arXiv subjects

Jingfang Fan

Publications and source records attributed to Jingfang Fan.

53 records · Page 3Linked to original sources

arXiv2019

Scaling Laws in Earthquake Memory for Interevent Times and Distances

Over the past decades much effort has been devoted towards understanding and forecasting natural hazards. However, earthquake forecasting skill is still very limited and remains a great scientific challenge. The limited earthquake predictability is partly due to the erratic nature of earthquakes and partly to the lack of understanding the underlying mechanisms of earthquakes. To improve our understanding and potential forecasting, here we study the spatial and temporal long-term memory of interevent earthquakes above a certain magnitude using lagged conditional probabilities. We find, in real data, that the lagged conditional probabilities show long-term memory for both the interevent times and interevent distances and that the memory functions obey scaling and decay slowly with time, while, at a characteristic time, the decay crossesover to a fast decay. We also show that the ETAS model, which is often used to forecast earthquake events, yields scaling functions of the temporal and spatial interevent intervals which are not consistent with those of real data.

physics.soc-ph↗

arXiv2019

Complexity based approach for El Nino magnitude forecasting before the "spring predictability barrier"

The El Nino Southern Oscillation (ENSO) is one of the most prominent interannual climate phenomena. An early and reliable ENSO forecasting remains a crucial goal, due to its serious implications for economy, society, and ecosystem. Despite the development of various dynamical and statistical prediction models in the recent decades, the ``spring predictability barrier'' (SPB) remains a great challenge for long (over 6-month) lead-time forecasting. To overcome this barrier, here we develop an analysis tool, the System Sample Entropy (SysSampEn), to measure the complexity (disorder) of the system composed of temperature anomaly time series in the Nino 3.4 region. When applying this tool to several near surface air-temperature and sea surface temperature datasets, we find that in all datasets a strong positive correlation exists between the magnitude of El Nino and the previous calendar year's SysSampEn (complexity). We show that this correlation allows to forecast the magnitude of an El Nino with a prediction horizon of 1 year and high accuracy (i.e., Root Mean Square Error $=0.23^\circ C$ for the average of the individual datasets forecasts). For the on-going 2018 El Nino event, our method forecasts a weak El Nino with a magnitude of $1.11\pm 0.23^\circ C$. Our framework presented here not only facilitates a long--term forecasting of the El Nino magnitude but can potentially also be used as a measure for the complexity of other natural or engineering complex systems.

physics.ao-ph↗

arXiv2019

Significant Impact of Rossby Waves on Air Pollution Detected by Network Analysis

Air pollution is associated with human diseases and has been found to be related to premature mortality. In response, environmental policies have been adopted in many countries, to decrease anthropogenic air pollution for the improvement of long-term air quality, since most air pollutant sources are anthropogenic. However, air pollution fluctuations have been found to strongly depend on the weather dynamics. This raises a fundamental question: What are the significant atmospheric processes that affect the local daily variability of air pollution? For this purpose, we develop here a multi-layered network analysis to detect the interlinks between the geopotential height of the upper air (~5 km) and surface air pollution in both China and the USA. We find that Rossby waves significantly affect air pollution fluctuations through the development of cyclone and anticyclone systems, and further affect the local stability of the air and the winds. The significant impacts of Rossby waves on air pollution are found to underlie most of the daily fluctuations in air pollution. Thus, the impact of Rossby waves on human life is greater than previously assumed. The rapid warming of the Arctic could slow down Rossby waves, thus increasing human health risks. Our method can help to determine the risk assessment of such extreme events and can improve potential predictability.

physics.ao-ph↗

arXiv2019

Percolation Framework of the Earth's Topography

Self-similarity and long-range correlations are the remarkable features of the Earth's surface topography. Here we develop an approach based on percolation theory to study the geometrical features of Earth. Our analysis is based on high-resolution, 1 arc min, ETOPO1 global relief records.We find some evidence for abrupt transitions that occurred during the evolution of the Earth's relief network, indicative of a continental/cluster aggregation. We apply finite-size-scaling analysis based on a coarse-graining procedure to show that the observed transition is most likely discontinuous. Furthermore, we study the percolation on two-dimensional fractional Brownian motion surfaces with Hurst exponent $H$ as a model of long-range correlated topography, which suggests that the long-range correlations may play a key role in the observed discontinuity on Earth. Our framework presented here provides a theoretical model to better understand the geometrical phase transition on Earth, and it also identifies the critical nodes that will be more exposed to global climate change in the Earth's relief network.

