Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting

• Published in: Scientific reports Vol. 10; no. 1; p. 4684
• Main Authors: Afan, Haitham Abdulmohsin, Allawi, Mohammed Falah, El-Shafie, Amr, Yaseen, Zaher Mundher, Ahmed, Ali Najah, Malek, Marlinda Abdul, Koting, Suhana Binti, Salih, Sinan Q., Mohtar, Wan Hanna Melini Wan, Lai, Sai Hin, Sefelnasr, Ahmed, Sherif, Mohsen, El-Shafie, Ahmed
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13-03-2020; Nature Publishing Group
• Subjects: 704/172/4081; 704/242; Algorithms; Genetic algorithms; Humanities and Social Sciences; multidisciplinary; Neural networks; Rivers; Science; Science (multidisciplinary); Stream flow; Time series; Water resources
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-020-61355-x

In nature, streamflow pattern is characterized with high non-linearity and non-stationarity. Developing an accurate forecasting model for a streamflow is highly essential for several applications in the field of water resources engineering. One of the main contributors for the modeling reliability is the optimization of the input variables to achieve an accurate forecasting model. The main step of modeling is the selection of the proper input combinations. Hence, developing an algorithm that can determine the optimal input combinations is crucial. This study introduces the Genetic algorithm (GA) for better input combination selection. Radial basis function neural network (RBFNN) is used for monthly streamflow time series forecasting due to its simplicity and effectiveness of integration with the selection algorithm. In this paper, the RBFNN was integrated with the Genetic algorithm (GA) for streamflow forecasting. The RBFNN-GA was applied to forecast streamflow at the High Aswan Dam on the Nile River. The results showed that the proposed model provided high accuracy. The GA algorithm can successfully determine effective input parameters in streamflow time series forecasting.