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Article ; Online: Analyzing data in complicated 3D domains: Smoothing, semiparametric regression, and functional principal component analysis.

Arnone, Eleonora / Negri, Luca / Panzica, Ferruccio / Sangalli, Laura M

2023 Volume 79, Issue 4, Page(s) 3510–3521

Abstract: In this work, we introduce a family of methods for the analysis of data observed at locations scattered in three-dimensional (3D) domains, with possibly complicated shapes. The proposed family of methods includes smoothing, regression, and functional ... ...

Abstract	In this work, we introduce a family of methods for the analysis of data observed at locations scattered in three-dimensional (3D) domains, with possibly complicated shapes. The proposed family of methods includes smoothing, regression, and functional principal component analysis for functional signals defined over (possibly nonconvex) 3D domains, appropriately complying with the nontrivial shape of the domain. This constitutes an important advance with respect to the literature, because the available methods to analyze data observed in 3D domains rely on Euclidean distances, which are inappropriate when the shape of the domain influences the phenomenon under study. The common building block of the proposed methods is a nonparametric regression model with differential regularization. We derive the asymptotic properties of the methods and show, through simulation studies, that they are superior to the available alternatives for the analysis of data in 3D domains, even when considering domains with simple shapes. We finally illustrate an application to a neurosciences study, with neuroimaging signals from functional magnetic resonance imaging, measuring neural activity in the gray matter, a nonconvex volume with a highly complicated structure.
MeSH term(s)	Principal Component Analysis ; Magnetic Resonance Imaging/methods ; Neuroimaging ; Computer Simulation ; Cerebral Cortex
Language	English
Publishing date	2023-03-20
Publishing country	United States
Document type	Journal Article
ZDB-ID	213543-7
ISSN	1541-0420 ; 0099-4987 ; 0006-341X
ISSN (online)	1541-0420
ISSN	0099-4987 ; 0006-341X
DOI	10.1111/biom.13845
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Article ; Online: Predicting peakflows in mountain river basins and data-scarce areas: a case study in northeastern Italy

Arnone, Elisa / Zoratti, Veronica / Formetta, Giuseppe / Bosa, Silvia / Petti, Marco

Hydrological Sciences Journal. 2023 Feb. 17, v. 68, no. 3 p.432-447

2023

Abstract: We present a procedure to predict peak flows in mountain and ungauged basins, by addressing two challenges: fine temporal resolution required to capture intense storms; scarcity of streamflow measurements needed to calibrate hydrological models. The ... ...

Abstract	We present a procedure to predict peak flows in mountain and ungauged basins, by addressing two challenges: fine temporal resolution required to capture intense storms; scarcity of streamflow measurements needed to calibrate hydrological models. The study area is the Fella River basin at Pontebba and its upstream Uque sub-basin, in the northeastern Italian Julian Alps. A non-stationary hydraulic model is combined with field measurements to derive rating curves at the downstream outlet which lacks discharge data. Five-minute rainfall series are exploited to predict hydrographs through a semi-distributed hydrological model, at continuous and event scales. The hydrological modelling is verified on an ungauged basin at the inner upstream outlet. Results indicated that the predictability of intense events is improved when a single storm is evaluated compared to a continuous hydrograph; event-based hydrographs at the upstream outlet are well reproduced by the model calibrated with downstream data, thus allowing the use of the parameters in an ungauged basin.
Keywords	basins ; case studies ; hydrograph ; hydrologic models ; rain ; rivers ; storms ; stream flow ; watersheds ; Alps region ; Italy ; hydrologic modelling ; temporal resolution ; ungauged basins ; intense events ; debris-flow initiation
Language	English
Dates of publication	2023-0217
Size	p. 432-447.
Publishing place	Taylor & Francis
Document type	Article ; Online
ISSN	2150-3435
DOI	10.1080/02626667.2022.2162408
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Article ; Online: Analyzing data in complicated 3D domains: Smoothing, semiparametric regression, and functional principal component analysis

Arnone, Eleonora / Negri, Luca / Panzica, Ferruccio / Sangalli, Laura M.

Biometrics. 2023 Dec., v. 79, no. 4 p.3510-3521

2023

Abstract: In this work, we introduce a family of methods for the analysis of data observed at locations scattered in three‐dimensional (3D) domains, with possibly complicated shapes. The proposed family of methods includes smoothing, regression, and functional ... ...