physics.soc-ph↗

arXiv2018

A combined network and machine learning approaches for product market forecasting

Sustainable financial markets play an important role in the functioning of human society. Still, the detection and prediction of risk in financial markets remain challenging and draw much attention from the scientific community. Here we develop a new approach based on combined network theory and machine learning to study the structure and operations of financial product markets. Our network links are based on the similarity of firms' products and are constructed using the Securities Exchange Commission (SEC) filings of US listed firms. We find that several features in our network can serve as good precursors of financial market risks. We then combine the network topology and machine learning methods to predict both successful and failed firms. We find that the forecasts made using our method are much better than other well-known regression techniques. The framework presented here not only facilitates the prediction of financial markets but also provides insight and demonstrate the power of combining network theory and machine learning.

physics.soc-ph↗

arXiv2018

Possible Origin of Memory in Earthquakes: Real catalogs and ETAS model

Earthquakes are one of the most devastating natural disasters that plague society. A skilled, reliable earthquake forecasting remains the ultimate goal for seismologists. Using the detrended fluctuation analysis (DFA) and conditional probability (CP) methods we find that memory exists not only in inter-occurrence seismic records, but also in released energy as well as in the series of the number of events per unit time. Analysis of the conventional earthquake model (Epidemic Type Aftershock Sequences, ETAS) indicates that earthquake memory can be reproduced only for a narrow range of model's parameters. This finding, therefore provides additional accuracy on the model parameters through tight restrictions on their values in different worldwide regions and can serve as a testbed for existing earthquake forecasting models.

physics.geo-ph↗

arXiv2018

Climate network percolation reveals the expansion and weakening of the tropical component under global warming

Global climate warming poses a significant challenge to humanity; it is associated with, e.g., rising sea level and declining Arctic sea ice. Increasing extreme events are also considered to be a result of climate warming~\cite{pachauri2014climate,ogorman_contrasting_2014} and they may have widespread and diverse effects on health, agriculture, economics and political conflicts \cite{hsiang2011civil,helbing_globally_2013,schleussner_armed-conflict_2016,carleton_social_2016}. Still, the detection and quantification of climate change, both in observations and climate models, constitute a main focus of the scientific community. Here we develop a new approach based on network and percolation frameworks \cite{cohen2010complex} to study the impacts of climate changes in the past decades using historical models and reanalysis records, and we analyze the expected upcoming impacts using various future global warming scenarios. More specifically, we classify the globe area into evolving percolation clusters and find a discontinuous phase transition, which indicates a consistent poleward expansion of the largest (tropical) cluster, as well as the weakening of the link's strength. This is found both in the reanalysis data and in the Coupled Model Intercomparison Project Phase 5 (CMIP5) twenty-first century climate change simulations~\cite{taylor2012overview}. The analysis is based on high resolution surface (2~m) air temperature field records. We discuss the underlying mechanism for the observed expansion of the tropical cluster and associate it with changes in atmospheric circulation represented by the weakening and expansion of the Hadley cell. Our framework can also be useful for forecasting the extent of the tropical cluster in order to detect its influence on different areas in response to global warming.

physics.soc-ph↗

arXiv2018

Resilience of spatial networks with inter-links behaving as an external field

Many real systems such as, roads, shipping routes, and infrastructure systems can be modeled based on spatially embedded networks. The inter-links between two distant spatial networks, such as those formed by transcontinental airline flights, play a crucial role in optimizing communication and transportation over such long distances. Still, little is known about how inter-links affect the resilience of such systems. Here, we develop a framework to study the resilience of interlinked spatially embedded networks based on percolation theory. We find that the inter-links can be regarded as an external field near the percolation phase transition, analogous to a magnetic field in a ferromagnetic-paramagnetic spin system. By defining the analogous critical exponents $δ$ and $γ$, we find that their values for various inter-links structures follow Widom's scaling relations. Furthermore, we study the optimal robustness of our model and compare it with the analysis of real-world networks. The framework presented here not only facilitates the understanding of phase transitions with external fields in complex networks but also provides insight into optimizing real-world infrastructure networks and a magnetic field in a management.

