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Federico Amato
Faculty of Geosciences and Environment - Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland

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Preprint content
Published: 03 March 2021
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Wind energy is a promising renewable resource to contribute to the energy transition in many parts of the world. In contrast to solar power, it is available at any time of the day; however, it is highly variable and complex to model. This poses challenges for the planning of future energy systems with high shares of wind power. The quantification of the spatial and temporal variation of wind power and the related uncertainty may hence provide valuable information for energy planners and policymakers. Here we propose an estimation of hourly wind energy potential at the Swiss national scale for pixels of 200 x 200 m2. To this aim, this research is structured into two parts. First, ten years of wind speed measurement collected at an hourly frequency on a set of 208 monitoring stations are interpolated using advanced spatio-temporal techniques, allowing the estimation of wind speed at unsampled locations. Second, the resulting wind field is used to estimate hourly wind power potential on a national scale.

Because of its turbulent nature and its very high variability, wind speed modelling is a challenging task, especially in complex mountainous regions. To face these challenges, the interpolation task is solved as follows. The wind speed data are decomposed through Empirical Orthogonal Functions (EOFs) in temporal basis and spatially dependent coefficients. Then, the spatial coefficients are interpolated. While any regression model could be used to model these coefficients, Extreme Learning Machine (ELM) - a single layer feed-forward neural network with random input weights – was chosen to perform this task, profiting of its high computation speed and of its ability to retrieve reliable and rigorous model uncertainty assessments. Finally, the wind speed time series are reconstructed at any location adopting the interpolated coefficients in the EOFs equation. Uncertainty is quantified by taking advantage of the ELM uncertainty estimates for the spatial coefficients’ models and of the orthogonality of the basis.

In the second part of the research, the modelled spatio-temporal wind field is used to estimate wind power potential, taking into account technical characteristics of horizontal-axis wind turbines as well as national regulatory planning limitations for the installation of power plants. The limitations include restrictions for noise abatement and landscape, natural, ecological and cultural heritage protection plans as provided in the Swiss national wind atlas. The resulting wind power potential represents the first dataset of its type for Switzerland, which may be used to model future energy systems with increased wind power production. Considering the spatial and temporal variability of wind hereby permits to assess the complementarity with other forms of renewables such as photovoltaics, which play a key role in Switzerland’s Energy Strategy.

 

References:

Amato, Federico, et al. "A novel framework for spatio-temporal prediction of environmental data using deep learning." Scientific Reports 10.1 (2020): 1-11.

Guignard, Fabian, et al. "Uncertainty Quantification in Extreme Learning Machine: Analytical Developments, Variance Estimates and Confidence Intervals." arXiv preprint arXiv:2011.01704 (2020).

Walch, Alina, et al. "Big Data Mining for the Estimation of Hourly Rooftop Photovoltaic Potential and Its Uncertainty". Applied Energy 262 (2020): 114404.

ACS Style

Federico Amato; Fabian Guignard; Alina Walch. Wind of change: predicting wind potentials for the energy transition. 2021, 1 .

AMA Style

Federico Amato, Fabian Guignard, Alina Walch. Wind of change: predicting wind potentials for the energy transition. . 2021; ():1.

Chicago/Turabian Style

Federico Amato; Fabian Guignard; Alina Walch. 2021. "Wind of change: predicting wind potentials for the energy transition." , no. : 1.

Journal article
Published: 17 December 2020 in Scientific Reports
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As the role played by statistical and computational sciences in climate and environmental modelling and prediction becomes more important, Machine Learning researchers are becoming more aware of the relevance of their work to help tackle the climate crisis. Indeed, being universal nonlinear function approximation tools, Machine Learning algorithms are efficient in analysing and modelling spatially and temporally variable environmental data. While Deep Learning models have proved to be able to capture spatial, temporal, and spatio-temporal dependencies through their automatic feature representation learning, the problem of the interpolation of continuous spatio-temporal fields measured on a set of irregular points in space is still under-investigated. To fill this gap, we introduce here a framework for spatio-temporal prediction of climate and environmental data using deep learning. Specifically, we show how spatio-temporal processes can be decomposed in terms of a sum of products of temporally referenced basis functions, and of stochastic spatial coefficients which can be spatially modelled and mapped on a regular grid, allowing the reconstruction of the complete spatio-temporal signal. Applications on two case studies based on simulated and real-world data will show the effectiveness of the proposed framework in modelling coherent spatio-temporal fields.

ACS Style

Federico Amato; Fabian Guignard; Sylvain Robert; Mikhail Kanevski. A novel framework for spatio-temporal prediction of environmental data using deep learning. Scientific Reports 2020, 10, 1 -11.

AMA Style

Federico Amato, Fabian Guignard, Sylvain Robert, Mikhail Kanevski. A novel framework for spatio-temporal prediction of environmental data using deep learning. Scientific Reports. 2020; 10 (1):1-11.

