arXiv subjects

Aurelienne A. S. Jorge

Publications and source records attributed to Aurelienne A. S. Jorge.

4 recordsLinked to original sources

arXiv2023

Precipitation event-based networks: an analysis of the relations between network metrics and meteorological properties

The study of complex systems in nature is essential to understand the interactions between different elements and how they influence one another. Complex network theory is a powerful tool that helps us to analyze these interactions and gain insights into the behavior of such systems. Surprisingly, this theory has been underutilized in the field of weather science, which focuses on the immediate state of the atmosphere. Our research aims to fill this gap by exploring the use of complex network theory in weather science. Specifically, we employ weather radar data to construct event-based geographical networks. By analyzing the relations between meteorological properties and network metrics in these event-based networks, we can gain a better understanding of the behavior of precipitation events. Our findings reveal significant correlations between various meteorological properties and network metrics, shedding light on the underlying mechanisms that govern precipitation events. Through our work, we hope to demonstrate the potential of complex network theory in weather science and inspire further research in this field.

cs.SI↗

arXiv2023

Vulnerability analysis in Complex Networks under a Flood Risk Reduction point of view

The measurement and mapping of transportation network vulnerability to natural hazards constitute subjects of global interest, especially due to climate change, and for a sustainable development agenda. During a flood, some elements of a transportation network can be affected, causing loss of life of people and damage to vehicles, streets/roads, and other logistics services, sometimes with severe economic impacts. The Network Science approach may offer a valuable perspective considering one type of vulnerability related to network type critical infrastructures: the topological vulnerability. The topological vulnerability index associated with an element is defined as the reduction in the network's average efficiency due to the removal of the set of edges related to that element. We present a topological vulnerability index analysis for the highways in the state of Santa Catarina, Brazil, and produce a map considering that index and the areas susceptible to urban floods and landslides. The risk knowledge, combining hazard and vulnerability, is the first pillar of an Early Warning System, and represent an important tool for stakeholders from the transportation sector in a disaster risk reduction agenda.

physics.soc-ph↗

arXiv2022

Flood risk map from hydrological and mobility data: a case study in São Paulo (Brazil)

Cities increasingly face flood risk primarily due to extensive changes of the natural land cover to built-up areas with impervious surfaces. In urban areas, flood impacts come mainly from road interruption. This paper proposes an urban flood risk map from hydrological and mobility data, considering the megacity of São Paulo, Brazil, as a case study. We estimate the flood susceptibility through the Height Above the Nearest Drainage algorithm; and the potential impact through the exposure and vulnerability components. We aggregate all variables into a regular grid and then classify the cells of each component into three classes: Moderate, High, and Very High. All components, except the flood susceptibility, have few cells in the Very High class. The flood susceptibility component reflects the presence of watercourses, and it has a strong influence on the location of those cells classified as Very High.

physics.soc-ph↗

arXiv2022

Global-threshold and backbone high-resolution weather radar networks are significantly complementary in a watershed

There are several criteria for building up networks from time series related to different points in geographical space. The most used criterion is the Global-Threshold (GT). Using a weather radar dataset, this paper shows that the Backbone (BB) - a local-threshold criterion - generates networks whose geographical configuration is complementary to the GT networks. We compare the results for two well-known similarities measures: the Pearson Correlation (PC) coefficient and the Mutual Information (MI). The extracted backbone network (miBB), whose number of links is the same as the global MI (miGT), has the lowest average shortest path and presents a small-world effect. Regarding the global PC (pcGT) and its corresponding BB network (pcBB), there is a significant linear relationship: $R2=0.77$ with a slope of $1.15$ (p-value $<E-7$) for the pcGT network, and $R2=0.68$ with a slope of $0.76$ (p-value $<E-7$) for the pcBB network. In relation to the MI ones, only the miGT present a high $R2$ ($0.79$, with slope = $1.95$), whereas the miBB has an $R2$ of only $0.20$ ($\text{slope} =0.24$). On the one hand, the GT networks present a sizeable connected component in the central area, close to the main rivers. On the other hand, the BB networks present a few meaningful connected components surrounding the watershed and dominating cells close to the outlet, with significant statistical differences in the altimetry distribution.

cs.SI↗