physics.soc-ph↗

arXiv2018

Resilience of networks with community structure behaves as if under an external field

Detecting and characterizing community structure plays a crucial role in the study of networked systems. However, there is still a lack of understanding of how community structure affects the systems' resilience and stability. Here, we develop a framework to study the resilience of networks with community structure based on percolation theory. We find both analytically and numerically that the interlinks (connections between the communities) affect the percolation phase transition in a manner similar to an external field in a ferromagnetic-paramagnetic spin system. We also study the universality class by defining the analogous critical exponents $δ$ and $γ$, and find that their values for various models and in real-world co-authors networks follow fundamental scaling relations as in physical phase transitions. The methodology and results presented here not only facilitate the study of resilience of networks but also brings a fresh perspective to the understanding of phase transitions under external fields.

physics.soc-ph↗

arXiv2018

Forecasting the magnitude and onset of El Nino based on climate network

El Nino is probably the most influential climate phenomenon on interannual time scales. It affects the global climate system and is associated with natural disasters and serious consequences in many aspects of human life. However, the forecasting of the onset and in particular the magnitude of El Nino are still not accurate, at least more than half a year in advance. Here, we introduce a new forecasting index based on network links representing the similarity of low frequency temporal temperature anomaly variations between different sites in the El Nino 3.4 region. We find that significant upward trends and peaks in this index forecast with high accuracy both the onset and magnitude of El Nino approximately 1 year ahead. The forecasting procedure we developed improves in particular the prediction of the magnitude of El Nino and is validated based on several, up to more than a century long, datasets.

physics.geo-ph↗

arXiv2018

Correlation and scaling behaviors of $PM_{2.5}$ concentration in China

Air pollution has become a major issue and caused widespread environmental and health problems. Aerosols or particulate matters are an important component of the atmosphere and can transport under complex meteorological conditions. Based on the data of $PM_{2.5}$ observations, we develop a network approach to study and quantify their spreading and diffusion patterns. We calculate cross-correlation functions of time lag between sites within different season. The probability distribution of correlation changes with season. It is found that the probability distributions in four seasons can be scaled into one scaling function with averages and standard deviations of correlation. This seasonal scaling behavior indicates there is the same mechanism behind correlations of $PM_{2.5}$ concentration in different seasons. Further, from weighted and directional degrees of complex network, different properties of $PM_{2.5}$ concentration are studied. The weighted degrees reveal the strongest correlations of $PM_{2.5}$ concentration in winter and in the North China plain. These directional degrees show net influences of $PM_{2.5}$ along Gobi and inner Mongolia, the North China plain, Central China, and Yangtze River Delta.

physics.ao-ph↗

arXiv2017

Abrupt transitions in collaborative social networks

Despite the wide use of networks as a versatile tool for exploring complex social systems, little is known about how to detect and forecast abrupt changes in social systems. In this report, we develop an early warning approach based on network properties to detect such changes. By analysing three collaborative social networks---one co-stardom, one patent and one scientific collaborative network, we discover that abrupt transitions inherent in these networks can serve as a good early warning signal, indicating, respectively, the dissolution of the Soviet Union, the emergence of the "soft matter" research field, and the merging of two scientific communities. We then develop a clique growth model that explains the universal properties of these real networks and find that they belong to a new universality class, described by the Gumbel distribution.