Chicago/Turabian Style

Federico Amato; Fabian Guignard; Sylvain Robert; Mikhail Kanevski. 2020. "A novel framework for spatio-temporal prediction of environmental data using deep learning." Scientific Reports 10, no. 1: 1-11.

Research article
Published: 17 October 2020 in International Journal of Disaster Risk Reduction
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The increasing accessibility of Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR) data, grants the opportunity to experiment new methods to support disaster risk management. However, while SAR analyses are becoming extremely popular, thanks, in particular, to the availability of open source satellite images such as those from the Copernicus project, LiDAR analyses are still less common because of the scarce availability of this type of data over significant time frequencies. In this paper we propose an innovative procedure based on the use of SAR and LiDAR data to rapidly assess seismic damage in the early post-emergency phases. The methodology was applied to the case study of the town of Amatrice (Central Italy), which was hit by a strong earthquake swarm that started in August 2016. Specifically, SAR data is used for a large-scale analysis of terrain displacements following the seismic event, while LiDAR reliefs are used to carry out a change detection and to identify the level of damage at a building-scale in the urban settlement of Amatrice. Results will show how the proposed approach can be extremely effective both in the non-emergency phases to monitor seismic-affected areas and support emergency planning, as well as during the immediate post-earthquake phases to assess the damage it has caused and to support first aid dispositions.

ACS Style

Lucia Saganeiti; Federico Amato; Gabriele Nolè; Marco Vona; Beniamino Murgante. Early estimation of ground displacements and building damage after seismic events using SAR and LiDAR data: The case of the Amatrice earthquake in central Italy, on 24th August 2016. International Journal of Disaster Risk Reduction 2020, 51, 101924 .

AMA Style

Lucia Saganeiti, Federico Amato, Gabriele Nolè, Marco Vona, Beniamino Murgante. Early estimation of ground displacements and building damage after seismic events using SAR and LiDAR data: The case of the Amatrice earthquake in central Italy, on 24th August 2016. International Journal of Disaster Risk Reduction. 2020; 51 ():101924.

Chicago/Turabian Style

Lucia Saganeiti; Federico Amato; Gabriele Nolè; Marco Vona; Beniamino Murgante. 2020. "Early estimation of ground displacements and building damage after seismic events using SAR and LiDAR data: The case of the Amatrice earthquake in central Italy, on 24th August 2016." International Journal of Disaster Risk Reduction 51, no. : 101924.

Preprint content
Published: 23 March 2020
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Spatio-temporal modelling of wind speed is an important issue in applied research, such as renewable energy and risk assessment. Due to its turbulent nature and its very high variability, wind speed interpolation is a challenging task. Being universal modeling tools, Machine Learning (ML) algorithms are well suited to detect and model non-linear environmental phenomena such as wind.

The present research proposes a novel and general methodology for spatio-temporal interpolation with an application to hourly wind speed in Switzerland. The methodology is organized as follows. First, the dataset is decomposed through Empirical Orthogonal Functions (EOFs) in temporal basis and spatially dependent coefficients. EOFs constitute an orthogonal basis of the spatio-temporal signal from which the original wind field can be reconstructed. Subsequently, in order to be able to reconstruct the signal at spatial locations where measurements are unknown, the spatial coefficients resulted from the decomposition are interpolated. To this aim, several ML algorithms were used and compared, including k-Nearest Neighbors, Random Forest, Support Vector Machine, General Regression Neural Networks and Extreme Learning Machine. Finally, wind field is reconstructed with the help of the interpolated coefficients.

A case study on real data is presented. Data consists of two years of wind speed measurements at hourly frequency collected by Meteoswiss at several hundreds of stations in Switzerland, which has a complex orography. After cleaning and handling of missing values, a careful exploratory data analysis was carried out, followed by the application of the proposed novel methodology. The model is validated on an independent test set of stations. The outcome of the case study is a time series of hourly maps of wind field at 250 meters spatial resolution, which is highly relevant for renewable energy potential assessment.

In conclusion, the study introduced a new way to interpolate irregular spatio-temporal datasets. Further developments of the methodology could deal with the investigation of alternative basis such as Fourier and wavelets.

 

Reference

N. Cressie, C. K. Wikle, Statistics for Spatio-Temporal Data, Wiley, 2011.

M. Kanevski, A. Pozdnoukhov, V. Timonin, Machine Learning for Spatial Environmental Data, CRC Press, 2009.

ACS Style

Fabian Guignard; Federico Amato; Sylvain Robert; Mikhail Kanevski. Spatio-Temporal Modeling of Wind Speed Using EOF and Machine Learning. 2020, 1 .

