Exploring the feasibility of Support Vector Machine for short-term hydrological forecasting in South Tyrol: challenges and prospects

Abstract Short-term hydrological forecasting is crucial for suitable multipurpose water resource management involving water uses, hydrological security, and renewable production. In the Alpine Regions such as South Tyrol, characterized by several small watersheds, quick information is essential to f...
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Autor*in:	Dalla Torre, Daniele [verfasserIn] Lombardi, Andrea Menapace, Andrea Zanfei, Ariele Righetti, Maurizio

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Short-term streamflow forecasting Data-driven pipeline Hydrological modelling Alpine region Water resource management

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: Discover applied sciences - Springer International Publishing, 2024, 6(2024), 4 vom: 22. März
Übergeordnetes Werk:	volume:6 ; year:2024 ; number:4 ; day:22 ; month:03

Links:	Volltext

DOI / URN:	10.1007/s42452-024-05819-z

Katalog-ID:	SPR055260470

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520			\|a Abstract Short-term hydrological forecasting is crucial for suitable multipurpose water resource management involving water uses, hydrological security, and renewable production. In the Alpine Regions such as South Tyrol, characterized by several small watersheds, quick information is essential to feed the decision processes in critical cases such as flood events. Predicting water availability ahead is equally crucial for optimizing resource utilization, such as irrigation or snow-making. The increasing data availability and computational power led to data-driven models becoming a serious alternative to physically based hydrological models, especially in complex conditions such as the Alpine Region and for short predictive horizons. This paper proposes a data-driven pipeline to use the local ground station data to infer information in a Support Vector Regression model, which can forecast streamflow in the main closure points of the area at hourly resolution with 48 h of lead time. The main steps of the pipeline are analysed and discussed, with promising results that depend on available information, watershed complexity, and human interactions in the catchment. The presented pipeline, as it stands, offers an accessible tool for integrating these models into decision-making processes to guarantee real-time streamflow information at several points of the hydrological network. Discussion enhances the potentialities, open challenges, and prospects of short-term streamflow forecasting to accommodate broader studies.
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allfields	10.1007/s42452-024-05819-z doi (DE-627)SPR055260470 (SPR)s42452-024-05819-z-e DE-627 ger DE-627 rakwb eng Dalla Torre, Daniele verfasserin aut Exploring the feasibility of Support Vector Machine for short-term hydrological forecasting in South Tyrol: challenges and prospects 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Short-term hydrological forecasting is crucial for suitable multipurpose water resource management involving water uses, hydrological security, and renewable production. In the Alpine Regions such as South Tyrol, characterized by several small watersheds, quick information is essential to feed the decision processes in critical cases such as flood events. Predicting water availability ahead is equally crucial for optimizing resource utilization, such as irrigation or snow-making. The increasing data availability and computational power led to data-driven models becoming a serious alternative to physically based hydrological models, especially in complex conditions such as the Alpine Region and for short predictive horizons. This paper proposes a data-driven pipeline to use the local ground station data to infer information in a Support Vector Regression model, which can forecast streamflow in the main closure points of the area at hourly resolution with 48 h of lead time. The main steps of the pipeline are analysed and discussed, with promising results that depend on available information, watershed complexity, and human interactions in the catchment. The presented pipeline, as it stands, offers an accessible tool for integrating these models into decision-making processes to guarantee real-time streamflow information at several points of the hydrological network. Discussion enhances the potentialities, open challenges, and prospects of short-term streamflow forecasting to accommodate broader studies. Highlights Data-driven approach offers viable alternatives to traditional hydrological models for short-term predictions.Support Vector Regression model results suitable for hydrological modelling also in complex Alpine Region.Data-driven pipeline can effectively bridge the gap between research and operational aspects of water management. Short-term streamflow forecasting (dpeaa)DE-He213 Data-driven pipeline (dpeaa)DE-He213 Hydrological modelling (dpeaa)DE-He213 Alpine region (dpeaa)DE-He213 Water resource management (dpeaa)DE-He213 Lombardi, Andrea aut Menapace, Andrea aut Zanfei, Ariele aut Righetti, Maurizio aut Enthalten in Discover applied sciences Springer International Publishing, 2024 6(2024), 4 vom: 22. März Online-Ressource (DE-627)1882945751 (DE-600)3181295-8 3004-9261 nnns volume:6 year:2024 number:4 day:22 month:03 https://dx.doi.org/10.1007/s42452-024-05819-z kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2024 4 22 03
