A self‐developed early warning system aimed at flooding effects prevention, is presented. FAMS, Flooding Areas Monitoring System, gathers static information useful for hydrological assessment and produces dynamic flood scenarios, according to forecasted (or present) rainfalls and to a past event archive. It operates in a near real time mode and it behaves as a Decision Support System (DSS). It is a modular system whose core is an hydrological procedure based on neural network algorithms aimed at forecasting water level at different river sections, starting from rainfall measurements coming from meteorological stations sited in the monitored basin. Estimated water levels are assumed as keys to define the potential flooding scenarios. The test area is referred to the High‐Tiber basin (Italy), in the river trait between the towns of Montemolino and Todi. The FAMS is a research prototype for an operational tool useful for those territory technicians facing an early warning context. In particular it is aimed at describing statically an hydrological basin and at following its evolution during risky situations related to floods. Its basic requirement is an operational mode near to the real time, operating through a continuous dialog between the processing module and a network of rainfall measurement stations spread over the monitored basin. The system was developed in a GIS environment but, due to its dynamical structure, it can be reasonably considered a true Decision Support System (DSS). Its main task is, in fact, to check for risky situations and to propose potential flood scenarios, including relationship between the calamitous event and the impacted features. It gathers, archives, manages, synthesizes, transforms and visualizes georeferenced data in order to supply the operators useful information for flood emergencies management. The ideal place for the exploitation of such a tool is the Operational Room, that is a centralized environment where data coming from the field can be addressed to, processed and transformed into information for managing the territory. This peculiarity is obtained assigning to the DSS a high degree of automation, able to produce a sort of monitoring (a repeated observation). The prototype is the result of research efforts lasted two years. It was developed merging skills coming from different fields and supplied by different research groups. Hydrologists, GIS developer, survey and map experts, hydraulics and technicians of the territory supplied their own scientific and operational contribution. The case study, use to evaluate DSS performance, is the High‐Tiber basin, but, the adopted philosophy can be suitable for any basin around the world.

A GIS Tool for Early Warning Monitoring of Flood Events

BORGOGNO MONDINO, ENRICO CORRADO;
2008

Abstract

A self‐developed early warning system aimed at flooding effects prevention, is presented. FAMS, Flooding Areas Monitoring System, gathers static information useful for hydrological assessment and produces dynamic flood scenarios, according to forecasted (or present) rainfalls and to a past event archive. It operates in a near real time mode and it behaves as a Decision Support System (DSS). It is a modular system whose core is an hydrological procedure based on neural network algorithms aimed at forecasting water level at different river sections, starting from rainfall measurements coming from meteorological stations sited in the monitored basin. Estimated water levels are assumed as keys to define the potential flooding scenarios. The test area is referred to the High‐Tiber basin (Italy), in the river trait between the towns of Montemolino and Todi. The FAMS is a research prototype for an operational tool useful for those territory technicians facing an early warning context. In particular it is aimed at describing statically an hydrological basin and at following its evolution during risky situations related to floods. Its basic requirement is an operational mode near to the real time, operating through a continuous dialog between the processing module and a network of rainfall measurement stations spread over the monitored basin. The system was developed in a GIS environment but, due to its dynamical structure, it can be reasonably considered a true Decision Support System (DSS). Its main task is, in fact, to check for risky situations and to propose potential flood scenarios, including relationship between the calamitous event and the impacted features. It gathers, archives, manages, synthesizes, transforms and visualizes georeferenced data in order to supply the operators useful information for flood emergencies management. The ideal place for the exploitation of such a tool is the Operational Room, that is a centralized environment where data coming from the field can be addressed to, processed and transformed into information for managing the territory. This peculiarity is obtained assigning to the DSS a high degree of automation, able to produce a sort of monitoring (a repeated observation). The prototype is the result of research efforts lasted two years. It was developed merging skills coming from different fields and supplied by different research groups. Hydrologists, GIS developer, survey and map experts, hydraulics and technicians of the territory supplied their own scientific and operational contribution. The case study, use to evaluate DSS performance, is the High‐Tiber basin, but, the adopted philosophy can be suitable for any basin around the world.
Proceedings of the 1st International Conference on Remote Sensing Techniques in Disaster Management and Emergency Response in the Mediterranean Region
Marinko Oluic
209
217
9789531548762
GIS; early warning; Natural hazards; Decision support systems
Borgogno Mondino E.; Gomarasca M. A.
File in questo prodotto:
File Dimensione Formato  
Borgogno5.pdf

Accesso riservato

Tipo di file: POSTPRINT (VERSIONE FINALE DELL’AUTORE)
Dimensione 362.92 kB
Formato Adobe PDF
362.92 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/60721
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact