Enhancing Rainfall-Runoff Simulation via Meteorological Variables and a Deep-Conceptual Learning-Based Framework
Accurate streamflow simulation is crucial for many applications, such as optimal reservoir operation and irrigation. Conceptual techniques employ physical ideas and are suitable for representing the physics of the hydrologic model, but they might fail in competition with their more advanced counterp...
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2022
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ftdoajarticles:oai:doaj.org/article:4fad12333eb5487eab5071338f168203 2023-05-15T17:45:08+02:00 Enhancing Rainfall-Runoff Simulation via Meteorological Variables and a Deep-Conceptual Learning-Based Framework Mohammed Achite Babak Mohammadi Muhammad Jehanzaib Nehal Elshaboury Quoc Bao Pham Zheng Duan 2022-10-01T00:00:00Z https://doi.org/10.3390/atmos13101688 https://doaj.org/article/4fad12333eb5487eab5071338f168203 EN eng MDPI AG https://www.mdpi.com/2073-4433/13/10/1688 https://doaj.org/toc/2073-4433 doi:10.3390/atmos13101688 2073-4433 https://doaj.org/article/4fad12333eb5487eab5071338f168203 Atmosphere, Vol 13, Iss 1688, p 1688 (2022) conceptual hydrological model deep learning model runoff simulation data-driven model Meteorology. Climatology QC851-999 article 2022 ftdoajarticles https://doi.org/10.3390/atmos13101688 2022-12-30T20:29:26Z Accurate streamflow simulation is crucial for many applications, such as optimal reservoir operation and irrigation. Conceptual techniques employ physical ideas and are suitable for representing the physics of the hydrologic model, but they might fail in competition with their more advanced counterparts. In contrast, deep learning (DL) approaches provide a great computational capability for streamflow simulation, but they rely on data characteristics and the physics of the issue cannot be fully understood. To overcome these limitations, the current study provided a novel framework based on a combination of conceptual and DL techniques for enhancing the accuracy of streamflow simulation in a snow-covered basin. In this regard, the current study simulated daily streamflow in the Kalixälven river basin in northern Sweden by integrating a snow-based conceptual hydrological model (MISD) with a DL model. Daily precipitation, air temperature (average, minimum, and maximum), dew point temperature, evapotranspiration, relative humidity, sunshine duration, global solar radiation, and atmospheric pressure data were used as inputs for the DL model to examine the effect of each meteorological variable on the streamflow simulation. Results proved that adding meteorological variables to the conceptual hydrological model underframe of parallel settings can improve the accuracy of streamflow simulating by the DL model. The MISD model simulated streamflow had an MAE = 8.33 (cms), r = 0.88, and NSE = 0.77 for the validation phase. The proposed deep-conceptual learning-based framework also performed better than the standalone MISD model; the DL method had an MAE = 7.89 (cms), r = 0.90, and NSE = 0.80 for the validation phase when meteorological variables and MISD results were combined as inputs for the DL model. The integrated rainfall-runoff model proposed in this research is a new concept in rainfall-runoff modeling which can be used for accurate streamflow simulations. Article in Journal/Newspaper Northern Sweden Directory of Open Access Journals: DOAJ Articles Kalixälven ENVELOPE(23.217,23.217,65.783,65.783) Atmosphere 13 10 1688 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
conceptual hydrological model deep learning model runoff simulation data-driven model Meteorology. Climatology QC851-999 |
spellingShingle |
conceptual hydrological model deep learning model runoff simulation data-driven model Meteorology. Climatology QC851-999 Mohammed Achite Babak Mohammadi Muhammad Jehanzaib Nehal Elshaboury Quoc Bao Pham Zheng Duan Enhancing Rainfall-Runoff Simulation via Meteorological Variables and a Deep-Conceptual Learning-Based Framework |
topic_facet |
conceptual hydrological model deep learning model runoff simulation data-driven model Meteorology. Climatology QC851-999 |
description |
Accurate streamflow simulation is crucial for many applications, such as optimal reservoir operation and irrigation. Conceptual techniques employ physical ideas and are suitable for representing the physics of the hydrologic model, but they might fail in competition with their more advanced counterparts. In contrast, deep learning (DL) approaches provide a great computational capability for streamflow simulation, but they rely on data characteristics and the physics of the issue cannot be fully understood. To overcome these limitations, the current study provided a novel framework based on a combination of conceptual and DL techniques for enhancing the accuracy of streamflow simulation in a snow-covered basin. In this regard, the current study simulated daily streamflow in the Kalixälven river basin in northern Sweden by integrating a snow-based conceptual hydrological model (MISD) with a DL model. Daily precipitation, air temperature (average, minimum, and maximum), dew point temperature, evapotranspiration, relative humidity, sunshine duration, global solar radiation, and atmospheric pressure data were used as inputs for the DL model to examine the effect of each meteorological variable on the streamflow simulation. Results proved that adding meteorological variables to the conceptual hydrological model underframe of parallel settings can improve the accuracy of streamflow simulating by the DL model. The MISD model simulated streamflow had an MAE = 8.33 (cms), r = 0.88, and NSE = 0.77 for the validation phase. The proposed deep-conceptual learning-based framework also performed better than the standalone MISD model; the DL method had an MAE = 7.89 (cms), r = 0.90, and NSE = 0.80 for the validation phase when meteorological variables and MISD results were combined as inputs for the DL model. The integrated rainfall-runoff model proposed in this research is a new concept in rainfall-runoff modeling which can be used for accurate streamflow simulations. |
format |
Article in Journal/Newspaper |
author |
Mohammed Achite Babak Mohammadi Muhammad Jehanzaib Nehal Elshaboury Quoc Bao Pham Zheng Duan |
author_facet |
Mohammed Achite Babak Mohammadi Muhammad Jehanzaib Nehal Elshaboury Quoc Bao Pham Zheng Duan |
author_sort |
Mohammed Achite |
title |
Enhancing Rainfall-Runoff Simulation via Meteorological Variables and a Deep-Conceptual Learning-Based Framework |
title_short |
Enhancing Rainfall-Runoff Simulation via Meteorological Variables and a Deep-Conceptual Learning-Based Framework |
title_full |
Enhancing Rainfall-Runoff Simulation via Meteorological Variables and a Deep-Conceptual Learning-Based Framework |
title_fullStr |
Enhancing Rainfall-Runoff Simulation via Meteorological Variables and a Deep-Conceptual Learning-Based Framework |
title_full_unstemmed |
Enhancing Rainfall-Runoff Simulation via Meteorological Variables and a Deep-Conceptual Learning-Based Framework |
title_sort |
enhancing rainfall-runoff simulation via meteorological variables and a deep-conceptual learning-based framework |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/atmos13101688 https://doaj.org/article/4fad12333eb5487eab5071338f168203 |
long_lat |
ENVELOPE(23.217,23.217,65.783,65.783) |
geographic |
Kalixälven |
geographic_facet |
Kalixälven |
genre |
Northern Sweden |
genre_facet |
Northern Sweden |
op_source |
Atmosphere, Vol 13, Iss 1688, p 1688 (2022) |
op_relation |
https://www.mdpi.com/2073-4433/13/10/1688 https://doaj.org/toc/2073-4433 doi:10.3390/atmos13101688 2073-4433 https://doaj.org/article/4fad12333eb5487eab5071338f168203 |
op_doi |
https://doi.org/10.3390/atmos13101688 |
container_title |
Atmosphere |
container_volume |
13 |
container_issue |
10 |
container_start_page |
1688 |
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1766147909330403328 |