spelling	10.1007/s42452-024-05819-z doi (DE-627)SPR055260470 (SPR)s42452-024-05819-z-e DE-627 ger DE-627 rakwb eng Dalla Torre, Daniele verfasserin aut Exploring the feasibility of Support Vector Machine for short-term hydrological forecasting in South Tyrol: challenges and prospects 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Short-term hydrological forecasting is crucial for suitable multipurpose water resource management involving water uses, hydrological security, and renewable production. In the Alpine Regions such as South Tyrol, characterized by several small watersheds, quick information is essential to feed the decision processes in critical cases such as flood events. Predicting water availability ahead is equally crucial for optimizing resource utilization, such as irrigation or snow-making. The increasing data availability and computational power led to data-driven models becoming a serious alternative to physically based hydrological models, especially in complex conditions such as the Alpine Region and for short predictive horizons. This paper proposes a data-driven pipeline to use the local ground station data to infer information in a Support Vector Regression model, which can forecast streamflow in the main closure points of the area at hourly resolution with 48 h of lead time. The main steps of the pipeline are analysed and discussed, with promising results that depend on available information, watershed complexity, and human interactions in the catchment. The presented pipeline, as it stands, offers an accessible tool for integrating these models into decision-making processes to guarantee real-time streamflow information at several points of the hydrological network. Discussion enhances the potentialities, open challenges, and prospects of short-term streamflow forecasting to accommodate broader studies. Highlights Data-driven approach offers viable alternatives to traditional hydrological models for short-term predictions.Support Vector Regression model results suitable for hydrological modelling also in complex Alpine Region.Data-driven pipeline can effectively bridge the gap between research and operational aspects of water management. Short-term streamflow forecasting (dpeaa)DE-He213 Data-driven pipeline (dpeaa)DE-He213 Hydrological modelling (dpeaa)DE-He213 Alpine region (dpeaa)DE-He213 Water resource management (dpeaa)DE-He213 Lombardi, Andrea aut Menapace, Andrea aut Zanfei, Ariele aut Righetti, Maurizio aut Enthalten in Discover applied sciences Springer International Publishing, 2024 6(2024), 4 vom: 22. März Online-Ressource (DE-627)1882945751 (DE-600)3181295-8 3004-9261 nnns volume:6 year:2024 number:4 day:22 month:03 https://dx.doi.org/10.1007/s42452-024-05819-z kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2024 4 22 03
allfields_unstemmed	10.1007/s42452-024-05819-z doi (DE-627)SPR055260470 (SPR)s42452-024-05819-z-e DE-627 ger DE-627 rakwb eng Dalla Torre, Daniele verfasserin aut Exploring the feasibility of Support Vector Machine for short-term hydrological forecasting in South Tyrol: challenges and prospects 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Short-term hydrological forecasting is crucial for suitable multipurpose water resource management involving water uses, hydrological security, and renewable production. In the Alpine Regions such as South Tyrol, characterized by several small watersheds, quick information is essential to feed the decision processes in critical cases such as flood events. Predicting water availability ahead is equally crucial for optimizing resource utilization, such as irrigation or snow-making. The increasing data availability and computational power led to data-driven models becoming a serious alternative to physically based hydrological models, especially in complex conditions such as the Alpine Region and for short predictive horizons. This paper proposes a data-driven pipeline to use the local ground station data to infer information in a Support Vector Regression model, which can forecast streamflow in the main closure points of the area at hourly resolution with 48 h of lead time. The main steps of the pipeline are analysed and discussed, with promising results that depend on available information, watershed complexity, and human interactions in the catchment. The presented pipeline, as it stands, offers an accessible tool for integrating these models into decision-making processes to guarantee real-time streamflow information at several points of the hydrological network. Discussion enhances the potentialities, open challenges, and prospects of short-term streamflow forecasting to accommodate broader studies. Highlights Data-driven approach offers viable alternatives to traditional hydrological models for short-term predictions.Support Vector Regression model results suitable for hydrological modelling also in complex Alpine Region.Data-driven pipeline can effectively bridge the gap between research and operational aspects of water management. Short-term streamflow forecasting (dpeaa)DE-He213 Data-driven pipeline (dpeaa)DE-He213 Hydrological modelling (dpeaa)DE-He213 Alpine region (dpeaa)DE-He213 Water resource management (dpeaa)DE-He213 Lombardi, Andrea aut Menapace, Andrea aut Zanfei, Ariele aut Righetti, Maurizio aut Enthalten in Discover applied sciences Springer International Publishing, 2024 6(2024), 4 vom: 22. März Online-Ressource (DE-627)1882945751 (DE-600)3181295-8 3004-9261 nnns volume:6 year:2024 number:4 day:22 month:03 https://dx.doi.org/10.1007/s42452-024-05819-z kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2024 4 22 03
