Systemic models for supporting flood warning procedures in river sections with no rating curves: application to a set of Emilia-Romagna watersheds

Calligola, Francesca (2017) Systemic models for supporting flood warning procedures in river sections with no rating curves: application to a set of Emilia-Romagna watersheds. [Laurea magistrale], Università di Bologna, Corso di Studio in Civil engineering [LM-DM270], Documento ad accesso riservato.
Documenti full-text disponibili:
[img] Documento PDF (Thesis)
Full-text accessibile solo agli utenti istituzionali dell'Ateneo
Disponibile con Licenza: Salvo eventuali più ampie autorizzazioni dell'autore, la tesi può essere liberamente consultata e può essere effettuato il salvataggio e la stampa di una copia per fini strettamente personali di studio, di ricerca e di insegnamento, con espresso divieto di qualunque utilizzo direttamente o indirettamente commerciale. Ogni altro diritto sul materiale è riservato

Download (19MB) | Contatta l'autore


The present work analyses the possibilities offered by the implementation of artificial neural networks (ANNs) for the flood warning purpose. In particular, four river basins located in the Emilia-Romagna region have been selected, analysing the forecast performances of the models at each lead-time from +1 to +18 hours. Specifically, the models, run at hourly time-scale, have been calibrated considering as input variables past river levels and past and future precipitation values. The ANN calibration has been done considering continuous simulation periods (therefore including peak, average and low flow regimes). In a first phase the models selected for each case study have been evaluated according to the Nash-Sutcliffe efficiency in order to understand the general behavior of the ANNs. Secondly, a further assessment is added considering the skill score indices (POD, FAR, ETS) of the exceedance of the assigned flood thresholds; in this way, it is possible to understand the reliability of the ANNs for flood warning purposes. For this second evaluation, ANN models which make use only of the past river levels hence with no precipitation input values, are included in the analysis as an additional standard of reference. Considering the simulation results on all the basins, the Nash-Sutcliffe efficiency highlighted acceptable results for all the forecasting lead-times (1 to 18 hours). On the other hand, the skill score indexes on the exceedance of the flood thresholds are satisfactory only over shorter time-horizons.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Calligola, Francesca
Relatore della tesi
Correlatore della tesi
Corso di studio
Infrastructure Design in River Basins
Ordinamento Cds
Parole chiave
artificial neural networks,flood warning
Data di discussione della Tesi
6 Ottobre 2017

Altri metadati

Statistica sui download

Gestione del documento: Visualizza il documento