Delineating flood prone areas using a statistical approach

Floods are frequent and widespread in Italy and pose a severe risk for the population. Local administrations commonly use flow propagation models to delineate the flood prone areas. These modeling approaches require a detail geo-environmental data knowledge, intensive calculation and long computational times. Conversely, statistical methods can be used to asses flood hazard over large areas, or to extend the flood hazard zonation to the portion of the river networks where hydraulic models have still not been applied or can be applied with difficulties. In this paper, we describe a statistical approach to prepare flood hazard maps for the whole of Italy. The proposed method is based on a multivariate machine learning algorithm calibrated using in input flood hazard maps delineated by the local authorities and terrain elevation data. The preliminary results obtained in several major Italian catchments indicate good performances of the statistical algorithm in matching the training data. Results are promising giving the possibility to obtain reliable delineations of flood prone areas obtained in the rest of the Italian territory.

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Delineating flood prone areas using a statistical approach

10.7287/peerj.preprints.1937 ◽

2016 ◽

Author(s):

Ivan Marchesini ◽

Mauro Rossi ◽

Paola Salvati ◽

Marco Donnini ◽

Simone Sterlacchini ◽

...

Keyword(s):

Statistical Approach ◽

Learning Algorithm ◽

Flood Hazard ◽

Environmental Data ◽

Training Data ◽

Hazard Maps ◽

Elevation Data ◽

Terrain Elevation ◽

Flood Prone Areas ◽

Flood Hazard Maps

Floods are frequent and widespread in Italy and pose a severe risk for the population. Local administrations commonly use flow propagation models to delineate the flood prone areas. These modeling approaches require a detail geo-environmental data knowledge, intensive calculation and long computational times. Conversely, statistical methods can be used to asses flood hazard over large areas, or to extend the flood hazard zonation to the portion of the river networks where hydraulic models have still not been applied or can be applied with difficulties. In this paper, we describe a statistical approach to prepare flood hazard maps for the whole of Italy. The proposed method is based on a multivariate machine learning algorithm calibrated using in input flood hazard maps delineated by the local authorities and terrain elevation data. The preliminary results obtained in several major Italian catchments indicate good performances of the statistical algorithm in matching the training data. Results are promising giving the possibility to obtain reliable delineations of flood prone areas obtained in the rest of the Italian territory.

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Combination of geomorphic classifiers through Machine Learning-based techniques for flood hazard assessment

10.5194/egusphere-egu21-4723 ◽

2021 ◽

Author(s):

Andrea Magnini ◽

Michele Lombardi ◽

Simone Persiano ◽

Antonio Tirri ◽

Francesco Lo Conti ◽

...

Keyword(s):

Machine Learning ◽

Hazard Assessment ◽

Water Depth ◽

Flood Hazard ◽

Research Centre ◽

Hazard Mapping ◽

Hazard Maps ◽

Flood Hazard Assessment ◽

Flood Prone Areas ◽

Flood Hazard Maps

Every year flood events cause worldwide vast economic losses, as well as heavy social and environmental impacts, which have been steadily increasing for the last five decades due to the complex interaction between climate change and anthropogenic pressure (i.e.&#160;land-use and land-cover modifications). As a result, the body of literature on flood risk assessment is constantly and rapidly expanding, aiming at developing faster, computationally lighter and more efficient methods relative to the traditional and resource-intensive hydrodynamic numerical models. Recent and reliable fast-processing techniques for flood hazard assessment and mapping consider binary geomorphic classifiers retrieved from the analysis of Digital Elevation Models (DEMs). These procedures (termed herein &#8220;DEM-based methods&#8221;) produce binary maps distinguishing between floodable and non-floodable areas based on the comparison between the local value of the considered geomorphic classifier and a threshold, which in turn is calibrated against existing flood hazard maps. Previous studies have shown the reliability of DEM-based methods using a single binary classifier, they also highlighted that different classifiers are associated with different performance, depending on the geomorphological, climatic and hydrological characteristics of the study area. The present study maps flood-prone areas and predicts water depth associated with a given non-exceedance probability by combining several geomorphic classifiers and terrain features through regression trees and random forests. We focus on Northern Italy (c.a. 100000 km2, including Po, Adige, Brenta, Bacchiglione and Reno watersheds), and we consider the recently compiled MERIT (Multi-Error Removed Improved-Terrain) DEM, with 3sec-resolution (~90m at the Equator). We select the flood hazard maps provided by (i) the Italian Institute for Environmental Protection and Research (ISPRA), and (ii) the Joint Research Centre (JRC) of the European Commission as reference maps. Our findings (a) confirm the usefulness of machine learning techniques for improving univariate DEM-based flood hazard mapping, (b) enable a discussion on potential and limitations of the approach and (c) suggest promising pathways for further exploring DEM-based approaches for predicting a likely water depth distribution with flood-prone areas.&#160;

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Fluvial flooding hazard assessment in Northern Italy: potential and informativeness of different geomorphic classifiers

10.5194/egusphere-egu2020-19760 ◽

2020 ◽

Author(s):

Attilio Castellarin ◽

Simone Persiano ◽

Caterina Samela ◽

Andrea Magnini ◽

Stefano Bagli ◽

...

