Evaluation of public participation in reconstruction of Bam,Iran, after the 2003 earthquake

In the midst of the ever-increasing natural and human-induced disasters, where many of the preparedness and mitigation measures show inefficiencies, there is narrow margin for decision-makers to make mistakes by misallocating budgets, designing infeasible reconstruction plans, and in other terms, making decisions not in line with the public preferences. In particular, public participation in post-disaster measures seems undoubtedly necessary to reduce the possible economic, social, political, and cultural conflicts around the stressful community after a major disaster. This paper aims at evaluating the role of public participation in increasing the reconstruction phase efficiency through a case study of the reconstruction process in Bam, a southeastern Iranian city, after the 2003 earthquake. It is attempted to identify the major motivators of the public participation through a combination of quantitative and qualitative studies. Statistical data are generated through a set of questionnaires being filled by a number of 200 randomly selected survivors. The numerical results were then discussed through the Focus Group technique sessions to determine the main contributors to the public participation. It is later found that the answers are found among the performance of the reconstruction authorities, financial policies, emotional resiliency of the survivors, public information mechanisms, public satisfaction, the pace of reconstruction, and temporary housing policies.     

A hybrid computational approach to formulate soil deformation moduli obtained from PLT

In this study, new empirical equations were developed to predict the soil deformation moduli utilizing a hybrid method coupling genetic programming and simulated annealing, called GP/SA. The proposed models relate secant (E_s), unloading (E_u) and reloading (E_r) moduli obtained from plate load–settlement curves to the basic soil physical properties. Several models with different combinations of the influencing parameters were developed and checked to select the best GP/SA models. The database used for developing the models was established upon a series of plate load tests (PLT) conducted on different soil types at various depths. The validity of the models was tested using parts of the test results that were not included in the analysis. The validation of the models was further verified using several statistical criteria. A traditional GP analysis was performed to benchmark the GP/SA models. The contributions of the parameters affecting E_s, E_u and E_r were analyzed through a sensitivity analysis. The proposed models are able to estimate the soil deformation moduli with an acceptable degree of accuracy. The E_s prediction model has a remarkably better performance than the models developed for predicting E_u and E_r. The simplified formulations for E_s, E_u and E_r provide significantly better results than the GP-based models and empirical models found in the literature.     

A new approach for developing comprehensive agricultural drought index using satellite-derived biophysical parameters and factor analysis method

The accurate assessment of drought and its monitoring is highly depending on the selection of appropriate indices. Despite the availability of countless drought indices, due to variability in environmental properties, a single universally drought index has not been presented yet. In this study, a new approach for developing comprehensive agricultural drought index from satellite-derived biophysical parameters is presented. Therefore, the potential of satellite-derived biophysical parameters for improved understanding of the water status of pistachio (Pistachio vera L.) crop grown in a semiarid area is evaluated. Exploratory factor analysis with principal component extraction method is performed to select the most influential parameters from seven biophysical parameters including surface temperature (T _s), surface albedo (α), leaf area index (LAI), soil heat flux (G _o), soil-adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), and net radiation (R _n). T _s and G _o were found as the most effective parameters by this method. However, T _s, LAI, α, and SAVI that accounts for 99.6 % of the total variance of seven inputs were selected to model a new biophysical water stress index (BPWSI). The values of BPWSI were stretched independently and compared with the range of actual evapotranspiration estimated through well-known METRIC (mapping evapotranspiration at high resolution with internal calibration) energy balance model. The results showed that BPWSI can be efficiently used for the prediction of the pistachio water status (RMSE of 0.52, 0.31, and 0.48 mm/day on three image dates of April 28, July 17, and August 2, 2010). The study confirmed that crop water status is accounted by several satellite-based biophysical parameters rather than single parameter.     