AMA Style

Fabian Guignard, Federico Amato, Sylvain Robert, Mikhail Kanevski. Spatio-Temporal Modeling of Wind Speed Using EOF and Machine Learning. . 2020; ():1.

Chicago/Turabian Style

Fabian Guignard; Federico Amato; Sylvain Robert; Mikhail Kanevski. 2020. "Spatio-Temporal Modeling of Wind Speed Using EOF and Machine Learning." , no. : 1.

Preprint content
Published: 23 March 2020
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Global climate has been the focus of an increasing number of researches over the last decades. The ratification of the Paris Agreement imposes to undertake the necessary actions to limit the increase in global average temperature below 1.5oC to ensure a reduction of the risks and impacts of climate change.

Despite the importance of its spatial and temporal distribution, warming has often been investigated only in terms of global and hemispheric means. Moreover, although it is known that climate is characterised by strong nonlinearity and chaotic behaviour, most of the studies in climate science adopt statistical methods valid only for stationary or linear systems. Nevertheless, it has already been shown that warming trends are characterised by strong nonlinearities, with an acceleration in the increase of temperatures since 1980.

In this work, we investigate the complex nature of global temperature trends. We study the maximum temperature at two meters above ground using the NCEP CDAS1 daily reanalysis data, with a spatial resolution of 2.5o by 2.5o and covering the time period from 1 of January 1948 to 30 of November 2018. For each spatial location, we characterize the corresponding temperature time series using methods from Information Theory. Specifically, we analysed the temperature by computing the Fisher Information Measure [1] (FIM) and the Shannon Entropy Power [2] (SEP) in a temporal sliding window, which allows to follow the temporal evolution of the two parameters. We find a significant change in the spatial patterns of the dynamic behaviour of temperatures starting from the early eighties. Specifically, two different patterns are recognizable. In the period from 1948 to the early eighties the latitudes higher than 60oN and lower than 60oS show high levels of SEP and low levels of FIM. The situation completely revers starting from 1980s, and in a faster way for the latitudes higher than 60oN, so that tropical and temperate zones are now characterized by high levels of entropy. The stronger growth of SEP is measured in the northern mid-latitudes. These regions are also known to have been characterized by higher warming trends. Finally, a drastic difference between oceans and land surfaces is detectable, with the former generally interested by significant increases of SEP since the eighties.

[1] Fisher, R.  A Theory of statistical estimation. Math. Proc. Camb. Philos. Soc.22, 700–725, DOI:  10.1017/S0305004100009580 (1925).

[2] Shannon, C. E.  A mathematical theory of communication. Bell Syst. Tech. J.27, 379–423, DOI: 10.1002/j.1538-7305.1948.tb01338.x (1948).

ACS Style

Federico Amato; Fabian Guignard; Mikhail Kanevski. Spatio-temporal global patterns of 70 years of daily temperature using Fisher-Shannon complexity measures. 2020, 1 .

AMA Style

Federico Amato, Fabian Guignard, Mikhail Kanevski. Spatio-temporal global patterns of 70 years of daily temperature using Fisher-Shannon complexity measures. . 2020; ():1.

Chicago/Turabian Style

Federico Amato; Fabian Guignard; Mikhail Kanevski. 2020. "Spatio-temporal global patterns of 70 years of daily temperature using Fisher-Shannon complexity measures." , no. : 1.

Preprint content
Published: 09 March 2020
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The research deals with an application of advanced exploratory tools to study hourly spatio-temporal air pollution data collected by NABEL monitoring network in Switzerland. Data analyzed consist of several pollutants, mainly NO2, O3, PM2.5, measured during last two years at 16 stations distributed over the country. The data are considered in two different ways: 1) as multivariate time series measured at the same station (different pollutants and environmental variables, like temperature), 2) as a spatially distributed time series of the same pollutant. In the first case, it is interesting to study both univariate and multivariate time series and their complexity. In the second case, similarity between time series distributed in space can signify the similar underlying phenomena and environmental conditions giving rise to the pollution. An important aspect of the data is that they are collected at the places of different land use classes – urban, suburban, rural etc., which helps in understanding and interpretation of the results.

Nowadays, unsupervised learning algorithms are widely applied in intelligent exploratory data analysis. Well known tasks of unsupervised learning include manifold learning, dimensionality reduction and clustering. In the present research, intrinsic and fractal dimensions, measures characterizing the similarity and redundancy in data and machine learning clustering algorithms were adapted and applied. The results obtained give a new and important information on the air pollution spatio-temporal patterns. The following results, between others, can be mentioned: 1) some measures of similarity (e.g., complexity-independent distance) are efficient in discriminating between time series; 2) intrinsic dimension, characterizing the ensemble of monitoring data, is pollutant dependent; 3) clustering of time series observed can be interpreted using the available information on land use.  

