Geodetic deformation vs. seismic strain deduced by historical earthquakes across the Alborz Mountains

Historical seismicity is used in order to map spatial distribution of seismic moment released by past earthquakes and to compare strain rate deduced from seismicity to those measured by geodetic GPS survey. Spatial analyses are performed on the seismicity of northern boundary of Central Iranian Block which coincides with the Alborz Mountains. This belt has been responsible for several catastrophic earthquakes in the past. In this study, the records of historical and instrumental earthquakes in the Alborz Mountains are used to calculate and plot geographical distribution of seismic moment released in time. A two-dimensional distribution function is proposed and used here to spread seismic moment along causative tectonic features. Using accumulated seismic moment, average slip rates across active faults are estimated for 32 sub-zones along the Alborz Mountains and western Kopet Dag. Seismic moment released by historical and recent earthquakes on this belt accounts for slip rate of 3–5 mm/year which is in good agreement with the geodetic vectors recently deduced from GPS survey in this region. The study also reveals geographical variations of slip rates along some 900 km length of this zone based on seismic history. The results are compared against finding from similar study in this region. Portions of Central and Eastern Alborz show lower seismic strain rate which could imply aseismic motion or overdue earthquakes. Completeness of historical earthquake catalogue and its reliability with regard to earthquake magnitudes, locations, and rupturing systems are among many plausible factors controlling the credibility of such results. Therefore, any conclusions derived from these results remain as reliable as the data and assumptions used for the analyses.     

An “Olympic” framework for a green decommissioning of an offshore oil platform

Oil and gas offshore platform and installations have a limited life of operations. When oil runs out, many terms are used to describe the situation: abandonment, removal, disposal, decommissioning, etc. Even the issue of decommissioning is now at the forefront of deep water oil drilling for many reason (the enormous costs required for disposal, the increasing number of rigs which required removal, the need to protect the marine environment, legal frameworks), there are very few published researchers studying the problem according to its different facets (legal, environmental, economical etc.). In this paper, we apply the concept of “Olympic” supply chain developed by Lakhal et al. Lakhal, SY, H'Mida S, Islam R. Green Supply Chain parameters for a Canadian Petroleum Refinery Company. International Journal of Environmental Technology and Management, IJETM 2007:7;56–67. to provide feedback for life-cycle offshore platforms. Our approach starts from platform development projects environmentally, exploit it in the respect of the environment and have an efficient decommissioning project.     

Factors affecting whether environmental migration is temporary or permanent: Evidence from Bangladesh

This study examines whether environmental migrants in Bangladesh move permanently or temporarily. The analyses are based on data collected in 2010 and 2011, and cover four themes, namely migrant characteristics, environmental change related factors, conflict and adaptation strategies, and social networks. The estimates obtained from binary logit models show that most sets of variables have statistically significant impacts on the temporary migration versus permanent migration decision. Females are more inclined to migrate temporarily, a finding which is consistent with prior studies that argued that female migration is one temporary household survival strategy in the face of an environmental crisis. The probability of intending to move temporarily is significantly affected by the prior occupational experience: Migrants who were previously engaged in agriculture or fishing are more inclined to migrate permanently. Those households who reported that they had lost assets due to environmental hazards are shown to have a higher probability of becoming permanent migrants. In contrast, loss of livestock and crop failure are associated with a greater likelihood of temporary migration. The empirical results reveal the groups that can be targeted in destination regions in settlement policy, and equally the groups whose return home can be facilitated once any immediate danger has passed.     

A fusion-based neural network methodology for monthly reservoir inflow prediction using MODIS products

ABSTRACT

A model fusion approach was developed based on five artificial neural networks (ANNs) and MODIS products. Static and dynamic ANNs – the multi-layer perceptron (MLP) with one and two hidden layers, general regression neural network (GRNN), radial basis function (RBF) and nonlinear autoregressive network with exogenous inputs (NARX) – were used to predict the monthly reservoir inflow in Mollasadra Dam, Fars Province, Iran. Leaf area index and snow cover from MODIS, and rainfall and runoff data were used to identify eight different combinations to train the models. Statistical error indices and the Borda count method were used to verify and rank the identified combinations. The best results for individual ANNs were combined with MODIS products in a fusion model. The results show that using MODIS products increased the accuracy of predictions, with the MLP with two hidden layers giving the best performance. Also, the fusion model was found to be superior to the best individual ANNs.     

