24 research outputs found
Climate Change And Land Use/cover Change Impacts On Watershed Hydrology, Nutrient Dynamics – A Case Study In Missisquoi River Watershed
Watershed regulation of water, carbon and nutrient dynamics support food, drinking water and human development. Projected climate changes and land use/cover change (LUCC) have been identified as drivers of watershed nutrient and hydrological processes and are likely to happen jointly in the future decades. Studying climate change and LUCC impacts on watersheds\u27 streamflow and nutrients dynamics is therefore essential for future watershed management.
This research aimed to unveil how climate change and LUCC affect water and nutrient dynamics in the Missisquoi River watershed, Vermont. We used 12 scenarios of future climate data (2021 – 2050) generated by three GCMs (ccsm4, mri-cgcm3, and gfdl-esm2m) under four Representative Concentration Pathways (RCPs). For LUCC, we used three different scenarios generated by the Interactive Land Use Transition Agent-Based Model (ILUTABM). The three LUCC scenarios were Business As Usual (BAU), Prefer Forest (proForest), and Prefer Agriculture (proAg). New land use maps were generated every 10 years for the period of 2021 – 2050. Combining each climate change and LUCC scenario resulted in 36 scenarios that were used to drive Regional Hydro-Ecologic Simulation System (RHESSys) ecohydrological model.
In chapter 3, we used RHESSys to study streamflow. We found climate was the main driver for streamflow because climate change directly controlled the system water input. For streamflow, climate change scenarios had larger impacts than LUCC, different LUCCs under the same climate change scenario had similar annual flow patterns.
In chapter 4, we used RHESSys to study streamflow NO3-N and NH4-N load. Because fertilizer application is the major source for nitrogen export, LUCC had larger impacts; watersheds with more agricultural land had larger nitrogen loads.
In chapter 5, we developed RHESSys-P by coupling the DayCent phosphorus module with RHESSys to study climate change and LUCC impacts on Dissolved Phosphorus (DP) load. RHESSys-P was calibrated with observed DP data for 2002 – 2004 and validated with data for 2009 - 2010. In both calibration and validation periods, simulated DP basically captured patterns of observed DP. In the validation period, the R2 of simulated vs observed DP was 0.788. Future projection results indicated BAU and proForest annual loads were around 4.0 × 104 kg under all climate change scenarios; proAg annual loads increased from around 4.0 × 104 kg in 2021 to 1.6 × 105 kg in 2050 under all climate change scenarios. The results showed LUCC was the dominant factor for dissolved phosphorus loading.
Overall, our results suggest that, while climate drives streamflow, N and P fluxes are largely driven by land use and management decisions. To balance human development and environmental quality, BAU is a feasible future development strategy
Similarity-Based Classification in Partially Labeled Networks
We propose a similarity-based method, using the similarity between nodes, to
address the problem of classification in partially labeled networks. The basic
assumption is that two nodes are more likely to be categorized into the same
class if they are more similar. In this paper, we introduce ten similarity
indices, including five local ones and five global ones. Empirical results on
the co-purchase network of political books show that the similarity-based
method can give high accurate classification even when the labeled nodes are
sparse which is one of the difficulties in classification. Furthermore, we find
that when the target network has many labeled nodes, the local indices can
perform as good as those global indices do, while when the data is sparce the
global indices perform better. Besides, the similarity-based method can to some
extent overcome the unconsistency problem which is another difficulty in
classification.Comment: 13 pages,3 figures,1 tabl
Local dominance unveils clusters in networks
Clusters or communities can provide a coarse-grained description of complex
systems at multiple scales, but their detection remains challenging in
practice. Community detection methods often define communities as dense
subgraphs, or subgraphs with few connections in-between, via concepts such as
the cut, conductance, or modularity. Here we consider another perspective built
on the notion of local dominance, where low-degree nodes are assigned to the
basin of influence of high-degree nodes, and design an efficient algorithm
based on local information. Local dominance gives rises to community centers,
and uncovers local hierarchies in the network. Community centers have a larger
degree than their neighbors and are sufficiently distant from other centers.