ACS Style

Mikhail Kanevski; Federico Amato; Fabian Guignard. Advanced Exploratory Analysis of Air Pollution Multivariate Spatio-Temporal Data. 2020, 1 .

AMA Style

Mikhail Kanevski, Federico Amato, Fabian Guignard. Advanced Exploratory Analysis of Air Pollution Multivariate Spatio-Temporal Data. . 2020; ():1.

Chicago/Turabian Style

Mikhail Kanevski; Federico Amato; Fabian Guignard. 2020. "Advanced Exploratory Analysis of Air Pollution Multivariate Spatio-Temporal Data." , no. : 1.

Journal article
Published: 28 February 2020 in Physica A: Statistical Mechanics and its Applications
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Air pollution is known to be a major threat to human and ecosystem health. A proper understanding of the factors generating pollution and of the behavior of air pollution in time is crucial to support the development of effective policies aiming at the reduction of pollutant concentration. This paper considers the hourly time series of three pollutants, namely NO2, O3 and PM2.5, collected on sixteen measurement stations in Switzerland. The air pollution patterns due to the location of measurement stations and their relationship with anthropogenic activities, and specifically land use, are studied using two approaches: Fisher–Shannon information plane and complexity-invariant distance between time series. Clustering analysis is used to recognize within the measurements of same pollutant groups of stations behaving in a similar way. The results clearly demonstrate the relationship between air pollution probability densities and land use activities.

ACS Style

Federico Amato; Mohamed Laib; Fabian Guignard; Mikhail Kanevski. Analysis of air pollution time series using complexity-invariant distance and information measures. Physica A: Statistical Mechanics and its Applications 2020, 547, 124391 .

AMA Style

Federico Amato, Mohamed Laib, Fabian Guignard, Mikhail Kanevski. Analysis of air pollution time series using complexity-invariant distance and information measures. Physica A: Statistical Mechanics and its Applications. 2020; 547 ():124391.

Chicago/Turabian Style

Federico Amato; Mohamed Laib; Fabian Guignard; Mikhail Kanevski. 2020. "Analysis of air pollution time series using complexity-invariant distance and information measures." Physica A: Statistical Mechanics and its Applications 547, no. : 124391.

Journal article
Published: 18 December 2018 in Sustainability
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Land take is one of the most studied phenomena in land use science. The increased attention to the issue of urban growth from both scientists and decision makers is justified by the dramatic negative effects on land use caused by anthropogenic activities. Within this context, researchers have developed and explored several models to forecast land use changes, some of which establish excellent scenario-based predictions of urban growth. However, there is still a lack of operative and user-friendly tools to be integrated into standard urban planning procedures. This paper explores the features of the recently published model FUTure Urban-Regional Environment Simulation integrated into the GRASSGIS environment, which generates urban growth simulation based on a plethora of driving variables. Specifically, the model was applied to the case study of urbanization in the Italian national territory. Hence, the aim of this work is to analyze the importance of population dynamics within the process of urban growth. A simulation of urban growth up to the year 2035 was performed. Results show that, despite the importance given to demographic aspects when defining urban policies over the last several decades, additional factors need to be considered during planning processes to overcome the housing issues currently experienced in Italy.

ACS Style

Claudia Cosentino; Federico Amato; Beniamino Murgante. Population-Based Simulation of Urban Growth: The Italian Case Study. Sustainability 2018, 10, 4838 .

AMA Style

Claudia Cosentino, Federico Amato, Beniamino Murgante. Population-Based Simulation of Urban Growth: The Italian Case Study. Sustainability. 2018; 10 (12):4838.

Chicago/Turabian Style

Claudia Cosentino; Federico Amato; Beniamino Murgante. 2018. "Population-Based Simulation of Urban Growth: The Italian Case Study." Sustainability 10, no. 12: 4838.

Journal article
Published: 31 March 2018 in Environmental Modelling & Software
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Land cover dynamics influence the spatio-temporal evolution of the Rural-Urban Interface (RUI). This represents the most prone area for human-caused forest fires ignitions in Mediterranean countries. Traditionally, RUI mapping is based on the measurement of the distances among specific land covers. This methodology suffers from the definition of pre-established fixed parameters. To avoid this arbitrariness, a new procedure based on Multilayer Perceptron and Fuzzy Set Theory is introduced in this paper. This allows to develop continuous non-categorical maps expressing the possibility of being part of this interface. Thus, an innovative way for assessing the uncertainty in identifying RUI is presented. The proposed methodology has been applied to the case study of Portugal, elaborating a future scenario for the RUI. The results show how the framework proposed in this paper is able to correctly identify the areas belonging to this interface, providing useful information for forest fires -prevention policies.