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allfieldsSound	10.1007/s42452-024-05819-z doi (DE-627)SPR055260470 (SPR)s42452-024-05819-z-e DE-627 ger DE-627 rakwb eng Dalla Torre, Daniele verfasserin aut Exploring the feasibility of Support Vector Machine for short-term hydrological forecasting in South Tyrol: challenges and prospects 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Abstract Short-term hydrological forecasting is crucial for suitable multipurpose water resource management involving water uses, hydrological security, and renewable production. In the Alpine Regions such as South Tyrol, characterized by several small watersheds, quick information is essential to feed the decision processes in critical cases such as flood events. Predicting water availability ahead is equally crucial for optimizing resource utilization, such as irrigation or snow-making. The increasing data availability and computational power led to data-driven models becoming a serious alternative to physically based hydrological models, especially in complex conditions such as the Alpine Region and for short predictive horizons. This paper proposes a data-driven pipeline to use the local ground station data to infer information in a Support Vector Regression model, which can forecast streamflow in the main closure points of the area at hourly resolution with 48 h of lead time. The main steps of the pipeline are analysed and discussed, with promising results that depend on available information, watershed complexity, and human interactions in the catchment. The presented pipeline, as it stands, offers an accessible tool for integrating these models into decision-making processes to guarantee real-time streamflow information at several points of the hydrological network. Discussion enhances the potentialities, open challenges, and prospects of short-term streamflow forecasting to accommodate broader studies. Highlights Data-driven approach offers viable alternatives to traditional hydrological models for short-term predictions.Support Vector Regression model results suitable for hydrological modelling also in complex Alpine Region.Data-driven pipeline can effectively bridge the gap between research and operational aspects of water management. Short-term streamflow forecasting (dpeaa)DE-He213 Data-driven pipeline (dpeaa)DE-He213 Hydrological modelling (dpeaa)DE-He213 Alpine region (dpeaa)DE-He213 Water resource management (dpeaa)DE-He213 Lombardi, Andrea aut Menapace, Andrea aut Zanfei, Ariele aut Righetti, Maurizio aut Enthalten in Discover applied sciences Springer International Publishing, 2024 6(2024), 4 vom: 22. März Online-Ressource (DE-627)1882945751 (DE-600)3181295-8 3004-9261 nnns volume:6 year:2024 number:4 day:22 month:03 https://dx.doi.org/10.1007/s42452-024-05819-z kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 6 2024 4 22 03
language	English
source	Enthalten in Discover applied sciences 6(2024), 4 vom: 22. März volume:6 year:2024 number:4 day:22 month:03
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topic_facet	Short-term streamflow forecasting Data-driven pipeline Hydrological modelling Alpine region Water resource management
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author	Dalla Torre, Daniele
spellingShingle	Dalla Torre, Daniele misc Short-term streamflow forecasting misc Data-driven pipeline misc Hydrological modelling misc Alpine region misc Water resource management Exploring the feasibility of Support Vector Machine for short-term hydrological forecasting in South Tyrol: challenges and prospects
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topic_title	Exploring the feasibility of Support Vector Machine for short-term hydrological forecasting in South Tyrol: challenges and prospects Short-term streamflow forecasting (dpeaa)DE-He213 Data-driven pipeline (dpeaa)DE-He213 Hydrological modelling (dpeaa)DE-He213 Alpine region (dpeaa)DE-He213 Water resource management (dpeaa)DE-He213
topic	misc Short-term streamflow forecasting misc Data-driven pipeline misc Hydrological modelling misc Alpine region misc Water resource management
topic_unstemmed	misc Short-term streamflow forecasting misc Data-driven pipeline misc Hydrological modelling misc Alpine region misc Water resource management
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title	Exploring the feasibility of Support Vector Machine for short-term hydrological forecasting in South Tyrol: challenges and prospects
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title_full	Exploring the feasibility of Support Vector Machine for short-term hydrological forecasting in South Tyrol: challenges and prospects
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author_browse	Dalla Torre, Daniele Lombardi, Andrea Menapace, Andrea Zanfei, Ariele Righetti, Maurizio
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title_sort	exploring the feasibility of support vector machine for short-term hydrological forecasting in south tyrol: challenges and prospects
title_auth	Exploring the feasibility of Support Vector Machine for short-term hydrological forecasting in South Tyrol: challenges and prospects
