An optimized Extreme Learning Machine by Evolutionary Computation for River Flow Prediction and Simulation of Water Pollution in Colorado River Basin, USA

Abstract

Humanity is witnessing scientific advances and since one of the vital concerns has been accessing water for drinking and agriculture, scientific innovations have been used to solve water challenges. Recently, Machine Learning (ML) is one of the most promising developments, therefore, for utilization of this innovation in simulating the Water Quality-Quantity Assessment (WQA) issues, the author has developed the Extreme Learning Machine (ELM) as the stand-alone model and its combination with evolutionary algorithms (EA) to optimize the modeling of the WQA parameters. So, the Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Imperialist Competitive Algorithm (ICA) are implemented to improve the performance of WQA parameters prediction. Then, the three novel models are developed for this purpose simultaneously with the stand-alone ML model. The novel models are GAELM, PSELM, and ICELM. The study area was the Colorado River basin in the USA, which was the input dataset extracted from the US Geological Survey. The considered parameters were Power of hydrogen (pH), river flow or Debi, Electrical Conductivity (EC), and Dissolved Oxygen (DO). To over- and under-fitting ML models, the input dataset was detrended and randomized by K-fold cross-validation. Furthermore, this study used seven evaluation methods for determining the models' performances. Based on the evaluation metrics, the ICELM model was the best in the EC, DO, and pH modeling. Also, the ELM model was superior in Debi prediction. Additionally, the discrepancy percent charts for all models in simulations were drawn. Moreover, the error percent plots also were drawn for all modeling. Finally, the Wilson method analyzed the prediction errors and related uncertainties.