Historical hydrometric context and explainable multi-horizon evaluation of a GloFAS-derived discharge product near Babahoyo, Ecuador
Open global hydrological products are often the only continuous source available for data-scarce locations, but their local use is stronger when the modelled signal is connected to documented hydrometric evidence. This study evaluates an explainable machine-learning workflow for anticipating the Glo...
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| Lenguaje: | inglés |
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2026
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| author | Alexander Haro-Sarango Silvia Cachay-Salcedo Jessica Karina Saavedra-Vasconez Rosa Salcedo-Dávalos Elizabeth Proaño-Altamirano |
| author_browse | Alexander Haro-Sarango Elizabeth Proaño-Altamirano Jessica Karina Saavedra-Vasconez Rosa Salcedo-Dávalos Silvia Cachay-Salcedo |
| author_facet | Alexander Haro-Sarango Silvia Cachay-Salcedo Jessica Karina Saavedra-Vasconez Rosa Salcedo-Dávalos Elizabeth Proaño-Altamirano |
| author_sort | Alexander Haro-Sarango |
| collection | Producción científica |
| description | Open global hydrological products are often the only continuous source available for data-scarce locations, but their local use is stronger when the modelled signal is connected to documented hydrometric evidence. This study evaluates an explainable machine-learning workflow for anticipating the GloFAS v4-derived discharge series returned by the Open-Meteo Flood API near Babahoyo, Ecuador, and complements the modern 2017–2026 analysis with a historical hydrometric-context protocol based on official INAMHI yearbooks for the San Pablo-Babahoyo system. Direct models were assessed at t+1, t+3, t+7, and t+14 using leakage-aware features, rolling-origin backtesting, a 365-day holdout, prediction intervals, product-state classification, permutation importance, and SHAP. ElasticNetCV achieved R 2 /NSE of 0.900 at t+1 in the final holdout, while mean backtest skill weakened at longer horizons. The dominant role of current and recent discharge values shows that the main short-term contribution is efficient local post-processing of GloFAS persistence; meteorological and seasonal information becomes relatively more important as lead time increases. The study therefore contributes a transparent multi-horizon evaluation, an auditable pathway for historical gauge-level corroboration, and practical diagnostics for deciding when local emulation is informative and when the native global product should remain the primary reference. |
| format | Article |
| id | openalex:W7203865128 |
| institution | UPeU |
| language | English |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| record_format | dspace |
| spelling | openalex:W7203865128Historical hydrometric context and explainable multi-horizon evaluation of a GloFAS-derived discharge product near Babahoyo, EcuadorAlexander Haro-SarangoSilvia Cachay-SalcedoJessica Karina Saavedra-VasconezRosa Salcedo-DávalosElizabeth Proaño-AltamiranoGloFASriver discharge predictionexplainable machine learningSHAPpermutation importancebacktestingdata-scarce regionsBabahoyolead timeHydrological Forecasting Using AIHydrology and Watershed Management StudiesEnvironmental Monitoring and Data ManagementOpen global hydrological products are often the only continuous source available for data-scarce locations, but their local use is stronger when the modelled signal is connected to documented hydrometric evidence. This study evaluates an explainable machine-learning workflow for anticipating the GloFAS v4-derived discharge series returned by the Open-Meteo Flood API near Babahoyo, Ecuador, and complements the modern 2017–2026 analysis with a historical hydrometric-context protocol based on official INAMHI yearbooks for the San Pablo-Babahoyo system. Direct models were assessed at t+1, t+3, t+7, and t+14 using leakage-aware features, rolling-origin backtesting, a 365-day holdout, prediction intervals, product-state classification, permutation importance, and SHAP. ElasticNetCV achieved R 2 /NSE of 0.900 at t+1 in the final holdout, while mean backtest skill weakened at longer horizons. The dominant role of current and recent discharge values shows that the main short-term contribution is efficient local post-processing of GloFAS persistence; meteorological and seasonal information becomes relatively more important as lead time increases. The study therefore contributes a transparent multi-horizon evaluation, an auditable pathway for historical gauge-level corroboration, and practical diagnostics for deciding when local emulation is informative and when the native global product should remain the primary reference.Articlehttps://doi.org/10.3389/frai.2026.1901591https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1901591/pdfW7203865128enFrontiers in Artificial Intelligence2624-82122026-08-21 |
| spellingShingle | GloFAS river discharge prediction explainable machine learning SHAP permutation importance backtesting data-scarce regions Babahoyo lead time Hydrological Forecasting Using AI Hydrology and Watershed Management Studies Environmental Monitoring and Data Management Alexander Haro-Sarango Silvia Cachay-Salcedo Jessica Karina Saavedra-Vasconez Rosa Salcedo-Dávalos Elizabeth Proaño-Altamirano Historical hydrometric context and explainable multi-horizon evaluation of a GloFAS-derived discharge product near Babahoyo, Ecuador |
| title | Historical hydrometric context and explainable multi-horizon evaluation of a GloFAS-derived discharge product near Babahoyo, Ecuador |
| title_full | Historical hydrometric context and explainable multi-horizon evaluation of a GloFAS-derived discharge product near Babahoyo, Ecuador |
| title_fullStr | Historical hydrometric context and explainable multi-horizon evaluation of a GloFAS-derived discharge product near Babahoyo, Ecuador |
| title_full_unstemmed | Historical hydrometric context and explainable multi-horizon evaluation of a GloFAS-derived discharge product near Babahoyo, Ecuador |
| title_short | Historical hydrometric context and explainable multi-horizon evaluation of a GloFAS-derived discharge product near Babahoyo, Ecuador |
| title_sort | historical hydrometric context and explainable multi horizon evaluation of a glofas derived discharge product near babahoyo ecuador |
| topic | GloFAS river discharge prediction explainable machine learning SHAP permutation importance backtesting data-scarce regions Babahoyo lead time Hydrological Forecasting Using AI Hydrology and Watershed Management Studies Environmental Monitoring and Data Management |
| topic_facet | GloFAS river discharge prediction explainable machine learning SHAP permutation importance backtesting data-scarce regions Babahoyo lead time Hydrological Forecasting Using AI Hydrology and Watershed Management Studies Environmental Monitoring and Data Management |
| url | https://doi.org/10.3389/frai.2026.1901591 https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1901591/pdf |
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