Real-time streamflow forecasting: AI vs. Hydrologic insights

In this paper, we propose a set of simple benchmarks for the evaluation of data-based models for real-time streamflow forecasting, such as those developed with sophisticated Artificial Intelligence (AI) algorithms. The benchmarks are also data-based and provide context to judge incremental improveme...

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Autores principales:	Witold F. Krajewski, Ganesh R. Ghimire, Ibrahim Demir, Ricardo Mantilla
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Real-time streamflow forecasting Artificial intelligence Hydrologic insights Benchmarking Persistence Iowa Environmental engineering TA170-171 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/4c2cfbdae0dd4b09a187c30eb60680c1
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id	oai:doaj.org-article:4c2cfbdae0dd4b09a187c30eb60680c1
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spelling	oai:doaj.org-article:4c2cfbdae0dd4b09a187c30eb60680c12021-11-30T04:17:31ZReal-time streamflow forecasting: AI vs. Hydrologic insights2589-915510.1016/j.hydroa.2021.100110https://doaj.org/article/4c2cfbdae0dd4b09a187c30eb60680c12021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2589915521000389https://doaj.org/toc/2589-9155In this paper, we propose a set of simple benchmarks for the evaluation of data-based models for real-time streamflow forecasting, such as those developed with sophisticated Artificial Intelligence (AI) algorithms. The benchmarks are also data-based and provide context to judge incremental improvements in the performance metrics from the more complicated approaches. The benchmarks include temporal and spatial persistence, persistence corrected for baseflow and streamflow, as well as river distance weighted runoff obtained from space-time distributed rainfall. In the development of the benchmarks, we use basic hydrologic insights such as flow aggregation by the river network, scale-dependence in basin response, streamflow partitioning into quick flow and baseflow, water travel time, and rainfall averaging by the basin width function. The study uses 140 streamflow gauges in Iowa that cover a range of basin scales between 7 and 37,000 km2. The data cover 17 years. This work demonstrates that the proposed benchmarks can provide good performance according to several commonly used metrics. For example, streamflow forecasting at half of the test locations across years achieves a Kling-Gupta Efficiency (KGE) score of 0.6 or higher at one-day ahead lead time, and 20% of cases reach the KGE of 0.8 or higher. The proposed benchmarks are easy to implement and should prove useful for developers of data-based as well as physics-based hydrologic models and real-time data assimilation techniques.Witold F. KrajewskiGanesh R. GhimireIbrahim DemirRicardo MantillaElsevierarticleReal-time streamflow forecastingArtificial intelligenceHydrologic insightsBenchmarkingPersistenceIowaEnvironmental engineeringTA170-171Environmental sciencesGE1-350ENJournal of Hydrology X, Vol 13, Iss , Pp 100110- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Real-time streamflow forecasting Artificial intelligence Hydrologic insights Benchmarking Persistence Iowa Environmental engineering TA170-171 Environmental sciences GE1-350
spellingShingle	Real-time streamflow forecasting Artificial intelligence Hydrologic insights Benchmarking Persistence Iowa Environmental engineering TA170-171 Environmental sciences GE1-350 Witold F. Krajewski Ganesh R. Ghimire Ibrahim Demir Ricardo Mantilla Real-time streamflow forecasting: AI vs. Hydrologic insights
description	In this paper, we propose a set of simple benchmarks for the evaluation of data-based models for real-time streamflow forecasting, such as those developed with sophisticated Artificial Intelligence (AI) algorithms. The benchmarks are also data-based and provide context to judge incremental improvements in the performance metrics from the more complicated approaches. The benchmarks include temporal and spatial persistence, persistence corrected for baseflow and streamflow, as well as river distance weighted runoff obtained from space-time distributed rainfall. In the development of the benchmarks, we use basic hydrologic insights such as flow aggregation by the river network, scale-dependence in basin response, streamflow partitioning into quick flow and baseflow, water travel time, and rainfall averaging by the basin width function. The study uses 140 streamflow gauges in Iowa that cover a range of basin scales between 7 and 37,000 km2. The data cover 17 years. This work demonstrates that the proposed benchmarks can provide good performance according to several commonly used metrics. For example, streamflow forecasting at half of the test locations across years achieves a Kling-Gupta Efficiency (KGE) score of 0.6 or higher at one-day ahead lead time, and 20% of cases reach the KGE of 0.8 or higher. The proposed benchmarks are easy to implement and should prove useful for developers of data-based as well as physics-based hydrologic models and real-time data assimilation techniques.
format	article
author	Witold F. Krajewski Ganesh R. Ghimire Ibrahim Demir Ricardo Mantilla
author_facet	Witold F. Krajewski Ganesh R. Ghimire Ibrahim Demir Ricardo Mantilla
author_sort	Witold F. Krajewski
title	Real-time streamflow forecasting: AI vs. Hydrologic insights
title_short	Real-time streamflow forecasting: AI vs. Hydrologic insights
title_full	Real-time streamflow forecasting: AI vs. Hydrologic insights
title_fullStr	Real-time streamflow forecasting: AI vs. Hydrologic insights
title_full_unstemmed	Real-time streamflow forecasting: AI vs. Hydrologic insights
title_sort	real-time streamflow forecasting: ai vs. hydrologic insights
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/4c2cfbdae0dd4b09a187c30eb60680c1
work_keys_str_mv	AT witoldfkrajewski realtimestreamflowforecastingaivshydrologicinsights AT ganeshrghimire realtimestreamflowforecastingaivshydrologicinsights AT ibrahimdemir realtimestreamflowforecastingaivshydrologicinsights AT ricardomantilla realtimestreamflowforecastingaivshydrologicinsights
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Real-time streamflow forecasting: AI vs. Hydrologic insights

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