Unsupported advance length in tunnels constructed using New Austrian Tunnelling Method and ground surface settlement

Tunnels constructed using New Austrian Tunnelling Method (NATM) are always based on certain round (unsupported) advance lengths, after which, the temporary lining is placed. The settlement of the ground surface resulting from such construction is of high significance in design and practice. The existing data in this respect, however, is scarce. It is the aim of this paper to propose a semi‐analytical procedure based on three‐dimensional finite element analyses to predict the maximum surface settlement of the ground in NATM tunnels under different combinations of tunnel diameter, overburden depth, round length and soil and lining properties. The comparison of the results with three case histories of real tunnels reveals reasonable accuracy of the present solution. Copyright © 2012 John Wiley & Sons, Ltd.     

Characteristics of meteorological drought in Bangladesh

Meteorological drought events occur in Bangladesh are diagnosed using monthly rainfall and mean air temperature from the surface observations and Regional Climate Model (RegCM) by calculating Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI) for the period 1961?C1990. The historical records of drought event obtained from the Bangladesh Bureau of Statistics and International Disaster Database are used to verify the SPI and PDSI detected events. The SPI and monthly PDSI are obtained for 27 station data across Bangladesh as well as for two subregions over the country. Result based on the observed data shows that regional information is better in drought diagnosis compared to the point information. The regional analysis is able to detect about 80?% of the drought events occurred during the study period. Frequency of moderate drought is higher for all over the country. The SPI calculated from RegCM rainfall shows that the detection of moderate drought events is 10, 7, and 21?% overestimated for 1-, 3-, and 6-month length, respectively, compared to using of observed data. For extreme drought cases, detection is overestimated (underestimated) by 25?% (79?%) for 1-month (6-month) length. The PDSI results for model and observed data are nearly same to SPI calculations. Model monthly PDSI result is overestimated (underestimated) by 29?% (50?%) for moderate (severe) drought events with reference to the observed PDSI. Hence, RegCM output may be useful to detect 3?C6-month (monthly to seasonal) length moderate drought events over a heavy rainfall region likely Bangladesh.     

A survey of challenges in reducing the impact of geological hazards associated with earthquakes in Iran

Most of the recent earthquakes in Iran were associated with some types of geological hazards that were the cause of additional damage and casualties in the earthquake-affected areas. In order to reduce the impacts of geo-hazards, several policies and plans were prepared, approved, and implemented by the Iranian government during the last two decades. However, such activities have not yet resulted in risk reduction to an acceptable level; since they are not formulated based on local conditions and are not linked to location-specific comprehensive plans. In this paper, after introducing some of the impacts of geo-hazards associated with recent earthquakes in Iran, the country trends in development of relevant risk reduction plans and policies are introduced, evaluated, and compared with some other countries having similar challenges. Strategies toward risk mitigation in the country are addressed, and a number of indices for the assessment of the geo-hazard risk reduction plans and activities are introduced. Finally, a conceptual model for the evaluation of the preparedness level against geo-hazards is proposed and discussed.     

Propagation of solitary waves in non uniform dusty plasmas

Interaction of dust acoustic solitary waves in plasmas consisting of medium disorders is investigated. Disorders and inhomogeneities of the medium are added to the equation of motion as perturbative terms through the medium parameters. The effects of these perturbations on the behaviour of solitary waves are studied with numerical simulations and the results are compared with theoretical predictions in a uniform media.     

A hybrid method for grade estimation using genetic algorithm and neural networks

In the present paper, a new hybrid method is proposed for grade estimation. In this method, the multilayer perceptron (MLP) network is trained using the combination of the Levenberg–Marquardt (LM) method and genetic algorithm (GA). Having a few samples for grade estimation, it is difficult to get a proper result using some function approximation methods like neural networks or geostatistical methods. The neural network training methods are very sensitive to initial weight values when there are a few samples as a training dataset. The main objective of the proposed method is to resolve this problem. Here, our method finds the optimal initial weights by combining GA and LM method. Having the optimal initial values for weights, the local minima are avoided in the training phase and subsequently the neural network sustainability is trained optimally. Furthermore, the hybrid method is applied for grade estimation of Gol-e-Gohar iron ore in south Iran. The proposed method shows significant improvements compared to both conventional MLP and Kriging method. The efficiency of the proposed method gets more highlighted when the training data set is small.