Abstract	In this work, we introduce a family of methods for the analysis of data observed at locations scattered in three‐dimensional (3D) domains, with possibly complicated shapes. The proposed family of methods includes smoothing, regression, and functional principal component analysis for functional signals defined over (possibly nonconvex) 3D domains, appropriately complying with the nontrivial shape of the domain. This constitutes an important advance with respect to the literature, because the available methods to analyze data observed in 3D domains rely on Euclidean distances, which are inappropriate when the shape of the domain influences the phenomenon under study. The common building block of the proposed methods is a nonparametric regression model with differential regularization. We derive the asymptotic properties of the methods and show, through simulation studies, that they are superior to the available alternatives for the analysis of data in 3D domains, even when considering domains with simple shapes. We finally illustrate an application to a neurosciences study, with neuroimaging signals from functional magnetic resonance imaging, measuring neural activity in the gray matter, a nonconvex volume with a highly complicated structure.
Keywords	data analysis ; magnetism ; principal component analysis ; regression analysis
Language	English
Dates of publication	2023-12
Size	p. 3510-3521.
Publishing place	John Wiley & Sons, Ltd
Document type	Article ; Online
Note	JOURNAL ARTICLE
ZDB-ID	213543-7
ISSN	0099-4987 ; 0006-341X
ISSN	0099-4987 ; 0006-341X
DOI	10.1111/biom.13845
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Article ; Online: Anatomically compliant modes of variations: New tools for brain connectivity.

Clementi, Letizia / Arnone, Eleonora / Santambrogio, Marco D / Franceschetti, Silvana / Panzica, Ferruccio / Sangalli, Laura M

PloS one

2023 Volume 18, Issue 11, Page(s) e0292450

Abstract: Anatomical complexity and data dimensionality present major issues when analysing brain connectivity data. The functional and anatomical aspects of the connections taking place in the brain are in fact equally relevant and strongly intertwined. However, ... ...

Abstract	Anatomical complexity and data dimensionality present major issues when analysing brain connectivity data. The functional and anatomical aspects of the connections taking place in the brain are in fact equally relevant and strongly intertwined. However, due to theoretical challenges and computational issues, their relationship is often overlooked in neuroscience and clinical research. In this work, we propose to tackle this problem through Smooth Functional Principal Component Analysis, which enables to perform dimensional reduction and exploration of the variability in functional connectivity maps, complying with the formidably complicated anatomy of the grey matter volume. In particular, we analyse a population that includes controls and subjects affected by schizophrenia, starting from fMRI data acquired at rest and during a task-switching paradigm. For both sessions, we first identify the common modes of variation in the entire population. We hence explore whether the subjects' expressions along these common modes of variation differ between controls and pathological subjects. In each session, we find principal components that are significantly differently expressed in the healthy vs pathological subjects (with p-values < 0.001), highlighting clearly interpretable differences in the connectivity in the two subpopulations. For instance, the second and third principal components for the rest session capture the imbalance between the Default Mode and Executive Networks characterizing schizophrenia patients.
MeSH term(s)	Humans ; Brain/pathology ; Magnetic Resonance Imaging/methods ; Brain Mapping/methods ; Schizophrenia ; Gray Matter/pathology ; Neural Pathways
Language	English
Publishing date	2023-11-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0292450
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: On the Leonardo's rule for the assessment of root profile

Preti, F. / Dani, A. / Noto, L.V. / Arnone, E.

Ecological engineering. 2022 Mar. 15,

2022

Abstract: Leonardo's rule (Lrule) applied to below-ground systems defines a simple topological scheme that describes how the branches of root architectures develop within the soil. The approach does not consider the soil-climate-root interactions. From another ... ...

Abstract	Leonardo's rule (Lrule) applied to below-ground systems defines a simple topological scheme that describes how the branches of root architectures develop within the soil. The approach does not consider the soil-climate-root interactions. From another hand, eco-hydrological approaches exploit physically-based formulations to derive the dynamic evolution of root profile based on soil and climate characteristics. In homogenous soil and simplified hydrological conditions, analytical solutions can be derived, as demonstrated by Laio's model, who proposed a simple exponential formulation to derive the Root Area (AR) profile. Apart from Laio's model, more generalized functions, i.e. derived by two and three parameters gamma distribution or others, can be efficiently used to derive the AR profile. This communication proposes a combination of the Lrule and eco-hydrological approaches to derive the AR profile, at given soil and climate conditions, allowing to identify a physical and theoretical meaning of the Lrule's parameters. A comprehensive root dataset from field measurements carried out in the region of Tuscany (Italy) is used. Results demonstrate that values of Lrule's parameters derived throughout the proposed mathematical relationships tend to constant values in case of exponential function, which is valid for homogenous soils. Moreover, in a realistic vegetated soil, where top-soil is different than deep-soil, functions derived from a two and three parameters gamma distribution may reproduce better root data observations.
Keywords	data collection ; hydrologic cycle ; models ; topology ; topsoil ; Italy
Language	English
Dates of publication	2022-0315
Publishing place	Elsevier B.V.
Document type	Article
Note	Pre-press version
ZDB-ID	1127407-4
ISSN	0925-8574
ISSN	0925-8574
DOI	10.1016/j.ecoleng.2022.106620
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Article ; Online: Interannual-to-multidecadal sea-level changes in the Venice lagoon and their impact on flood frequency