ACS Style

Federico Amato; Marj Tonini; Beniamino Murgante; Mikhail Kanevski. Fuzzy definition of Rural Urban Interface: An application based on land use change scenarios in Portugal. Environmental Modelling & Software 2018, 104, 171 -187.

AMA Style

Federico Amato, Marj Tonini, Beniamino Murgante, Mikhail Kanevski. Fuzzy definition of Rural Urban Interface: An application based on land use change scenarios in Portugal. Environmental Modelling & Software. 2018; 104 ():171-187.

Chicago/Turabian Style

Federico Amato; Marj Tonini; Beniamino Murgante; Mikhail Kanevski. 2018. "Fuzzy definition of Rural Urban Interface: An application based on land use change scenarios in Portugal." Environmental Modelling & Software 104, no. : 171-187.

Journal article
Published: 01 February 2018 in Applied Geography
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ACS Style

F. Martellozzo; F. Amato; B. Murgante; K.C. Clarke. Modelling the impact of urban growth on agriculture and natural land in Italy to 2030. Applied Geography 2018, 91, 156 -167.

AMA Style

F. Martellozzo, F. Amato, B. Murgante, K.C. Clarke. Modelling the impact of urban growth on agriculture and natural land in Italy to 2030. Applied Geography. 2018; 91 ():156-167.

Chicago/Turabian Style

F. Martellozzo; F. Amato; B. Murgante; K.C. Clarke. 2018. "Modelling the impact of urban growth on agriculture and natural land in Italy to 2030." Applied Geography 91, no. : 156-167.

Conference paper
Published: 19 July 2017 in Transactions on Petri Nets and Other Models of Concurrency XV
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The spread of new satellite and LiDAR data is recently leading to the development of effective methodologies to support the monitoring and management of disaster risks, assessing the level of damages in the very early post-event phase. The increasing availability of SAR images and the diffusion of LiDAR data due to technologies such as solutions such as drones offers the opportunity to experiment new techniques for monitoring the territory. The paper will examine the case study of Amatrice (Central Italy), the Municipality most affected by the seismic swarm started in August 2016, and discuss the results obtained with the technique of interferometric differentiation and detection of change.

ACS Style

Lucia Saganeiti; Federico Amato; Michele Potleca; Gabriele Nolè; Marco Vona; Beniamino Murgante. Change Detection and Classification of Seismic Damage with LiDAR and RADAR Surveys in Supporting Emergency Planning. The Case of Amatrice. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 10407, 722 -731.

AMA Style

Lucia Saganeiti, Federico Amato, Michele Potleca, Gabriele Nolè, Marco Vona, Beniamino Murgante. Change Detection and Classification of Seismic Damage with LiDAR and RADAR Surveys in Supporting Emergency Planning. The Case of Amatrice. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; 10407 ():722-731.

Chicago/Turabian Style

Lucia Saganeiti; Federico Amato; Michele Potleca; Gabriele Nolè; Marco Vona; Beniamino Murgante. 2017. "Change Detection and Classification of Seismic Damage with LiDAR and RADAR Surveys in Supporting Emergency Planning. The Case of Amatrice." Transactions on Petri Nets and Other Models of Concurrency XV 10407, no. : 722-731.

Conference paper
Published: 15 July 2017 in Transactions on Petri Nets and Other Models of Concurrency XV
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The 2030 Agenda by United Nations highlights the necessity of undertake concrete actions to “protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss”. However, human activities on land use are strongly threatening habitat quality, causing their fragmentation and a dramatic loss of biodiversity all over the world. This paper proposes an application of the InVEST Habitat Quality model as a tool to support the definition of sustainable development policies able to favour the preservation of habitat structures while promoting their exploitation as cultural and landscape assets. The model is applied to the Basilicata Region (Southern Italy). Results show how modelling the impacts of human activities on biodiversity and ecosystem services can strongly help planning activities in distinguish those areas that should undergo to a conservation regime to preserve habitat integrity from those which are most prone to transformations, taking advantage by the social and economic benefit deriving from the human activities connected to their use.

ACS Style

Rosa Epifani; Federico Amato; Beniamino Murgante; Gabriele Nolé. A Quantitative Measure of Habitat Quality to Support the Implementation of Sustainable Urban Planning Measures. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 10409, 585 -600.

AMA Style

Rosa Epifani, Federico Amato, Beniamino Murgante, Gabriele Nolé. A Quantitative Measure of Habitat Quality to Support the Implementation of Sustainable Urban Planning Measures. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; 10409 ():585-600.

Chicago/Turabian Style

Rosa Epifani; Federico Amato; Beniamino Murgante; Gabriele Nolé. 2017. "A Quantitative Measure of Habitat Quality to Support the Implementation of Sustainable Urban Planning Measures." Transactions on Petri Nets and Other Models of Concurrency XV 10409, no. : 585-600.

