Forecasting hurricane-induced power outage durations

Accurate estimates of the duration of power outages caused by hurricanes prior to landfall are valuable for utility companies and government agencies that wish to plan and optimize their restoration efforts. Accurate pre-storm estimates are also important information for customers and operators of other infrastructures systems, who rely heavily on electricity. Traditionally, utilities make restoration plans based on managerial judgment and experience. However, skillful outage forecast models are conducive to improved decision-making practices by utilities and can greatly enhance storm preparation and restoration management procedures of power companies and emergency managers. This paper presents a novel statistical approach for estimating power outage durations that is 87 % more accurate than existing models in the literature. The power outage duration models are developed and carefully validated for outages caused by Hurricanes Dennis, Katrina, and Ivan in a central Gulf Coast state. This paper identifies the key variables in predicting hurricane-induced outage durations and their degree of influence on predicting outage restoration for the utility company service area used as our case study.     

The Sabzevar blueschists of the North-Central Iranian micro-continent as remnants of the Neotethys-related oceanic crust subduction

The Sabzevar ophiolites mark the Neotethys suture in east-north-central Iran. The Sabzevar metamorphic rocks, as part of the Cretaceous Sabzevar ophiolitic complex, consist of blueschist, amphibolite and greenschist. The Sabzevar blueschists contain sodic amphibole, epidote, phengite, calcite ± omphacite ± quartz. The epidote amphibolite is composed of sodic-calcic amphibole, epidote, albite, phengite, quartz ± omphacite, ilmenite and titanite. The greenschist contains chlorite, plagioclase and pyrite, as main minerals. Thermobarometry of a blueschist yields a pressure of 13–15.5 kbar at temperatures of 420–500 °C. Peak metamorphic temperature/depth ratios were low (~12 °C/km), consistent with metamorphism in a subduction zone. The presence of epidote in the blueschist shows that the rocks were metamorphosed entirely within the epidote stability field. Amphibole schist samples experienced pressures of 5–7 kbar and temperatures between 450 and 550 °C. The presence of chlorite, actinolite, biotite and titanite indicate greenschist facies metamorphism. Chlorite, albite and biotite replacing garnet or glaucophane suggests temperatures of >300 °C for greenschist facies. The formation of high-pressure metamorphic rocks is related to north-east-dipping subduction of the Neotethys oceanic crust and subsequent closure during lower Eocene between the Central Iranian Micro-continent and Eurasia (North Iran).     

Geomorphologic assessment of relative tectonic activity in the Maharlou Lake Basin,Zagros Mountains of Iran

Spatial differences of Quaternary deformation and intensity of tectonic activity are assessed through a detailed quantitative geomorphic study of the fault‐generated mountain fronts and alluvial/fluvial systems around the Maharlou Lake Basin in the Zagros Fold–Thrust Belt of Iran. The Maharlou Lake Basin is defined as an approximately northwest–southeast trending, linear, topographic depression located in the central Zagros Mountains of Iran. The lake is located in a tectonically active area delineated by the Ghareh and Maharlou faults. Combined geomorphic and morphometric data reveal differences between the Ghareh and Maharlou mountain front faults indicating different levels of tectonic activity along each mountain front. Geomorphic indices show a relatively high degree of tectonic activity along the Ghareh Mountain Front in the southwest, in contrast with less tectonic activity along the Ahmadi Mountain Front northeast of the lake which is consistent with field evidence and seismotectonic data for the study area. A ramp valley tectonic setting is proposed to explain the tectonosedimentary evolution of the lake. Copyright © 2011 John Wiley & Sons, Ltd.     

Effects of the Permian–Triassic boundary on reservoir characteristics of the South Pars gas field,Persian Gulf

The Permian–Triassic boundary (PTB) is a world‐wide event characterized by the most extensive mass extinction in the history of life. In the Persian Gulf, the rock record of this time interval host one of the most important hydrocarbon reserves in the world: the South Pars Gas Field and its southern extension, the North Dome (or North Field). These carbonate and evaporite successions were sampled in eight wells for petrographic, geochemical and porosity–permeability studies. An important characteristic of the Dalan and Kangan formations is the centimetre‐scale lithological heterogeneities caused by facies changes and diagenetic imprints that led to the compartmentalization of these reservoirs. These Permian–Triassic (P‐T) sediments were deposited in a shallow marine homoclinal ramp. The PTB in this hydrocarbon field is represented by a reworked coarse‐grained intraclastic/bioclastic grainstone facies deposited during a marine transgression. Prolonged subaerial exposure in the P‐T transition caused hypersaline and meteoric diagenesis, including extensive cementation, dolomitization and some dissolution, influencing reservoir characteristics of bordering units. Both δ¹⁸O and δ¹³C values in this succession mirror worldwide excursions typical of other P‐T sections, with some variations due to diagenetic alterations. A pronounced decline in ⁸⁷Sr/⁸⁶Sr values, reflective of global seawater geochemistry for most of the Permian is evident in our data. Reservoir quality declines through the late Permian, as a result of facies change and diagenesis. The Late Permian is succeeded by a Triassic transgressive facies and decline in reservoir quality. Copyright © 2009 John Wiley & Sons, Ltd.     

