River water quality management using a fuzzy optimization model and the NSFWQI Index

Abstract

In this study, a novel multiple-pollutant waste load allocation (WLA) model for a river system is presented based on the National Sanitation Foundation Water Quality Index (NSFWQI). This study aims to determine the value of the quality index as the objective function integrated into the fuzzy set theory so that it could decrease the uncertainties associated with water quality goals as well as specify the river's water quality status rapidly. The simulation-optimization (S-O) approach is used for solving the proposed model. The QUAL2K model is used for simulating water quality in different parts of the river system and ant colony optimization (ACO) algorithm is applied as an optimizer of the model. The model performance was examined on a hypothetical river system with a length of 30 km and 17 checkpoints. The results show that for a given number of both the simulator model runs and the artificial ants, the maximum objective function will be obtained when the regulatory parameter of the ACO algorithm (i.e., q0) is considered equal to 0.6 and 0.7 (instead of 0.8 and 0.9). Also, the results do not depend on the exponent of the membership function (i.e., γ). Furthermore, the proposed methodology can find optimum solutions in a shorter time.

Application of Ant Colony Optimization in Water Resource Management

“…Ghorbani et al [38] used ACO algorithm to solve the fuzzy optimization problem for river water quality management. The proposed model handled multiple objectives and multiple pollutants in a river system.…”

Ant-Inspired Metaheuristic Algorithms for Combinatorial Optimization Problems in Water Resources Management

“…ACOR is coupled with ANFIS (ANFIS-ACOR) and used to study spatial salinity distribution. ACO is being widely coupled with machine learning algorithms to predict soil salinity in different environmental conditions and prediction of groundwater salinity [61]. Electrical resistivity imaging is used to reconstruct a conductivity model of subsurface water-bearing bodies.…”

Enhancing Water Potability Assessment Using Hybrid Fuzzy-Naïve Bayes

“…In addition to using the methods mentioned above, many water potability determinations are also carried out by applying the fuzzy logic method. Fuzzy set theory can reduce uncertainty related to water quality objectives and determine river water quality status quickly [9]. In addition, the level of water quality can be divided into several categories and used for various community needs according to the resulting fuzzy classification.…”