A novel hybridized neuro-fuzzy model with an optimal input combination for dissolved oxygen estimation

Abstract

Dissolved oxygen (DO) is one of the main prerequisites to protect amphibian biological systems and to support powerful administration choices. This research investigated the applicability of Shannon’s entropy theory and correlation in obtaining the combination of the optimum inputs, and then the abstracted input variables were used to develop three novel intelligent hybrid models, namely, NF-GWO (neuro-fuzzy with grey wolf optimizer), NF-SC (subtractive clustering), and NF-FCM (fuzzy c-mean), for estimation of DO concentration. 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 performance of proposed models for DO estimation was assessed using statistical metrics and visual interpretation. The results revealed the better performance of NF-GWO for all input combinations than other models where its performance was improved by 24.2–66.2% and 14.9–31.2% in terms of CC (correlation coefficient) and WI (Willmott index) compared to standalone NF for different input combinations. Additionally, the MAE (mean absolute error) and RMSE (root mean absolute error) of the NF model were reduced using the NF-GWO model by 9.9–46.0% and 8.9–47.5%, respectively. Therefore, NF-GWO with all water quality variables as input can be considered the optimal model for predicting DO concentration of the two stations. In contrast, NF-SC performed worst for most of the input combinations. 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 the NF-FCM model. Therefore, this study promotes the hybrid intelligence models to predict DO concentration accurately and resolve complex hydro-environmental problems.

Solar Radiation Prediction Using an Improved Adaptive Neuro-Fuzzy Inference System (ANFIS) Optimization Ensemble

“…The goal is to minimize the objective function, as illustrated in Figure 4, before computing new fuzzy clusters. The fuzzifier exponent, P, lies ANFIS structure with two inputs, one output, and two rules [40]. between 0 and 1, while c represents the number of clusters.…”