Optimizing the performance of rainflow-runoff models using multi-source satellite data-application to the region of Beni-Bahdel (Algeria)

Chahrouri, Hiba; Bemmoussat, Abdelkader; Korichi, Khaled

doi:10.12912/27197050/199448

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Tytuł artykułu

Optimizing the performance of rainflow-runoff models using multi-source satellite data-application to the region of Beni-Bahdel (Algeria)

Autorzy

Chahrouri Hiba , Bemmoussat Abdelkader , Korichi Khaled

Treść / Zawartość

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32_chahrouri_optimizing_the_eeet_2_2025.pdf

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DOI

10.12912/27197050/199448

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Języki publikacji

Abstrakty

The integration of satellite data in rainfall-runoff simulation is of paramount importance in regions where data are limited and not easily available. The data used in this study, concerning the daily precipitation and daily runoff during the period extending from 2007 to 2015, were measured and recorded in situ. This study aims primarily to optimize the performance of the modeling carried out, using data from various satellite sources in order to complete the missing data, and consequently to predict the liquid flow in the Beni Bahdel watershed, in the northwestern region of Algeria, by applying the long short-term memory (LSTM) learning model. It is important to know that the optimization of rainfall-runoff modeling is based on the use of satellite data relating to evapotranspiration, mean temperatures, minimum and maximum temperatures, net radiation, wind speed, and relative humidity. These data come from the NOAA CPC, ERA, ERA5_AG, ERA5-Land, GLDAS, CFSR and MERRA2 satellites. In addition, two statistical indicators were calculated to perform this optimization that is based on the LSTM approach that integrates remote sensing data, the coefficient of determination (R²), and the Nash-Sutcliffe efficiency (NSE). Thus, a performance difference of about 0.30 was observed, for the NSE and R² coefficients, between the CFSR temperature data (NSE of 0.61and R² of 0.61) and the maximum and minimum temperature data from the ERA5-LAND and ERA5 satellite sources (0.92 for NSE and 0.93 for R²), for the validation period. This significant difference suggests that the use of the minimum and maximum temperature data from the ERA5-LAND source allows achieving a rainfall-runoff modeling wih optimal performance. Indeed, the findings showed that quite high performances were achieved for the calibration period (0.93 for NSE and 0.95 for R²) and for the validation period (0.92 for NSE and 0.93 for R²).

Słowa kluczowe

optimization modeling satellite data long short-term memory Beni Bahdel Algeria

Wydawca

Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej
Lublin University of Technology
WNGB Wydawnictwo Naukowe

Czasopismo

Ecological Engineering & Environmental Technology

Rocznik

2025

Tom

Vol. 26, iss. 2

Strony

407--419

Opis fizyczny

Bibliogr. 58 poz., rys., tab.

Twórcy

autor

Chahrouri Hiba

Civil and Environmental Engineering Laboratory, University of Djillali Liabes, BP 89. DZ-22000, Sidi Bel Abbés, Algeria

https://orcid.org/0009-0002-0881-2559

autor

Bemmoussat Abdelkader

bemmoussaek@yahoo.fr

Civil and Environmental Engineering Laboratory, University of Djillali Liabes, BP 89. DZ-22000, Sidi Bel Abbés, Algeria

https://orcid.org/0000-0002-8110-2054

autor

Korichi Khaled

Civil and Environmental Engineering Laboratory, University of Djillali Liabes, BP 89. DZ-22000, Sidi Bel Abbés, Algeria

https://orcid.org/0000-0002-2663-2757

Bibliografia

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