Enhancing streamflow forecasting using the augmenting ensemble procedure coupled machine learning models: case study of Aswan High Dam

Rezaie-Balf, Mohammad and Naganna, Sujay Raghavendra and Kisi, Ozgur and El-Shafie, Ahmed (2019) Enhancing streamflow forecasting using the augmenting ensemble procedure coupled machine learning models: case study of Aswan High Dam. Hydrological Sciences Journal, 64 (13). pp. 1629-1646. ISSN 0262-6667

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Official URL: https://doi.org/10.1080/02626667.2019.1661417

Abstract

The potential of the most recent pre-processing tool, namely, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), is examined for providing AI models (artificial neural network, ANN; M5-model tree, M5-MT; and multivariate adaptive regression spline, MARS) with more informative input–output data and, thence, evaluate their forecasting accuracy. A 130-year inflow dataset for Aswan High Dam, Egypt, is considered for training, validating and testing the proposed models to forecast the reservoir inflow up to six months ahead. The results show that, after the pre-processing analysis, there is a significant enhancement in the forecasting accuracy. The MARS model combined with CEEMDAN gave superior performance compared to the other models–CEEMDAN-ANN and CEEMDAN-M5-MT–with an increase in accuracy of, respectively, about 13–25% and 6–20% in terms of the root mean square error. © 2019, © 2019 IAHS.

Item Type: Article
Uncontrolled Keywords: multi-lead-time forecasting; reservoir inflow forecasting; Aswan High Dam; pre-processing analysis; CEEMDAN
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 03 Feb 2020 03:24
Last Modified: 03 Feb 2020 03:24
URI: http://eprints.um.edu.my/id/eprint/23647

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