physics.soc-ph↗

arXiv2016

Percolation framework to describe El Niño conditions

Complex networks have been used intensively to investigate the flow and dynamics of many natural systems including the climate system. Here, we develop a percolation based measure, the order parameter, to study and quantify climate networks. We find that abrupt transitions of the order parameter usually occur $\sim$1 year before El Niño ~ events, suggesting that they can be used as early warning precursors of El Niño. Using this method we analyze several reanalysis datasets and show the potential for good forecasting of El Niño. The percolation based order parameter exhibits discontinuous features, indicating possible relation to the first order phase transition mechanism.

physics.ao-ph↗

arXiv2016

Climate network suggests enhanced El Niño global impacts in localized areas

We construct directed and weighted climate networks based on near surface air temperature to investigate the global impacts of El Nino and La Nina. We find that regions which are characterized by higher positive or negative network in weighted links, are exhibiting stronger correlations with the El Nino basin and are warmer or cooler during El Nino or La Nina periods. These stronger in-weighted activities are found to be concentrated in localized areas, as compared to non-El Nino periods, whereas a large fraction of the globe is not influenced by the events. The regions of localized activity vary from one El Nino (La Nina) event to another; still some El Nino (La Nina) events are more similar to each other. We quantify this similarity using network community structure. The results and methodology reported here may be used to improve the understanding and prediction of El Nino or La Nina events and also may be applied in the investigation of other climate variables.

physics.ao-ph↗

arXiv2013

General Clique Percolation in Network Evolution

We introduce a general $(k,l)$ clique community, which consists of adjacent $k$-cliques sharing at least $l$ vertices with $k-1 \ge l \ge 1$. The emergence of a giant $(k,l)$ clique community indicates a $(k,l)$ clique percolation, which is studied by the largest size gap $Δ$ of the largest clique community during network evolution and the corresponding evolution step $T_c$. For a clique percolation, the averages of $Δ$ and $T_c$ and the root-mean-squares of their fluctuations have power law finite-size effects whose exponents are related to the critical exponents. The fluctuation distribution functions of $Δ$ and $T_c$ follow a finite-size scaling form. In the evolution of the Erdős-Rényi network, there are a series of $(k,l)$ clique percolation with $(k,l)=(2,1),(3,1),(3,2),(4,1),(4,2),(5,1),(4,3)$, and so on. The critical exponents of clique percolation depend on $l$, but are independent of $k$. The universality class of a $(k,l)$ clique percolation is characterized alone by $l$.

cond-mat.stat-mech↗

arXiv2012

Continuous Percolation Phase Transitions of Two-dimensional Lattice Networks under a Generalized Achlioptas Process

The percolation phase transitions of two-dimensional lattice networks under a generalized Achlioptas process (GAP) are investigated. During the GAP, two edges are chosen randomly from the lattice and the edge with minimum product of the two connecting cluster sizes is taken as the next occupied bond with a probability $p$. At $p=0.5$, the GAP becomes the random growth model and leads to the minority product rule at $p=1$. Using the finite-size scaling analysis, we find that the percolation phase transitions of these systems with $0.5 \le p \le 1$ are always continuous and their critical exponents depend on $p$. Therefore, the universality class of the critical phenomena in two-dimensional lattice networks under the GAP is related to the probability parameter $p$ in addition.

cond-mat.stat-mech↗

arXiv2012

Continuous percolation phase transitions of random networks under a generalized Achlioptas process

Using the finite-size scaling, we have investigated the percolation phase transitions of evolving random networks under a generalized Achlioptas process (GAP). During this GAP, the edge with minimum product of two connecting cluster sizes is taken with a probability $p$ from two randomly chosen edges. This model becomes the Erd\H os-Rényi network at $p=0.5$ and the random network under the Achlioptas process at $p=1$. Using both the fixed point of $s_2/s_1$ and the straight line of $\ln s_1$, where $s_1$ and $s_2$ are the reduced sizes of the largest and the second largest cluster, we demonstrate that the phase transitions of this model are continuous for $0.5 \le p \le 1$. From the slopes of $\ln s_1$ and $\ln (s_2/s_1)'$ at the critical point we get the critical exponents $β$ and $ν$, which depend on $p$. Therefore the universality class of this model should be characterized by $p$ also.

cond-mat.stat-mech↗