Conference paper
Published: 15 July 2017 in Computer Vision
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The effort towards the reduction of energy consumption, reduction of emissions and the adoption of Renewable Energy production technologies produced significant spatial and urban transformations. In terms of environmental impact assessment, a structural contradiction between a system of governance that promotes renewable plants, an economic system ready to invest huge resources and high profitability, a weak system of territorial planning rules and instruments of landscape protection not yet adequate to govern such transformations. This paper proposes a local case study (the city of Matera) where the ex-ante evaluation of investment programs for the energy regeneration of the public housing stock under the Covenant of Mayors has to be compared with the preservation objectives of an unique historical settlements (“i sassi”). In fact, the city, elected European Capital of Culture 2019, has characteristics of unique historical and architectural value of historical value. On it they act the signs of a PRG dated and the management rules of the UNESCO site most recently adopted (2014). The Municipalities adopted the Sustainable Energy Action Plan (SEAP) - a new category of instrument of urban government which includes strategies and methods of urban transformation - but the intervention scenario not considered the integration of RES plants and technologies with historical settlements. This paper, starting from remote sensing assessment of local radiation index, proposes a methodology to improve the integration between the issue of implementing RES at urban scale and to preserve traditional settlements in a sustainable perspective.

ACS Style

Francesco Scorza; Luigi Santopietro; Beatrice Giuzio; Federico Amato; Beniamino Murgante; Giuseppe Las Casas. Conflicts Between Environmental Protection and Energy Regeneration of the Historic Heritage in the Case of the City of Matera: Tools for Assessing and Dimensioning of Sustainable Energy Action Plans (SEAP). Computer Vision 2017, 10409, 527 -539.

AMA Style

Francesco Scorza, Luigi Santopietro, Beatrice Giuzio, Federico Amato, Beniamino Murgante, Giuseppe Las Casas. Conflicts Between Environmental Protection and Energy Regeneration of the Historic Heritage in the Case of the City of Matera: Tools for Assessing and Dimensioning of Sustainable Energy Action Plans (SEAP). Computer Vision. 2017; 10409 ():527-539.

Chicago/Turabian Style

Francesco Scorza; Luigi Santopietro; Beatrice Giuzio; Federico Amato; Beniamino Murgante; Giuseppe Las Casas. 2017. "Conflicts Between Environmental Protection and Energy Regeneration of the Historic Heritage in the Case of the City of Matera: Tools for Assessing and Dimensioning of Sustainable Energy Action Plans (SEAP)." Computer Vision 10409, no. : 527-539.

Conference paper
Published: 07 July 2017 in Transactions on Petri Nets and Other Models of Concurrency XV
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Due to both the huge amount of strategic and residential buildings that require interventions and limited economic availability, the definition of mitigation strategies based on retrofit priority intervention are a fundamental step. Interventions priority ranking based on concept of seismic risk should be defined. They should be able to address the economic resources by the areas more seismic risky to less ones. In this work, an effective hierarchical spatial distribution of seismic risk of Italian RC buildings stock has been defined based on a spatial georeferencing of Italian seismic hazard, vulnerability, building types exposure, and the employment of Analytical Hierarchical Method. Moreover, a novel and reliable definition of seismic risk index has been employed in building type characterization.

ACS Style

Marco Vona; Monica Mastroberti; Federico Amato. Hierarchical Spatial Distribution of Seismic Risk of Italian RC Buildings Stock. Transactions on Petri Nets and Other Models of Concurrency XV 2017, 10405, 217 -229.

AMA Style

Marco Vona, Monica Mastroberti, Federico Amato. Hierarchical Spatial Distribution of Seismic Risk of Italian RC Buildings Stock. Transactions on Petri Nets and Other Models of Concurrency XV. 2017; 10405 ():217-229.

Chicago/Turabian Style

Marco Vona; Monica Mastroberti; Federico Amato. 2017. "Hierarchical Spatial Distribution of Seismic Risk of Italian RC Buildings Stock." Transactions on Petri Nets and Other Models of Concurrency XV 10405, no. : 217-229.

Conference paper
Published: 01 May 2017 in 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC)
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In this paper we extend the finite-time stability (FTS) theory to two dimensional (2D)-systems. Such class of systems plays an important role in many engineering contexts, such as digital filtering, image processing, gas absorpsion technology, as well as in other fields, like seismological data processing, thermal and industrial processes. Each independent variable of a 2D-systems attains values into a given (possibly finite) interval (for example, an infinite dimensional system depends on a time variable taking values in the interval [0, +∞], and on a space variable attaining values into a given finite interval). Due to the finite-interval definition of some (or all) of the independent variables, it is quite straightforward the idea to exploit, for 2D-systems, the FTS theory developed in the context of the classical one-dimensional system framework. To this regard, we provide a sufficient condition for the FTS, and a sufficient condition for the finite-time stabilization of both continuous and discrete-time linear 2D-system; such conditions will require the solution of feasibility problems based on linear matrix inequalities (LMIs). A numerical example illustrates the benefits of the proposed methodology.