Rubinetti, S. / Taricco, C. / Zanchettin, D. / Arnone, E. / Bizzarri, I. / Rubino, A.

Climatic Change. 2022 Oct., v. 174, no. 3-4 p.26-26

2022

Abstract: Tidal measurements from the Italian city of Venice, available since 1872 and constituting the longest sea-level record in the Mediterranean area, indicate that local flooding statistics have dramatically worsened during the last decades. Individual ... ...

Abstract	Tidal measurements from the Italian city of Venice, available since 1872 and constituting the longest sea-level record in the Mediterranean area, indicate that local flooding statistics have dramatically worsened during the last decades. Individual flooding episodes are associated with adverse meteorological conditions, and their increased frequency is mainly attributed to the rise of the average local Relative Sea Level (RSL). However, the role of interannual-to-multidecadal modes of average RSL variability in shaping the evolution of Venice flooding is highly significant and can cause sharp increases in the flood frequency episodes. Here, we use local tidal measurements in Venice covering 1872–2020 to deeply inspect the contribution and predictability of the different components characterizing the observed average RSL variability, including a long-term trend and four quasi-periodic modes. Our results demonstrate that the observed increase in flooding frequency is not only due to the average RSL rise but also due to a progressive widening of tidal anomalies around the average RSL, revealed by opposite trends in mean tidal maxima and minima. Moreover, interannual and decadal periodicities are not negligible in modulating the timing of annual mean RSL and flood frequency extremes. This study demonstrates that the last decades experienced an unprecedented sharp increase in sea level, which significantly affected the decadal predictability of RSL with statistical methods. Our work contributes to a deeper understanding of the sources of uncertainty in decadal sea-level variability and predictability in the Venice lagoon.
Keywords	climate change ; evolution ; sea level ; uncertainty ; Mediterranean region
Language	English
Dates of publication	2022-10
Size	p. 26.
Publishing place	Springer Netherlands
Document type	Article ; Online
ZDB-ID	751086-x
ISSN	0165-0009
ISSN	0165-0009
DOI	10.1007/s10584-022-03448-2
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Article: A roughness penalty approach to estimate densities over two-dimensional manifolds

Arnone, Eleonora / Ferraccioli, Federico / Pigolotti, Clara / Sangalli, Laura M.

Computational statistics & data analysis. 2021 Sept. 23,

2021

Abstract: An innovative nonparametric method for density estimation over general two-dimensional Riemannian manifolds is proposed. The method follows a functional data analysis approach, combining maximum likelihood estimation with a roughness penalty that ... ...

Abstract	An innovative nonparametric method for density estimation over general two-dimensional Riemannian manifolds is proposed. The method follows a functional data analysis approach, combining maximum likelihood estimation with a roughness penalty that involves a differential operator appropriately defined over the manifold domain, thus controlling the smoothness of the estimate. The proposed method can accurately handle point pattern data over complicated curved domains. Moreover, it is able to capture complex multimodal signals, with strongly localized and highly skewed modes, with varying directions and intensity of anisotropy. The estimation procedure exploits a discretization in finite element bases, enabling great flexibility on the spatial domain. The method is tested through simulation studies, showing the strengths of the proposed approach. Finally, the density estimation method is illustrated with an application to the distribution of earthquakes in the world.
Keywords	anisotropy ; data analysis ; finite element analysis ; roughness ; statistical analysis
Language	English
Dates of publication	2021-0923
Publishing place	Elsevier B.V.
Document type	Article
Note	Pre-press version
ZDB-ID	1478763-5
ISSN	0167-9473
ISSN	0167-9473
DOI	10.1016/j.csda.2022.107527
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Article: Brooklyn Botanic Garden and the new Brooklyn Academy of Science and Environment

