eprintid: 10185812 rev_number: 6 eprint_status: archive userid: 699 dir: disk0/10/18/58/12 datestamp: 2024-01-19 09:35:59 lastmod: 2024-01-19 09:36:00 status_changed: 2024-01-19 09:35:59 type: article metadata_visibility: show sword_depositor: 699 creators_name: Nakhaei, Mahdi creators_name: Zanjanian, Hossein creators_name: Nakhaei, Pouria creators_name: Gheibi, Mohammad creators_name: Moezzi, Reza creators_name: Behzadian, Kourosh creators_name: Campos, Luiza C title: Comparative Evaluation of Deep Learning Techniques in Streamflow Monthly Prediction of the Zarrine River Basin ispublished: pub divisions: UCL divisions: B04 divisions: C05 divisions: F44 keywords: LSTM; RNN; ANOVA; input data sensitivity analysis; Zarrine River; precipitation note: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). abstract: 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. date: 2024-01-06 date_type: published publisher: MDPI AG official_url: http://dx.doi.org/10.3390/w16020208 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2140431 doi: 10.3390/w16020208 lyricists_name: Campos, Luiza lyricists_id: LCCAM91 actors_name: Flynn, Bernadette actors_id: BFFLY94 actors_role: owner full_text_status: public publication: Water volume: 16 number: 2 article_number: 208 issn: 2073-4441 citation: Nakhaei, Mahdi; Zanjanian, Hossein; Nakhaei, Pouria; Gheibi, Mohammad; Moezzi, Reza; Behzadian, Kourosh; Campos, Luiza C; (2024) Comparative Evaluation of Deep Learning Techniques in Streamflow Monthly Prediction of the Zarrine River Basin. Water , 16 (2) , Article 208. 10.3390/w16020208 <https://doi.org/10.3390/w16020208>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10185812/1/water-16-00208.pdf