Data-Driven and Stochastic Approaches for Synthetic Inflow Simulation at the Ribb Embankment Dam, Tana Sub-Basin, Upper Blue Nile (Abay) Basin, Ethiopia

Data-Driven and Stochastic Approaches for Synthetic Inflow Simulation at the Ribb Embankment Dam, Tana Sub-Basin, Upper Blue Nile (Abay) Basin, Ethiopia | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 5 December 2025 V1 Latest version Share on Data-Driven and Stochastic Approaches for Synthetic Inflow Simulation at the Ribb Embankment Dam, Tana Sub-Basin, Upper Blue Nile (Abay) Basin, Ethiopia Authors : Adnan Arega Belay 0009-0009-1212-7897 , Bogale GebreMariam Neka , and Getachew Bereta Geremew [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176492174.47634614/v1 190 views 82 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This study presents a comprehensive evaluation of synthetic inflow generation for the Ribb Embankment Dam using Artificial Neural Networks (ANN) and the Thomas-Fiering Model (TFM). The analysis employs both daily and monthly datasets for the ANN model and monthly datasets for the TFM by enabling a comparative assessment of their performance across different temporal scales. The ANN model demonstrates strong predictive capabilities by achieving high accuracy on both daily and monthly scales with R 2 values ranging from 0.9045 to 0.931 for daily data and 0.8721 to 0.9248 for monthly data. The model effectively captures hydrological variability as evidenced by low error metrics MSE, RMSE, and MAE and consistent alignment between observed and simulated series in time series and scatter plots. In contrast, the TFM provides a robust stochastic benchmark, with R 2 values ranging from 0.802 to 0.871. And it demonstrating effectiveness in preserving monthly flow patterns. Comparative analysis highlights the superior performance of the ANN model in replicating both short- and long-term hydrological patterns. while the TFM maintains its value as a conventional statistical approach for monthly inflow simulation. The results are validated against historical inflow patterns from the dam’s design documentation. The outcome reinforcing the reliability of both models for hydrological modeling and water resource management. This study offers valuable insights into the application of data-driven and stochastic approaches for synthetic inflow generation, supporting effective decision-making in dam design and water resource planning. Supplementary Material File (obj_2 for submition_18.docx) Download 3.65 MB Information & Authors Information Version history V1 Version 1 05 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords ann embankment dam synthetic inflow simulation tfm Authors Affiliations Adnan Arega Belay 0009-0009-1212-7897 Arba Minch University View all articles by this author Bogale GebreMariam Neka Arba Minch University View all articles by this author Getachew Bereta Geremew [email protected] Arba Minch University View all articles by this author Metrics & Citations Metrics Article Usage 190 views 82 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Adnan Arega Belay, Bogale GebreMariam Neka, Getachew Bereta Geremew. Data-Driven and Stochastic Approaches for Synthetic Inflow Simulation at the Ribb Embankment Dam, Tana Sub-Basin, Upper Blue Nile (Abay) Basin, Ethiopia. Authorea . 05 December 2025. DOI: https://doi.org/10.22541/au.176492174.47634614/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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