Keyword(s):

Flood Hazard ◽

Numerical Models ◽

The United States ◽

Northern Italy ◽

Economic Losses ◽

Steady Increase ◽

Threshold Values ◽

Hazard Maps ◽

Flood Prone Areas ◽

Flood Hazard Maps

The steady increase of economic losses and social consequences caused by flood events in Europe, as a result of the combined effects of anthropization (e.g. land-use and land-cover changes) and climate change, calls for updated and efficient technologies for assessing pluvial, fluvial and coastal flood hazards and risks. In this context, the EIT-Climate KIC SaferPLACES () project aims at exploring and developing innovative and simplified modelling techniques to assess and map flood hazard and risk in urban environments under current and future climates. Concerning fluvial flooding, detailed inundation maps can be accurately obtained by means of hydrological and hydraulic numerical models, whose application, though, is often very resource intensive. For this reason, consistent and harmonized national flood hazard maps are still lacking in many countries of the world. Several studies have proved that flood-prone areas can be delineated by considering linear binary geomorphic classifiers, which are computed by analysing Digital Elevation Models, DEMs, and whose threshold values are calibrated relative to existing hydraulic flood hazard maps. One of these indices, the so-called Geomorphic Flood Index (GFI), was recently shown to be cost-effective, reliable and efficient for identifying flood-prone areas in several test sites in the United States, Africa and Europe. As part of the activities of SaferPLACES, in this study we test different geomorphic classifiers (GFI included) for the identification of flood-prone areas in a wide area in Northern Italy (c.a. 100000 km2, including Po, Adige, Brenta-Bacchiglione and Reno river basins). We refer to the recently compiled MERIT (Multi-Error-Removed Improved-Terrain) DEM, a 3sec-resolution (~90m at the equator) DEM developed by removing multiple error components from existing spaceborne DEMs. As reference maps for the calibration, we select the flood hazard maps provided by (i) the Italian Institute for Environmental Protection and Research (ISPRA), and (ii) the Joint Research Center (JRC) of the European Commission. Our study confirms the better performances of GFI compared to other geomorphic classifiers, also providing useful information regarding the sensitivity of GFI threshold values relative to different reference hazard maps; it also suggests as a promising avenue for future researches the combination of multiple geomorphic indices through data-driven approaches and artificial intelligence.

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An integrated hydrological and hydraulic modelling approach for the flood risk assessment over Po river basin

10.5194/nhess-2019-356 ◽

2019 ◽

Cited By ~ 2

Author(s):

Rita Nogherotto ◽

Adriano Fantini ◽

Francesca Raffaele ◽

Fabio Di Sante ◽

Francesco Dottori ◽

...

Keyword(s):

Ad Hoc ◽

Flood Hazard ◽

Hydraulic Modeling ◽

Return Periods ◽

Hazard Maps ◽

Joint Research ◽

Po River ◽

Essential Information ◽

Flood Prone Areas ◽

Flood Hazard Maps

Abstract. Identification of flood prone areas is instrumental for a large number of applications, ranging from engineering to climate change studies, and provides essential information for planning effective emergency responses. In this work we describe an integrated hydrological and hydraulic modeling approach for the assessment of flood-prone areas in Italy and we present the first results obtained over the Po river (Northern Italy) at a resolution of 90 m. River discharges are obtained through the hydrological model CHyM driven by GRIPHO, a newly-developed high resolution hourly precipitation dataset. Runoff data is then used to obtain Synthetic Design Hydrographs (SDHs) for different return periods along the river network. Flood hydrographs are subsequently processed by a parallelized version of the CA2D hydraulic model to calculate the flow over an ad hoc re-shaped HydroSHEDS digital elevation model which includes information about the channel geometry. Modeled hydrographs and SDHs are compared with those obtained from observed data for a choice of gauging stations, showing an overall good performance of the CHyM model. The flood hazard maps for return periods of 50, 100, 500 are validated by comparison with the official flood hazard maps produced by the River Po Authority (Adbpo) and with the Joint Research Centre's (JRC) pan-European maps. The results show a good agreement with the available official national flood maps for high return periods. For lower return periods the results and less satisfactory but overall the application suggests strong potential of the proposed approach for future applications.

