Watershed health and ecological security zoning throughout Iran.

Today, land degradation and the decrease in the expected services of watersheds have been mainly influenced by human-induced activities. Hence, it requires more attention to adaptively manage and provide feasible solutions to watershed disruptions. However, appropriate management of precious commodities such as water, soil, air, and vegetation cover needs insight planning on a proper scale. Nonetheless, such an integrated approach to comprehensive health assessment of watershed resources is yet to be indoctrinated by scholars, implemental agencies, managers, and policymakers. Accordingly, the present endeavor has tried to evaluate the health status of Iran's 30 second-order large watersheds with the pressure-state-response (PSR) approach. In this regard, 44 problem-oriented, influential, and, at the same time, accessible variables with compatible scales at the national level were primarily determined in climatic, hydrologic, anthropogenic, and natural sectors. The collinearity-free and independent variables were then finalized using the variance inflation factor (VIF) test. Ultimately, P, S, and R indices were calculated using the arithmetic mean of 25 normalized variables based on which PSR-based health and security indices were also mapped countrywide. The results indicated that P, S, and R indices varied from 0.49 to 0.69, 0.42 to 0.82, and 0.40 to 0.94, respectively. Health and security indices ranged from 0.46 to 0.69 and 0.30 to 0.89, respectively. The weighted mean of P, S, and R was 0.59, 0.62, and 0.67, respectively, wholly placing them in the intermediate class. The weighted health and security indices were also 0.58 and 0.59, representing the intermediate class. The results showed that study watersheds had different health and security conditions from interplaying watershed-specific factors. The results revealed the necessity of watershed-unique managerial strategies to cope with the existing unfavorable conditions at the country level. However, further insight with high resolution is recommended for the high-priority watersheds to plan implementation and executive projects.

High-resolution soil moisture mapping using PSO-based optimized cerebellar model articulation controller (CMAC).

A good knowledge in eco-hydrological processes requires significant understanding of geospatial distribution of soil moisture (SM). However, SM monitoring remains challenging due to its large spatial variability and its dynamic time response. This study was performed to assess the performance of a particle swarm optimization (PSO)-based optimized Cerebellar Model Articulation Controller (CMAC) in generating high-resolution surface SM estimates using sentinel-2 imagery over a Mediterranean agro-ecosystem. Furthermore, the results were compared with those of PSO-based optimized group method of data handling (GMDH) as a more common data-driven method. Two different modeling approaches i.e. modeling in homogenous clusters (local approach) and modeling in entire area as an entity (global approach) were examined. Candidate predictors namely sentinel-2 spectral bands, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), digital elevation model (DEM), slope and aspect were used as the input variables to estimate SM. An intensive field survey had been done to gather in-situ SM data using a time-domain reflectometer (TDR). K-fold validation based on in-situ SM measurements demonstrated the reasonability of the SM estimation of the proposed methodology. Detecting homogeneous areas was done using genetic and particle swarm optimization algorithms. Synthesized SM product of PSO-GMDH showed a mean Normalized Root-Mean-Square Error (NRMSE) of 13.6 to 8.91 for global and local approaches in the test phase. PSO-CMAC method with an average NRMSE of 12.47 to 8.72 for global and local approaches shows the highest accuracy and outperforms the PSO-GMDH method at both local and global approaches. Overall, results revealed that clustering study area prior to running machine learning (ML) models coupled with optical satellite imagery and geophysical properties, boosts their predictive performance and can lead to more accurate mapping of SM with more heterogeneity. The results also showed that the global approach had a moderate performance in capturing the SM heterogeneity.

StationRank: Aggregate dynamics of the Swiss railway.

Increasing availability and quality of actual, as opposed to scheduled, open transport data offers new possibilities for capturing the spatiotemporal dynamics of railway and other networks of social infrastructure. One way to describe such complex phenomena is in terms of stochastic processes. At its core, a stochastic model is domain-agnostic and algorithms discussed here have been successfully used in other applications, including Google's PageRank citation ranking. Our key assumption is that train routes constitute meaningful sequences analogous to sentences of literary text. A corpus of routes is thus susceptible to the same analytic tool-set as a corpus of sentences. With our experiment in Switzerland, we introduce a method for building Markov Chains from aggregated daily streams of railway traffic data. The stationary distributions under normal and perturbed conditions are used to define systemic risk measures with non-evident, valuable information about railway infrastructure.

A Markovian model of evolving world input-output network.

The initial theoretical connections between Leontief input-output models and Markov chains were established back in 1950s. However, considering the wide variety of mathematical properties of Markov chains, so far there has not been a full investigation of evolving world economic networks with Markov chain formalism. In this work, using the recently available world input-output database, we investigated the evolution of the world economic network from 1995 to 2011 through analysis of a time series of finite Markov chains. We assessed different aspects of this evolving system via different known properties of the Markov chains such as mixing time, Kemeny constant, steady state probabilities and perturbation analysis of the transition matrices. First, we showed how the time series of mixing times and Kemeny constants could be used as an aggregate index of globalization. Next, we focused on the steady state probabilities as a measure of structural power of the economies that are comparable to GDP shares of economies as the traditional index of economies welfare. Further, we introduced two measures of systemic risk, called systemic influence and systemic fragility, where the former is the ratio of number of influenced nodes to the total number of nodes, caused by a shock in the activity of a node, and the latter is based on the number of times a specific economic node is affected by a shock in the activity of any of the other nodes. Finally, focusing on Kemeny constant as a global indicator of monetary flow across the network, we showed that there is a paradoxical effect of a change in activity levels of economic nodes on the overall flow of the world economic network. While the economic slowdown of the majority of nodes with high structural power results to a slower average monetary flow over the network, there are some nodes, where their slowdowns improve the overall quality of the network in terms of connectivity and the average flow of the money.