An improved adaptive neuro fuzzy inference system model using conjoined metaheuristic algorithms for electrical conductivity prediction.

Precise prediction of water quality parameters plays a significant role in making an early alert of water pollution and making better decisions for the management of water resources. As one of the influential indicative parameters, electrical conductivity (EC) has a crucial role in calculating the proportion of mineralization. In this study, the integration of an adaptive hybrid of differential evolution and particle swarm optimization (A-DEPSO) with adaptive neuro fuzzy inference system (ANFIS) model is adopted for EC prediction. The A-DEPSO method uses unique mutation and crossover processes to correspondingly boost global and local search mechanisms. It also uses a refreshing operator to prevent the solution from being caught inside the local optimal solutions. This study uses A-DEPSO optimizer for ANFIS training phase to eliminate defects and predict accurately the EC water quality parameter every month at the Maroon River in the southwest of Iran. Accordingly, the recorded dataset originated from the Tange-Takab station from 1980 to 2016 was operated to develop the ANFIS-A-DEPSO model. Besides, the wavelet analysis was jointed to the proposed algorithm in which the original time series of EC was disintegrated into the sub-time series through two mother wavelets to boost the prediction certainty. In the following, the comparison between statistical metrics of the standalone ANFIS, least-square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), generalized regression neural network (GRNN), wavelet-LSSVM (WLSSVM), wavelet-MARS (W-MARS), wavelet-ANFIS (W-ANFIS) and wavelet-GRNN (W-GRNN) models was implemented. As a result, it was apparent that not only was the W-ANFIS-A-DEPSO model able to rise remarkably the EC prediction certainty, but W-ANFIS-A-DEPSO (R = 0.988, RMSE = 53.841, and PI = 0.485) also had the edge over other models with Dmey mother in terms of EC prediction. Moreover, the W-ANFIS-A-DEPSO can improve the RMSE compared to the standalone ANFIS-DEPSO model, accounting for 80%. Hence, this model can create a closer approximation of EC value through W-ANFIS-A-DEPSO model, which is likely to act as a promising procedure to simulate the prediction of EC data.

Surface water sodium (Na+) concentration prediction using hybrid weighted exponential regression model with gradient-based optimization.

Undeniably, there is a link between water resources and people's lives and, consequently, economic development, which makes them vital in health and the environment. Proper water quality forecasting time series has a crucial role in giving on-time warnings for water pollution and supporting the decision-making of water resource management. The principal aim of this study is to develop a novel and cutting-edge ensemble data intelligence model named the weighted exponential regression and hybridized by gradient-based optimization (WER-GBO). Indeed, this is to reach more meticulous sodium (Na+) prediction monthly at Maroon River in the southwest of Iran. This developed model has advantages over other previous methodologies thanks to the following merits: (i) it can improve the performance and ability by mixing the outputs of four distinct data intelligence (DI) models, i.e., adaptive neuro-fuzzy inference system (ANFIS), least square support vector regression (LSSVM), Bayesian linear regression (BLR), and response surface regression (RSR); (ii) the proposed model can employ a Cauchy weighted function combined with an exponential-based regression model being optimized by GBO algorithm. To evaluate the performance of these models, diverse statistical indices and graphical assessment including error distributions, box plots, scatter-plots with confidence bounds and Taylor diagrams were conducted. According to obtained statistical metrics and verified validation procedures, the proposed WER-GBO resulted in promising accuracy compared to other models. Furthermore, the outcomes revealed the WER-GBO (R = 0.9712, RMSE = 0.639, and KGE = 0.948) reached more accurate and reliable results than other methods such as the ANFIS, LSSVM, BLR, and RSR for Na prediction in this study. Hence, the WER-GBO model can be considered a constructive technique to forecast the water quality parameters.