ACS Style

F. Amato; M. Cesarelli; C. Cosentino; A. Merola; M. Romano. On the finite-time stability of two-dimensional linear systems. 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC) 2017, 317 -321.

AMA Style

F. Amato, M. Cesarelli, C. Cosentino, A. Merola, M. Romano. On the finite-time stability of two-dimensional linear systems. 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC). 2017; ():317-321.

Chicago/Turabian Style

F. Amato; M. Cesarelli; C. Cosentino; A. Merola; M. Romano. 2017. "On the finite-time stability of two-dimensional linear systems." 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC) , no. : 317-321.

Journal article
Published: 01 March 2017 in Journal of Cultural Heritage
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Landscape preservation in Italy is a major issue in national cultural heritage conservation policies. Urban settlements growth is among the most threatening factors for the correct landscape preservation. Such phenomenon may result in corrupting the correct landscape-system functioning, particularly when the development occurs without precise planning prescriptions. Land-use/cover evolution dynamic is a subject widely and thoroughly investigated, especially concerning consumption of natural and other lands due to anthropogenic activities. This paper focuses on a region in southern Italy, where soil consumption is known to represent a urging matter of concern. However, although the negative impacts of soil consumption are well known, to our knowledge there are no case studies presenting a precise quantitative assessment of the intensity of such phenomenon for the region of interest. Furthermore, this study aims at forecasting the development of urban settlements through the application of the cellular automata model SLEUTH; the case study concerns the Municipality of Altamura (Apulia region, Italy). Results highlight how current landscape preservation instruments alone cannot ensure a reduction in soil consumption phenomenon and how urban areas expansion is incompatible with a correct landscape conservation in the study area

ACS Style

Federico Amato; Federico Martellozzo; Gabriele Nolè; Beniamino Murgante. Preserving cultural heritage by supporting landscape planning with quantitative predictions of soil consumption. Journal of Cultural Heritage 2017, 23, 44 -54.

AMA Style

Federico Amato, Federico Martellozzo, Gabriele Nolè, Beniamino Murgante. Preserving cultural heritage by supporting landscape planning with quantitative predictions of soil consumption. Journal of Cultural Heritage. 2017; 23 ():44-54.

Chicago/Turabian Style

Federico Amato; Federico Martellozzo; Gabriele Nolè; Beniamino Murgante. 2017. "Preserving cultural heritage by supporting landscape planning with quantitative predictions of soil consumption." Journal of Cultural Heritage 23, no. : 44-54.

Book chapter
Published: 05 October 2016 in The Life and Afterlife of Gay Neighborhoods
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The issue of suburb regeneration is highly relevant today. Current urban planning approaches are mainly focused on large and permanent transformations. These solutions often do not take into account the great dynamism and rapid transformations of cities, making any intervention, in most cases, outdated even before its realisation. Nevertheless, these expensive interventions implemented by local government frequently tend to fail the regeneration of these public spaces. This chapter presents the experience of ‘Serpentone reload’, a workshop based on participatory reactivation of abandoned and underused spaces and buildings in the ‘Cocuzzo/Serpentone’ neighbourhood of Potenza in Basilicata, Italy. The workshop focussed particularly on the reuse of the ‘Ship’, an underground building completed in 2010 but never used because, since then, it has been perceived as an extraneous element, being the result of an imposition and not the outcome of shared choices. The aim of this chapter is to propose a methodology that will allow the reactivation of public spaces, the empowerment of communities and the increase in citizens’ interest in planning choices, pointing therefore alternative ways for urban governance and for local government urban policies. The results show that the involvement of people in urban planning and in Placemaking activities raises the level of social cohesion, generating both social benefits and quality of public spaces.

ACS Style

Gerardo Sassano; Antonio Graziadei; Federico Amato; Beniamino Murgante. Involving Citizens in the Reuse and Regeneration of Urban Peripheral Spaces. The Life and Afterlife of Gay Neighborhoods 2016, 193 -206.

AMA Style

Gerardo Sassano, Antonio Graziadei, Federico Amato, Beniamino Murgante. Involving Citizens in the Reuse and Regeneration of Urban Peripheral Spaces. The Life and Afterlife of Gay Neighborhoods. 2016; ():193-206.

Chicago/Turabian Style

Gerardo Sassano; Antonio Graziadei; Federico Amato; Beniamino Murgante. 2016. "Involving Citizens in the Reuse and Regeneration of Urban Peripheral Spaces." The Life and Afterlife of Gay Neighborhoods , no. : 193-206.

