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Comparative Evaluation of Deep Learning Techniques in Streamflow Monthly Prediction of the Zarrine River Basin

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F24%3A00011631" target="_blank" >RIV/46747885:24220/24:00011631 - isvavai.cz</a>

  • Alternative codes found

    RIV/46747885:24620/24:00011631

  • Result on the web

    <a href="https://www.mdpi.com/2073-4441/16/2/208" target="_blank" >https://www.mdpi.com/2073-4441/16/2/208</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3390/w16020208" target="_blank" >10.3390/w16020208</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Comparative Evaluation of Deep Learning Techniques in Streamflow Monthly Prediction of the Zarrine River Basin

  • Original language description

    Predicting monthly streamflow is essential for hydrological analysis and water resource management. Recent advancements in deep learning, particularly long short-term memory (LSTM) and recurrent neural networks (RNN), exhibit extraordinary efficacy in streamflow forecasting. This study employs RNN and LSTM to construct data-driven streamflow forecasting models. Sensitivity analysis, utilizing the analysis of variance (ANOVA) method, also is crucial for model refinement and identification of critical variables. This study covers monthly streamflow data from 1979 to 2014, employing five distinct model structures to ascertain the most optimal configuration. Application of the models to the Zarrine River basin in northwest Iran, a major sub-basin of Lake Urmia, demonstrates the superior accuracy of the RNN algorithm over LSTM. At the outlet of the basin, quantitative evaluations demonstrate that the RNN model outperforms the LSTM model across all model structures. The S3 model, characterized by its inclusion of all input variable values and a four-month delay, exhibits notably exceptional performance in this aspect. The accuracy measures applicable in this particular context were RMSE (22.8), R2 (0.84), and NSE (0.8). This study highlights the Zarrine River’s substantial impact on variations in Lake Urmia’s water level. Furthermore, the ANOVA method demonstrates exceptional performance in discerning the relevance of input factors. ANOVA underscores the key role of station streamflow, upstream station streamflow, and maximum temperature in influencing the model’s output. Notably, the RNN model, surpassing LSTM and traditional artificial neural network (ANN) models, excels in accurately mimicking rainfall–runoff processes. This emphasizes the potential of RNN networks to filter redundant information, distinguishing them as valuable tools in monthly streamflow forecasting.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10511 - Environmental sciences (social aspects to be 5.7)

Result continuities

  • Project

    <a href="/en/project/LM2023066" target="_blank" >LM2023066: Nanomaterials and Nanotechnologies for Environment Protection and Sustainable Future</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2024

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Water

  • ISSN

    2073-4441

  • e-ISSN

  • Volume of the periodical

    16

  • Issue of the periodical within the volume

    2

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    21

  • Pages from-to

  • UT code for WoS article

    001150860000001

  • EID of the result in the Scopus database

    2-s2.0-85183323957