abstract	Abstract Short-term hydrological forecasting is crucial for suitable multipurpose water resource management involving water uses, hydrological security, and renewable production. In the Alpine Regions such as South Tyrol, characterized by several small watersheds, quick information is essential to feed the decision processes in critical cases such as flood events. Predicting water availability ahead is equally crucial for optimizing resource utilization, such as irrigation or snow-making. The increasing data availability and computational power led to data-driven models becoming a serious alternative to physically based hydrological models, especially in complex conditions such as the Alpine Region and for short predictive horizons. This paper proposes a data-driven pipeline to use the local ground station data to infer information in a Support Vector Regression model, which can forecast streamflow in the main closure points of the area at hourly resolution with 48 h of lead time. The main steps of the pipeline are analysed and discussed, with promising results that depend on available information, watershed complexity, and human interactions in the catchment. The presented pipeline, as it stands, offers an accessible tool for integrating these models into decision-making processes to guarantee real-time streamflow information at several points of the hydrological network. Discussion enhances the potentialities, open challenges, and prospects of short-term streamflow forecasting to accommodate broader studies. Highlights Data-driven approach offers viable alternatives to traditional hydrological models for short-term predictions.Support Vector Regression model results suitable for hydrological modelling also in complex Alpine Region.Data-driven pipeline can effectively bridge the gap between research and operational aspects of water management. © The Author(s) 2024
abstractGer	Abstract Short-term hydrological forecasting is crucial for suitable multipurpose water resource management involving water uses, hydrological security, and renewable production. In the Alpine Regions such as South Tyrol, characterized by several small watersheds, quick information is essential to feed the decision processes in critical cases such as flood events. Predicting water availability ahead is equally crucial for optimizing resource utilization, such as irrigation or snow-making. The increasing data availability and computational power led to data-driven models becoming a serious alternative to physically based hydrological models, especially in complex conditions such as the Alpine Region and for short predictive horizons. This paper proposes a data-driven pipeline to use the local ground station data to infer information in a Support Vector Regression model, which can forecast streamflow in the main closure points of the area at hourly resolution with 48 h of lead time. The main steps of the pipeline are analysed and discussed, with promising results that depend on available information, watershed complexity, and human interactions in the catchment. The presented pipeline, as it stands, offers an accessible tool for integrating these models into decision-making processes to guarantee real-time streamflow information at several points of the hydrological network. Discussion enhances the potentialities, open challenges, and prospects of short-term streamflow forecasting to accommodate broader studies. Highlights Data-driven approach offers viable alternatives to traditional hydrological models for short-term predictions.Support Vector Regression model results suitable for hydrological modelling also in complex Alpine Region.Data-driven pipeline can effectively bridge the gap between research and operational aspects of water management. © The Author(s) 2024
abstract_unstemmed	Abstract Short-term hydrological forecasting is crucial for suitable multipurpose water resource management involving water uses, hydrological security, and renewable production. In the Alpine Regions such as South Tyrol, characterized by several small watersheds, quick information is essential to feed the decision processes in critical cases such as flood events. Predicting water availability ahead is equally crucial for optimizing resource utilization, such as irrigation or snow-making. The increasing data availability and computational power led to data-driven models becoming a serious alternative to physically based hydrological models, especially in complex conditions such as the Alpine Region and for short predictive horizons. This paper proposes a data-driven pipeline to use the local ground station data to infer information in a Support Vector Regression model, which can forecast streamflow in the main closure points of the area at hourly resolution with 48 h of lead time. The main steps of the pipeline are analysed and discussed, with promising results that depend on available information, watershed complexity, and human interactions in the catchment. The presented pipeline, as it stands, offers an accessible tool for integrating these models into decision-making processes to guarantee real-time streamflow information at several points of the hydrological network. Discussion enhances the potentialities, open challenges, and prospects of short-term streamflow forecasting to accommodate broader studies. Highlights Data-driven approach offers viable alternatives to traditional hydrological models for short-term predictions.Support Vector Regression model results suitable for hydrological modelling also in complex Alpine Region.Data-driven pipeline can effectively bridge the gap between research and operational aspects of water management. © The Author(s) 2024
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title_short	Exploring the feasibility of Support Vector Machine for short-term hydrological forecasting in South Tyrol: challenges and prospects
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author2	Lombardi, Andrea Menapace, Andrea Zanfei, Ariele Righetti, Maurizio
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up_date	2024-07-03T14:25:37.767Z
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Exploring the feasibility of Support Vector Machine for short-term hydrological forecasting in South Tyrol: challenges and prospects

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