Modeling the Water Pollutants Nonlinear-based Problems Using Optimized Intelligence Techniques and Determination of Uncertainties

Abstract

Abstract Along with the global population growth, the human need for safe drinking water sources has increased. With global warming, the water challenge is perhaps the most crucial challenge for the world community. At the same time, scientific methods are one of the best tools to help humanity. Considering that in many natural phenomena, it is possible to describe them based on complex relationships, it is almost impossible to solve them analytically and mathematically. Therefore, it is necessary to use methods with the ability, accuracy, and high speed to justify nonlinear relationships. One of these methods is Artificial Intelligence (AI). This research used the Extreme Learning Machine (ELM) model and Genetic Algorithm (GA) to create a new hybrid model Genetic Extreme Learning Machine (GAELM). AI and hybrid models were used to simulate and predict the water quality parameter changes. The study area in this work was the Colorado River Basin in the United States. The desired qualitative parameters were Electrical Conductivity (EC) and Dissolved Oxygen (DO). Finally, using seven approaches, the models' performance was compared. The results showed that the best simulation related to the GAELM hybrid model in the EC parameter modeling with indices RMSE and R2 equal to 0.1304, and 0.8619, respectively. Also, the ELM model was ranked in second place in accuracy. Based on the uncertainty analysis (UA-WSM) results, the GAELM(EC) model was the most accurate, with the minimum average prediction error equal to 0.01.