Multivariate Streamflow Simulation Using Hybrid Deep Learning Models

Reliable and accurate streamflow simulation has a vital role in water resource development, mainly in agriculture, environment, domestic water supply, hydropower generation, flood control, and early warning systems. In this context, these days, deep learning algorithms have got enormous attention du...

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Autores principales:	Eyob Betru Wegayehu, Fiseha Behulu Muluneh
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/3ef76632b4f14077abf2a56337d3a273
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spelling	oai:doaj.org-article:3ef76632b4f14077abf2a56337d3a2732021-11-08T02:36:24ZMultivariate Streamflow Simulation Using Hybrid Deep Learning Models1687-527310.1155/2021/5172658https://doaj.org/article/3ef76632b4f14077abf2a56337d3a2732021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5172658https://doaj.org/toc/1687-5273Reliable and accurate streamflow simulation has a vital role in water resource development, mainly in agriculture, environment, domestic water supply, hydropower generation, flood control, and early warning systems. In this context, these days, deep learning algorithms have got enormous attention due to their high-performance simulation capacity. In this study, we compared multilayer perceptron (MLP), long short-term memory (LSTM), and gated recurrent unit (GRU) with the proposed new hybrid models, including CNN-LSTM and CNN-GRU. Hence, we can simulate one-step daily streamflow in different agroclimatic conditions, rolling time windows, and a range of variable input combinations. The analysis used daily multivariate and multisite time series data collected from Awash River Basin (Borkena watershed: Ethiopia) and Tiber River Basin (Upper Tiber River Basin: Italy) stations. The datasets were subjected to rigorous quality control processes. Consequently, it rolled to a different time lag to remove noise in the time series and further split into training and testing datasets using a ratio of 80 : 20, respectively. Finally, the results showed that integrating the GRU layer with the convolutional layer and using monthly rolled average daily input time series could substantially improve the simulation of streamflow time series.Eyob Betru WegayehuFiseha Behulu MulunehHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Eyob Betru Wegayehu Fiseha Behulu Muluneh Multivariate Streamflow Simulation Using Hybrid Deep Learning Models
description	Reliable and accurate streamflow simulation has a vital role in water resource development, mainly in agriculture, environment, domestic water supply, hydropower generation, flood control, and early warning systems. In this context, these days, deep learning algorithms have got enormous attention due to their high-performance simulation capacity. In this study, we compared multilayer perceptron (MLP), long short-term memory (LSTM), and gated recurrent unit (GRU) with the proposed new hybrid models, including CNN-LSTM and CNN-GRU. Hence, we can simulate one-step daily streamflow in different agroclimatic conditions, rolling time windows, and a range of variable input combinations. The analysis used daily multivariate and multisite time series data collected from Awash River Basin (Borkena watershed: Ethiopia) and Tiber River Basin (Upper Tiber River Basin: Italy) stations. The datasets were subjected to rigorous quality control processes. Consequently, it rolled to a different time lag to remove noise in the time series and further split into training and testing datasets using a ratio of 80 : 20, respectively. Finally, the results showed that integrating the GRU layer with the convolutional layer and using monthly rolled average daily input time series could substantially improve the simulation of streamflow time series.
format	article
author	Eyob Betru Wegayehu Fiseha Behulu Muluneh
author_facet	Eyob Betru Wegayehu Fiseha Behulu Muluneh
author_sort	Eyob Betru Wegayehu
title	Multivariate Streamflow Simulation Using Hybrid Deep Learning Models
title_short	Multivariate Streamflow Simulation Using Hybrid Deep Learning Models
title_full	Multivariate Streamflow Simulation Using Hybrid Deep Learning Models
title_fullStr	Multivariate Streamflow Simulation Using Hybrid Deep Learning Models
title_full_unstemmed	Multivariate Streamflow Simulation Using Hybrid Deep Learning Models
title_sort	multivariate streamflow simulation using hybrid deep learning models
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/3ef76632b4f14077abf2a56337d3a273
work_keys_str_mv	AT eyobbetruwegayehu multivariatestreamflowsimulationusinghybriddeeplearningmodels AT fisehabehulumuluneh multivariatestreamflowsimulationusinghybriddeeplearningmodels
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Multivariate Streamflow Simulation Using Hybrid Deep Learning Models

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