Arnone, E

Public garden : the journal of the American Association of Botanical Gardens and Arboreta. 2005, v. 20, no. 3

2005

Keywords	botanical gardens ; science education ; youth programs ; secondary education ; high school students ; case studies ; New York
Language	English
Size	p. 26-28.
Document type	Article
ISSN	0885-3894
Database	NAL-Catalogue (AGRICOLA)

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Article: Effect of raster resolution and polygon-conversion algorithm on landslide susceptibility mapping

Arnone, E / A. Francipane / A. Scarbaci / C. Puglisi / L.V. Noto

Environmental modelling & software. 2016 Oct., v. 84

2016

Abstract: The choice of the proper resolution in landslide susceptibility mapping is a worth considering issue. If, on the one hand, a coarse spatial resolution may describe the terrain morphologic properties with low accuracy, on the other hand, at very fine ... ...

Abstract	The choice of the proper resolution in landslide susceptibility mapping is a worth considering issue. If, on the one hand, a coarse spatial resolution may describe the terrain morphologic properties with low accuracy, on the other hand, at very fine resolutions, some of the DEM-derived morphometric factors may hold an excess of details. Moreover, the landslide inventory maps are represented throughout geospatial vector data structure, therefore a conversion procedure vector-to-raster is required.This work investigates the effects of raster resolution on the susceptibility mapping in conjunction with the use of different algorithms of vector-raster conversion. The Artificial Neural Network technique is used to carry out such analyses on two Sicilian basins. Seven resolutions and three conversion algorithms are investigated. Results indicate that the finest resolutions do not lead to the highest model performances, whereas the algorithm of conversion data may significantly affect the ANN training procedure at coarse resolutions.
Keywords	algorithms ; basins ; computer software ; environmental models ; inventories ; landslides ; morphometry ; neural networks ; vector data
Language	English
Dates of publication	2016-10
Size	p. 467-481.
Publishing place	Elsevier Ltd
Document type	Article
ISSN	1364-8152
DOI	10.1016/j.envsoft.2016.07.016
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Modeling the hydrological and mechanical effect of roots on shallow landslides

Arnone, E. / Caracciolo, D. / Noto, L. V. / Preti, F. / Bras, R. L.

Water Resources Research. 2016 Nov., v. 52, no. 11 p.8590-8612

2016

Abstract: This study proposes a new methodology for estimating the additional shear strength (or cohesion) exerted by vegetation roots on slope stability analysis within a coupled hydrological‐stability model. The mechanical root cohesion is estimated within a ... ...

Abstract	This study proposes a new methodology for estimating the additional shear strength (or cohesion) exerted by vegetation roots on slope stability analysis within a coupled hydrological‐stability model. The mechanical root cohesion is estimated within a Fiber Bundle Model framework that allows for the evaluation of the root strength as a function of stress‐strain relationships of populations of fibers. The use of such model requires the knowledge of the root architecture. A branching topology model based on Leonardo's rule is developed, providing an estimation of the amount of roots and the distribution of diameters with depth. The proposed methodology has been implemented into an existing distributed hydrological‐stability model able to simulate the dynamics of factor of safety as a function of soil moisture dynamics. The model also accounts for the hydrological effects of vegetation, which reduces soil water content via root water uptake, thus increasing the stability. The entire methodology has been tested in a synthetic hillslope with two configurations of vegetation type, i.e., trees and shrubs, which have been compared to a configuration without vegetation. The vegetation has been characterized using roots data of two mediterranean plant species. The results demonstrate the capabilities of the topological model in accurately reproducing the observed root structure of the analyzed species. For the environmental setting modeled, the effects of root uptake might be more significant than the mechanical reinforcement; the additional resistance depends strictly on the vegetation root depth. Finally, for the simulated climatic environment, landslides are seasonal, in agreement with past observations.
Keywords	cohesion ; methodology ; models ; research ; shear strength ; soil water ; soil water content ; topographic slope ; topology ; vegetation types ; water uptake
Language	English
Dates of publication	2016-11
Size	p. 8590-8612.
Publishing place	John Wiley & Sons, Ltd
Document type	Article ; Online
Note	JOURNAL ARTICLE
ZDB-ID	5564-5
ISSN	1944-7973 ; 0043-1397
ISSN (online)	1944-7973
ISSN	0043-1397
DOI	10.1002/2015WR018227
Database	NAL-Catalogue (AGRICOLA)

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