Modeling Surface Water Quality Using the Adaptive Neuro-Fuzzy Inference System Aided by Input Optimization

Abstract

Modeling surface water quality using soft computing techniques is essential for the effective management of scarce water resources and environmental protection. The development of accurate predictive models with significant input parameters and inconsistent datasets is still a challenge. Therefore, further research is needed to improve the performance of the predictive models. This study presents a methodology for dataset pre-processing and input optimization for reducing the modeling complexity. The objective of this study was achieved by employing a two-sided detection approach for outlier removal and an exhaustive search method for selecting essential modeling inputs. Thereafter, the adaptive neuro-fuzzy inference system (ANFIS) was applied for modeling electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River. A larger dataset of a 30-year historical period, measured monthly, was utilized in the modeling process. The prediction capacity of the developed models was estimated by statistical assessment indicators. Moreover, the 10-fold cross-validation method was carried out to address the modeling overfitting issue. The results of the input optimization indicate that Ca2+, Na+, and Cl− are the most relevant inputs to be used for EC. Meanwhile, Mg2+, HCO3−, and SO42− were selected to model TDS levels. The optimum ANFIS models for the EC and TDS data showed R values of 0.91 and 0.92, and the root mean squared error (RMSE) results of 30.6 µS/cm and 16.7 ppm, respectively. The optimum ANFIS structure comprises a hybrid training algorithm with 27 fuzzy rules of triangular fuzzy membership functions for EC and a Gaussian curve for TDS modeling, respectively. Evidently, the outcome of the present study reveals that the ANFIS modeling, aided with data pre-processing and input optimization, is a suitable technique for simulating the quality of surface water. It could be an effective approach in minimizing modeling complexity and elaborating proper management and mitigation measures.

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

“…Several ML approaches have been promoted for modelling WQ parameters. The ML models applied include ANN [10,[26][27][28], adaptive neuro-fuzzy inference system (ANFIS) [7,29,30], (SVR) [31][32][33], random forest (RF) [34,35], k-nearest neighbours (KNN) [36], Naive Bayes [37], decision tree (DT) [38,39], and extreme gradient boosting (XGB) [40]. The advantages and disadvantages of the most used ML techniques are summarised in Table 2.…”

Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality

“…The available literature demonstrated that the conventional models often produced inaccurate results due to the complicated hydrological processes. Therefore, more suitable, reliable, and robust modeling methods are required for water quality assessment [6,13].…”

Dissolved oxygen modelling of the Yamuna River using different ANFIS models

“…The assessment of DO variation for heavily polluted rivers based on statistical methods is not the appropriate approach nowadays due to complex and nonlinear water quality parameters (Cox 2003;Parmar & Keshari 2012;Arora & Keshari 2021a). Various researchers have used machine learning techniques such as artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) to predict the variation, simulate and forecast the water quality parameters (Singh et al 2009;Chen & Liu 2014;Ay & Kisi 2017;Tiwari et al 2018;Shah et al 2021, Alsulaili & Refaie 2021.…”

“…The fuzzy theory has been widely used to model the nonlinear behaviour for various hydrological applications (Altunkaynak et al 2005;Keskin et al 2006;Chang et al 2015;Khan & Valeo 2015;Ay & Kisi 2017;Arora & Keshari 2021a). The fuzzy system can remove the uncertainties from the data and develop the model structure through the rule-based system (Huang et al 2010;Shah et al 2021). Altunkaynak et al (2005) used the Takagi-Sugeno fuzzy logic approach to model fluctuations in DO at Golden Horn, Turkey, and compared the results with autoregressive moving average (ARMA) models.…”

“…Khan & Valeo (2015) applied fuzzy regression and compared it with the Tanaka and Diamond method of fuzzy modelling to predict the DO and found that the ability to record water quality parameters' uncertainty makes the fuzzy regression technique a substantial approach for predicting DO. Shah et al (2021) compared the performance of various ANFIS models by varying the type and number of membership functions (MFs) to predict the electrical conductivity (EC) and total dissolved solids (TDS) in the Indus River. The ANFIS model was developed using three MFs where triangular and Gaussian MF type was used for EC and TDS, respectively.…”