Integrating the multi-label land-use concept and cellular automata with the artificial neural network-based Land Transformation Model: an integrated ML-CA-LTM modeling framework

Cellular automata (CA) and artificial neural networks (ANNs) have been used by researchers over the last three decades to simulate land-use change (LUC). While conventional CA and ANN models assign a cell to only one land-use class, in reality, a cell may belong to several land-use classes simultaneously. The recently developed multi-label (ML) concept overcomes this limitation in land change science. Although the ML concept is a new paradigm with nonexclusive classes and has shown considerable merit in several applications, few studies in land change science have applied it. In addition, determining transition rules in conventional CA is difficult when the number of drivers is large. Since CA has been shown as a potential model to consider neighborhood effects and ANN has been shown effective in determining CA transition rules, we integrated both CA with an ANN model to overcome limitations of each tool. In this study, we specifically extended the ANN-based Land Transformation Model (LTM) with both a CA-based model and the ML concept to create an integrated ML-CA-LTM modeling framework. We also compared, using standard evaluation measures, differences between the proposed integrated model with a conventional CA-based LTM model (called the ml-CA-LTM). Parameterization was made using a learning and testing procedure common in machine learning. Results showed that the modified LUC model, ML-CA-LTM, produced consistently better goodness of fit calibration values compared to the ml-CA-LTM. The outcome of this modified model can be used by managers and decision makers for improved urban planning.     

Nonlinear site response evaluation of earthquakes: A case study from Sirdjan Balvavard Bridge,Kerman province,Iran

The aim of this study is to illustrate the soil conditions encountered at the proposed bridge sites to analyze and evaluate the test conducted, submit recommendations regarding foundation design. At first by field investigation, the required data was collected and after primary processing the acceptable data was selected. For nonlinear analysis of elastic and rigid half space bed rock, standard hyperbolic model was selected and performed, and the results were compared. The study clearly showed that the effect of bed rock on soil behavior during earthquake is one of the main factors controlling prediction of ground response. A critical aspect of this work was to develop and use a computer code “Abbas Converter” developed by the authors that has several advantages, such as quick installation, acting as a connecter function between the used softwares which can generate the input data corresponding to a defined format and finally, the results of this computer code can be easily exported to the other softwares used in this study. Moreover this code can make it easy to solve the problems encountered.     

Stratosphere key for wintertime atmospheric response to warm Atlantic decadal conditions

There is evidence that the observed changes in winter North Atlantic Oscillation (NAO) drive a significant portion of Atlantic Multi Decadal Variability (AMV). However, whether the observed decadal NAO changes can be forced by the ocean is controversial. There is also evidence that artificially imposed multi-decadal stratospheric changes can impact the troposphere in winter. But the origins of such stratospheric changes are still unclear, especially in early to mid winter, where the radiative ozone-impact is negligible. Here we show, through observational analysis and atmospheric model experiments, that large-scale Atlantic warming associated with AMV drives high-latitude precursory stratospheric warming in early to mid winter that propagates downward resulting in a negative tropospheric NAO in late winter. The mechanism involves stratosphere/troposphere dynamical coupling, and can be simulated to a large extent, but only with a stratosphere resolving model (i.e., high-top). Further analysis shows that this precursory stratospheric response can be explained by the shift of the daily extremes toward more major stratospheric warming events. This shift cannot be simulated with the atmospheric (low-top) model configuration that poorly resolves the stratosphere and implements a sponge layer in upper model levels. While the potential role of the stratosphere in multi-decadal NAO and Atlantic meridional overturning circulation changes has been recognised, our results show that the stratosphere is an essential element of extra-tropical atmospheric response to ocean variability. Our findings suggest that the use of stratosphere resolving models should improve the simulation, prediction, and projection of extra-tropical climate, and lead to a better understanding of natural and anthropogenic climate change.     

Multi-label class assignment in land-use modelling

During the last two decades, a variety of models have been applied to understand and predict changes in land use. These models assign a single-attribute label to each spatial unit at any particular time of the simulation. This is not realistic because mixed use of land is quite common. A more detailed classification allowing the modelling of mixed land use would be desirable for better understanding and interpreting the evolution of the use of land. A possible solution is the multi-label (ML) concept where each spatial unit can belong to multiple classes simultaneously. For example, a cluster of summer houses at a lake in a forested area should be classified as water, forest and residential (built-up). The ML concept was introduced recently, and it belongs to the machine learning field. In this article, the ML concept is introduced and applied in land-use modelling. As a novelty, we present a land-use change model that allows ML class assignment using the k nearest neighbour (kNN) method that derives a functional relationship between land use and a set of explanatory variables. A case study with a rich data-set from Luxembourg using biophysical data from aerial photography is described. The model achieves promising results based on the well-known ML evaluation criteria. The application described in this article highlights the value of the multi-label k nearest neighbour method (MLkNN) for land-use modelling.