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Design Floods Considering the Epistemic Uncertainty

Water ◽

10.3390/w13111601 ◽

2021 ◽

Vol 13 (11) ◽

pp. 1601

Author(s):

Radu Drobot ◽

Aurelian Florentin Draghia ◽

Daniel Ciuiu ◽

Romică Trandafir

Keyword(s):

Flood Hazard ◽

Epistemic Uncertainty ◽

Statistical Distributions ◽

Hazard Maps ◽

Design Flood ◽

Flood Hydrograph ◽

Recorded Data ◽

Different Shapes ◽

Significant Spread ◽

Flood Hazard Maps

The Design Flood (DF) concept is an essential tool in designing hydraulic works, defining reservoir operation programs, and identifying reliable flood hazard maps. The purpose of this paper is to present a methodology for deriving a Design Flood hydrograph considering the epistemic uncertainty. Several appropriately identified statistical distributions allow for the acceptable approximation of the frequent values of maximum discharges or flood volumes, and display a significant spread for their medium/low Probabilities of Exceedance (PE). The referred scattering, as a consequence of epistemic uncertainty, defines an area of uncertainty for both recorded data and extrapolated values. In considering the upper and lower values of the uncertainty intervals as limits for maximum discharges and flood volumes, and by further combining them compatibly, a set of DFs as completely defined hydrographs with different shapes result for each PE. The herein proposed procedure defines both uni-modal and multi-modal DFs. Subsequently, such DFs help water managers in examining and establishing tailored approaches for a variety of input hydrographs, which might be typically generated in river basins.

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Moving to 3-D flood hazard maps for enhancing risk communication

Environmental Modelling & Software ◽

10.1016/j.envsoft.2018.11.005 ◽

2019 ◽

Vol 111 ◽

pp. 510-522 ◽

Cited By ~ 22

Author(s):

Francesco Macchione ◽

Pierfranco Costabile ◽

Carmelina Costanzo ◽

Rosa De Santis

Keyword(s):

Risk Communication ◽

Flood Hazard ◽

Hazard Maps ◽

Flood Hazard Maps

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Seaside, Oregon Tsunami Pilot Study - modernization of FEMA flood hazard maps

Open-File Report ◽

10.3133/ofr20061234 ◽

2006 ◽

Cited By ~ 4

Author(s):

Keyword(s):

Pilot Study ◽

Flood Hazard ◽

Hazard Maps ◽

Flood Hazard Maps

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Concept of a Polish Database of a Multi-Dimensional Cadastral System with Particular Focus on Geo-Hazards

Proccedings of 10th International Conference "Environmental Engineering" ◽

10.3846/enviro.2017.247 ◽

2017 ◽

Author(s):

Agnieszka Trystuła

Keyword(s):

Crisis Management ◽

New Technologies ◽

Flood Hazard ◽

Data Visualisation ◽

Hazard Maps ◽

Multiple Factors ◽

Dynamic Growth ◽

Cadastral System ◽

Flood Hazard Maps ◽

Cadastral Systems

The dynamic growth of contemporary cadastral systems depends on multiple factors, which include, e.g. economic policy of a given country and possibilities of implementing activities supporting innovation and transfer of new technologies. A modern cadastre should satisfy not only its leading functions, which include, e.g. fiscal, information, legal or record functions. It should also be oriented towards new challenges, including 3D geovisualisation, which will enable multidimensional visualisation of cadastral objects. New data visualisation methods will contribute to extending the existing functions of cadastral systems and to emergence of new functions, e.g. related to ensuring public safety as a basic aim of crisis management, being an important element of sustainable development. This paper presents a concept of a database of multidimensional cadastral system enabling, for instance, 3D visualisation of system objects, incorporating its known functions (e.g. fiscal, information or legal functions), and also a new purpose –support for crisis management. Additionally, the study indicates sources of data that should be used for this type of undertaking (e.g. flood hazard maps, maps of areas at risk of mass land movements, orthophotomaps).

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Flood hazard assessment of a flood-prone intensively urbanized area – A case study from Samsun Province, Turkey

Geofizika ◽

10.15233/gfz.2020.37.2 ◽

2020 ◽

Vol 37 (1) ◽

pp. 1-25

Author(s):

Neslihan Beden ◽

Aslı Ülke Keskin

Keyword(s):

Flood Hazard ◽

Wide Spectrum ◽

Flood Plain ◽

Flood Management ◽

Flood Damage ◽

The Black Sea ◽

Hazard Maps ◽

Flood Depth ◽

The Social ◽

Flood Hazard Maps

Flooding is one of the most catastrophic events among the wide spectrum of natural disasters that impact human communities. The identification of floodprone areas and the probability of occurrence, or estimated return period, of flood events are fundamental to proper planning for flood management and minimization of the social and economic costs of flood damage. In this study, 1D/2D coupled flood models of the Mert River, which flows into the Black Sea at Samsun in north-central Turkey, were developed. Based on the flood modeling results, flood extent, flood depth and flood hazard maps for the river were produced and they showed that the study area is particularly flood prone, as evidenced by catastrophic flooding in 2012. Specifically, the estimated 100, 500 and 1000-year peak discharges would affect 184 ha, 262 ha and 304 ha, respectively, of the 1,200 ha study area. Hazard ratings for the areas expected to be affected are shown in the flood hazard maps generated. The results of this research can be used by local government agencies in Samsun for the development of policies, strategies and actions that would help minimize the social and economic impacts of flooding, especially adjacent to the downstream sections where there is intensive development on the flood plain.

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