A novel hybridized neuro-fuzzy model with an optimal input combination for dissolved oxygen estimation - Maroufpoor, Saman; Sammen, Saad Sh.; Alansari, Nadhir; Abba, S.I.; Malik, Anurag; Shahid, Shamsuddin; Mokhtar, Ali; Maroufpoor, Eisa (Frontiers in Environmental Science, 2022)
Dissolved oxygen (DO) is one of the main prerequisites to protect amphibian biological systems and to support powerful administration choices. This research aimed to develop an intelligent hybrid paradigm for predicting DO concentration in river water using three hybrid models, NF-GWO (Neuro-Fuzzy with Grey Wolf Optimizer), NF-SC (Subtractive Clustering), and NF-FCM (Fuzzy c-mean). The newly proposed models' efficacy was established by comparing their performance with the standalone Neuro-Fuzzy (NF) model. Seven different input combinations of water quality variables, including water temperature (TE), specific conductivity (SC, turbidity (Tu), and pH, were used to develop the prediction models at two stations in California. The influence of the inputs on models' performance was estimated using Shannon's entropy theory and correlation. The results revealed the better performance of NF-GWO for all input combinations compared to other models. Therefore, NF-GWO with all water quality variables as input can be considered the optimal model in predicting DO concentration for the two stations. In contrast, NF-SC performed worst for most of the input combinations. The NF-GWO predicted variability of DO was 0.38, which was very near the observed variability of DO (0.44). The violin plot of NF-GWO predicted DO was found most similar to the violin plot of observed data. The dissimilarity with the observed violin was found high for NF-FCM. Therefore, this study promotes the hybrid intelligence models to predict DO concentration accurately and resolve complex hydro-environmental problems.