Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index.

Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index. Environ Sci Pollut Res Int. 2020 Jul 20;: Authors: Abba SI, Pham QB, Saini G, Linh NTT, Ahmed AN, Mohajane M, Khaledian M, Abdulkadir RA, Bach QV Abstract In recent decades, various conventional techniques have been formulated around the world to evaluate the overall water quality (WQ) at particular locations. In the present study, back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and one multilinear regression (MLR) are considered for the prediction of water quality index (WQI) at three stations, namely Nizamuddin, Palla, and Udi (Chambal), across the Yamuna River, India. The nonlinear ensemble technique was proposed using the neural network ensemble (NNE) approach to improve the performance accuracy of the single models. The observed WQ parameters were provided by the Central Pollution Control Board (CPCB) including dissolved oxygen (DO), pH, biological oxygen demand (BOD), ammonia (NH3), temperature (T), and WQI. The performance of the models was evaluated by various statistical indices. The obtained results indicated the feasibility of the developed data intelligence models for predicting the WQI at the three stations with the superior modelling results of the NNE. The results also showed that the minimum values for root mean square (RMS)...
Source: Environmental Science and Pollution Research International - Category: Environmental Health Authors: Tags: Environ Sci Pollut Res Int Source Type: research