The strength of our framework is demonstrated on synthesized and empirical
networks with ground-truth community labels. The notion of local dominance and
the associated asymmetric relations between nodes are not restricted to
community detection, and can be utilised in clustering problems, as we
illustrate on networks derived from vector data
Empirical analysis of web-based user-object bipartite networks
Understanding the structure and evolution of web-based user-object networks
is a significant task since they play a crucial role in e-commerce nowadays.
This Letter reports the empirical analysis on two large-scale web sites,
audioscrobbler.com and del.icio.us, where users are connected with music groups
and bookmarks, respectively. The degree distributions and degree-degree
correlations for both users and objects are reported. We propose a new index,
named collaborative clustering coefficient, to quantify the clustering behavior
based on the collaborative selection. Accordingly, the clustering properties
and clustering-degree correlations are investigated. We report some novel
phenomena well characterizing the selection mechanism of web users and outline
the relevance of these phenomena to the information recommendation problem.Comment: 6 pages, 7 figures and 1 tabl
Link Prediction in Complex Networks: A Survey
Link prediction in complex networks has attracted increasing attention from
both physical and computer science communities. The algorithms can be used to
extract missing information, identify spurious interactions, evaluate network
evolving mechanisms, and so on. This article summaries recent progress about
link prediction algorithms, emphasizing on the contributions from physical
perspectives and approaches, such as the random-walk-based methods and the
maximum likelihood methods. We also introduce three typical applications:
reconstruction of networks, evaluation of network evolving mechanism and
classification of partially labelled networks. Finally, we introduce some
applications and outline future challenges of link prediction algorithms.Comment: 44 pages, 5 figure
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Identifying influential nodes in complex networks
Identifying influential nodes that lead to faster and wider spreading in complex networks is of theoretical and practical significance. The degree centrality method is very simple but of little relevance. Global metrics such as betweenness centrality and closeness centrality can better identify influential nodes, but are incapable to be applied in large-scale networks due to the computational complexity. In order to design an effective ranking method, we proposed a semi-local centrality measure as a tradeoff between the low-relevant degree centrality and other time-consuming measures. We use the Susceptible–Infected–Recovered (SIR) model to evaluate the performance by using the spreading rate and the number of infected nodes. Simulations on four real networks show that our method can well identify influential nodes
Relevance is more significant than correlation: Information filtering on sparse data
In some recommender systems where users can vote objects by ratings, the similarity between users can be quantified by a benchmark index, namely the Pearson correlation coefficient, which reflects the rating correlations. Another alternative way is to calculate the similarity based solely on the relevance information, namely whether a user has voted an object. The former one uses more information than the latter, and is intuitively expected to give more accurate rating predictions under the standard collaborative filtering framework. However, according to the extensive experimental analysis, this letter reports the opposite results that the latter method, making use of only the relevance information, can outperform the former method, especially when the data set is sparse. Our finding challenges the routine knowledge on information filtering, and suggests some alternatives to address the sparsity problem
Empirical comparison of local structural similarity indices for collaborative-filtering-based recommender systems
Collaborative filtering is one of the most successful recommendation techniques, which can effectively predict the possible future likes of users based on their past preferences. The key problem of this method is how to define the similarity between users. A standard approach is using the correlation between the ratings that two users give to a set of objects, such as Cosine index and Pearson correlation coefficient. However, the costs of computing this kind of indices are relatively high, and thus it is impossible to be applied in the huge-size systems. To solve this problem, in this paper, we introduce six local-structure-based similarity indices and compare their performances with the above two benchmark indices. Experimental results on two data sets demonstrate that the structure-based similarity indices overall outperform the Pearson correlation coefficient. When the data is dense, the structure-based indices can perform competitively good as Cosine index, while with lower computational complexity. Furthermore, when the data is sparse, the structure-based indices give even better results than Cosine index