Optimizing transient monitoring of river streamflow by a highly predictive model utilizing Ensemble learning models and Multi algorithms

Global warming and population growth have significantly intensified the challenges in securing drinking water supplies. This study investigates transient instabilities of streamflow using ensemble machine learning (EML) and machine learning (ML) methodologies on the South Platte river in the United States. The United States Geological Survey’s online database was utilized to obtain the primary dataset. Several technical approaches were employed for preprocessing the initial dataset: cleaning outlier data, clean missing data, and 10 fold cross-validation. Nonlinear programming, genetic algorithm, least square, linear programming, gradient descent, particle swarm optimization, Nelder Mead, and simulated annealing were employed algorithms to develop eight-weighted EML models. The results showed that the ensemble learning approach and the aggregation of weak learners by mentioned algorithms have been significantly successful. Particularly, the nonlinear programming-EML (NLP-EML) outperformed others, achieving the highest prediction accuracy with an R² coefficient equal to 0.97. The probability density function showed that NLP-EML was the most reliable model. Overall, the findings highlight the superior performance and reliability of EML approaches in hydrological modeling, offering practical guidance to experts on the creation of robust ensemble models for improved prediction accuracy.