A novel hybrid framework based on the ANFIS, discrete wavelet transform, and optimization algorithm for the estimation of water quality parameters

Abstract

Abstract Improving the performance of machine learning (ML) algorithms is essential for accurately estimating water quality parameters (WQPs). For the first time, a novel hybrid framework, namely the adaptive neural fuzzy inference system–discrete wavelet transform–gradient-based optimization (ANFIS–DWT–GBO), for estimation of electrical conductivity (EC) and total dissolved solids (TDS), is used. Before estimating WQPs, the performance of the ANFIS–DWT–GBO is proven by several benchmark data sets. In addition, three benchmark algorithms, including ANFIS, ANFIS–DWT, and ANFIS–GBO, are used to demonstrate the strength of the novel framework. The principal component analysis (PCA) method determines the best input combination in EC and TDS estimation. The consequences show that the ANFIS–DWT–GBO produces very successful and competitive results in benchmark data sets modeling and WQPs estimation compared to other algorithms. This result is due to the simultaneous use of DWT and optimization algorithm in the proposed framework. DWT can process WQP data before applying it to the algorithms. The GBO is utilized to optimize the hyperfine parameters in the ANFIS. The results show that the highest accuracy of estimating EC and TDS is in Mollasani and Gotvand stations, respectively. The correlation coefficient (R) value in the Mollasani station is 0.99, and in the Gotvand station it is 0.98.