Journal article
Published: 06 July 2016 in ISPRS International Journal of Geo-Information
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The negative impacts of land take on natural components and economic resources affect planning choices and territorial policies. The importance of land take monitoring, in Italy, has been only recently considered, but despite this awareness, in the great part of the country, effective monitoring and containment measures have not been started, yet. This research proposes a methodology to map and monitor land use changes. To this end, a time series from 1985–2010, based on the multi-temporal Landsat data Thematic Mapper (TM), has been analyzed in the Vulture Alto-Bradano area, a mountain zone of the Basilicata region (Southern Italy). Results confirm a double potentiality of using these data: on the one hand, the use of multi-temporal Landsat data allows going very back in time, producing accurate datasets that provide a phenomenon trend over time; on the other hand, these data can be considered a first experience of open data in the field of spatial information. The proposed methodology provides agencies, local authorities and practitioners with a valuable tool to implement monitoring actions. This represents the first step to pursue territorial governance methods based on sustainability, limiting the land take.

ACS Style

Flavia Di Palma; Federico Amato; Gabriele Nolè; Federico Martellozzo; Beniamino Murgante. A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation. ISPRS International Journal of Geo-Information 2016, 5, 109 .

AMA Style

Flavia Di Palma, Federico Amato, Gabriele Nolè, Federico Martellozzo, Beniamino Murgante. A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation. ISPRS International Journal of Geo-Information. 2016; 5 (7):109.

Chicago/Turabian Style

Flavia Di Palma; Federico Amato; Gabriele Nolè; Federico Martellozzo; Beniamino Murgante. 2016. "A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation." ISPRS International Journal of Geo-Information 5, no. 7: 109.

Conference paper
Published: 01 July 2016 in Computer Vision
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Among the objectives of the Sustainable Development Goals by United Nations, “Affordable and Clean Energy” aims at ensuring access to affordable, reliable, sustainable and modern energy for all. However, in Europe there is not a precise understanding of the unleashed potential that could be achieved through the exploitation of solar and wind resources. This study presents an application to retrieve spatial explicit estimates of Direct Normal Irradiance (DNI) through the use of data from geo-stationary satellites. The energetic demand of large metropolitan areas in Europe is then retrieved and compared with the potential production of energy for domestic use through solar panels. Results of this comparison are presented based on the assumption that only the 1 % of the built up area could be covered with solar panels, and hence devoted to energy production. Outcomes suggest that even such a little coverage, if spread systematically over urban areas can in most of the cases satisfy urban population domestic needs

ACS Style

Federico Amato; Federico Martellozzo; Beniamino Murgante; Gabriele Nolè. Urban Solar Energy Potential in Europe. Computer Vision 2016, 443 -453.

AMA Style

Federico Amato, Federico Martellozzo, Beniamino Murgante, Gabriele Nolè. Urban Solar Energy Potential in Europe. Computer Vision. 2016; ():443-453.

Chicago/Turabian Style

Federico Amato; Federico Martellozzo; Beniamino Murgante; Gabriele Nolè. 2016. "Urban Solar Energy Potential in Europe." Computer Vision , no. : 443-453.

Journal article
Published: 24 March 2016 in Sustainability
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For more than a decade, the European Union recognizes soil as a common good and considers it as a finite resource of inestimable value. The European Union defines it as the “upper layer of earth’s crust, formed by mineral particles, organic matter, water, air and living organisms”. Despite such definitions, usually, planning choices do not take into account the need to reduce soil consumption to build up resilience. This paper presents the controversial case of Agri Valley (Basilicata, Southern Italy); on the one hand, this region is characterized by the presence of extremely valuable land, because of the exceptional degree of soil fertility; on the other hand, Valdagri is also known to have one of the largest oilfields of Europe. An application built around the SLEUTH model was developed in order to produce a simulation and an estimate of the extent to which urban areas may grow in the near future. Results confirm that urban policies implemented so far by local governments—which aimed almost exclusively to favor industrial development—irreversibly threaten the integrity of the natural values of the valley.

ACS Style

Federico Amato; Biagio Antonio Maimone; Federico Martellozzo; Gabriele Nolè; Beniamino Murgante. The Effects of Urban Policies on the Development of Urban Areas. Sustainability 2016, 8, 297 .

AMA Style

Federico Amato, Biagio Antonio Maimone, Federico Martellozzo, Gabriele Nolè, Beniamino Murgante. The Effects of Urban Policies on the Development of Urban Areas. Sustainability. 2016; 8 (4):297.

Chicago/Turabian Style

Federico Amato; Biagio Antonio Maimone; Federico Martellozzo; Gabriele Nolè; Beniamino Murgante. 2016. "The Effects of Urban Policies on the Development of Urban Areas." Sustainability 8, no. 4: 297.