Runoff Predictions in a Semiarid Watershed by Convolutional Neural Networks Improved with Metaheuristic Algorithms and Forced with Reanalysis and Climate Data
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Volume 28, Issue 7
Abstract
In this research, the role of climate variability and weather change in short-term streamflows, including extreme event, was investigated in semiarid climates. The deep learning convolutional neural networks (CNN) were modified by incorporating the imperialist competitive algorithm (ICA) and the grey wolf optimizer (GWO) method to improve hourly runoff predictions at multiple scales, ranging from 100 to over in the Seybouse Basin, Algeria. The atmospheric reanalysis data set, ECMWF Reanalysis v5 (ERA5) with a 31-km resolution, climate variability indices, and in situ runoff observations were used. The most relevant atmospheric and soil moisture predictors from the reanalysis grids covering the study area were used to represent spatial variability. The prediction performance of the original CNN and modified CNN-ICA and CNN-GWO models were evaluated. The CNN-GWO model outperformed CNN-ICA and the original model in predicting runoff and improved the Nash-Sutcliffe Efficiency score up to 0.99. Results across multiple scales disclose that the models with climate indices as inputs showed higher performance than the models with only atmospheric data as inputs, especially in predicting extreme runoff values in basins with elevations above 670 m, suggesting that climate variability indices need to be considered in flood predictions and infrastructure design in mountainous areas with increasing climate change uncertainties.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. The sea surface temperature data were obtained from https://www.cpc.ncep.noaa.gov/data/indices/sstoi.indices.
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Journal of Hydrologic Engineering
Volume 28 • Issue 7 • July 2023
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© 2023 American Society of Civil Engineers.
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Received: Sep 21, 2022
Accepted: Jan 9, 2023
Published online: Apr 19, 2023
Published in print: Jul 1, 2023
Discussion open until: Sep 19, 2023
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