), artificial neural network (ANN) and multiple linear regression (MLR) on water quality parameters of Wangchu river located at capital city of Bhutan. The performance and predictive ability of these models are compared and the optimal model for predicting the parameters are recommended based on the coefficient correlation (CC), root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE) evaluation criteria. Overall NSE and RMSE, the ANN model predicted parameters with maximum efficiency of 97.3 percent and minimum error of 8.57. The efficiency of MLR and ANFIS model are 95.9 percent and 94.1 percent respectively. The overall error generated by MLR and ANFIS are 10.64 and 12.693 respectively. From the analysis made, the ANN is recommended as the most suitable model in predicting the water quality parameters of Wangchu river. From the six-training function of ANN, trainBR (Bayesian Regularization) achieved the CC of 99.8%, NSE of 99.3% and RMSE of 9.822 for next year data prediction. For next location prediction, trainBR achieved CC of 99.2%, NSE of 98.4% and RMSE of 6.485, which is the higher correlation and maximum efficiency with less error compared to rest of the training functions. The study represents first attempt in assessing water quality using AI technology in Bhutan and the results showed a positive conclusion that the traditional means of experiments to check the quality of river water can be substituted with this reliable and realistic data driven water models. Article highlights Total dissolved solids (TDS), electrical conductivity (EC), potential of hydrogen (pH) and dissolved oxygen (DO) are selected as main water quality parameters as data for modeling. Artificial neural network model gives highest efficiency and accuracy compared to MLR and ANFIS model. Use of artificial intelligence shows better performance to provide water quality and future predictions over conventional methods leading to conservation of water resources and sustainability.

The eutrophication-related index of drinking water sources based on the oxidation-reduction potential

“…Given the numerous water quality parameters, researchers often use different models to predict water quality or study the relationships between different water quality parameters (Roy et al 2018;Nimisha et al 2020). For instance, Choden et al (2022) selected parameters such as dissolved total solids, conductivity, pH, and DO as modeling data, using a neural network model to provide water quality analysis and future predictions, thus achieving water resource protection and sustainability. Fijani et al (2019) used the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) algorithms, coupled with extreme learning machines (ELM) and least-squares support vector machines (LSSVM), to achieve real-time evaluation of reservoir water quality.…”