Computing eddy-current response and current-channeling response of the spheroidal conductor for the separated receiver and transmitter systems

In this article the response of the induced eddy-current in an unlimited conduction spheroidal as well as the response of the current-channeling resulted from the presence of the spheroidal in a weakly conducted medium are derived. The common methods are based on receiving electromagnetic induction response from the homocentric and coaxial receiver and transmitter coils which are exactly over the anomaly. As the spheroidal location is unknown in the practice, it is suggested to measure the electromagnetic induction response in a case that the receiver and the transmitter have an arbitrary situation regarding to the anomaly. In this article, eddy-current response and current-channeling response of the spheroid have been computed in a more general case in which at least one axis of spheroid and the dipole axes of the receiver and transmitter coils are on the same plate; however, none of the coils are laid over the anomaly. Using this method, we can determine the location and the depth of the spheroid through fixing the transmitter’s position and of the receivers’ coil movements.     

PM10 Source Apportionment in Ahvaz,Iran, Using Positive Matrix Factorization

Source apportionment of particulate matter <10 µm in diameter (PM₁₀), having considerable impacts on human health and the environment, is of high priority in air quality management. The present study, therefore, aimed at identifying the potential sources of PM₁₀ in an arid area of Ahvaz located in southwest of Iran. For this purpose, we collected 24‐h PM₁₀ samples by a high volume air sampler. The samples were then analyzed for their elemental (Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Mg, Mn, Na, Ni, Pb, Se, Si, Sn, Sr, Li, Ti, V, Zn, Mo, and Sb) and ionic (NH, Cl^?, NO, and SO) components using inductively coupled plasma optical emission spectrometry and ion chromatography instruments, respectively. Eight factors were identified by positive matrix factorization: crustal dust (41.5%), road dust (5.5%), motor vehicles (11.5%), marine aerosol (8.0%), secondary aerosol (9.5%), metallurgical plants (6.0%), petrochemical industries and fossil fuel combustion (13.0%), and vegetative burning (5.0%). Result of this study suggested that the natural sources contribute most to PM₁₀ particles in the area, followed closely by the anthropogenic sources.     

Hydro-mechanical evaluation of stabilized mine tailings

. In this study, mine tailings waste was stabilized using a combination of lime, fly ash type "C", and aluminum. Treated samples were subjected to mineral identification for evaluating the formation of ettringite and gypsum. Also, unconfined compression, hydraulic conductivity, and cyclic freeze and thaw tests were performed to evaluate the hydro-mechanical properties of the stabilized samples. Experimental results have shown that the application of lime and fly ash type "C" to high sulfate content tailings has improved its plasticity, workability, and volume stability. Moreover, upon addition of aluminum to lime and fly ash in a sulfate-rich environment, ettringite and calcium sulfo-aluminate hydrate are formed in these samples. Application of 5% lime, 10% fly ash type "C", in combination with 110 ppm aluminum, resulted in the formation of a solid monolith capable of producing more than 1,000 kPa of unconfined compressive strength, and reduced tailings permeability to 1.96᎒^–6 cm s^–1, which is less than the recommended permeability of 10^–5 cm s^–1 by most environmental protection agencies for reusability of solidified/stabilized samples. The permeability of the treated tailings samples remained below the recommended permeability, even after exposing the treated samples to 12 freeze and thaw cycles. Therefore, based on the experimental results, it is concluded that treatment of high sulfate-content tailings with lime and fly ash, combined with the availability of aluminum for reactions, is a successful method of solidifying highly reactive mine tailings.     

Predictive value of Keetch-Byram Drought Index for cereal yields in a semi-arid environment

The study examines the potential of urban roofs to reduce the urban heat island (UHI) effect by changing their reflectivity and implementing vegetation (green roofs) using the example of the City of Vienna. The urban modelling simulations are performed based on high-resolution orography and land use data, climatological observations, surface albedo values from satellite imagery and registry of the green roof potential in Vienna. The modelling results show that a moderate increase in reflectivity of roofs (up to 0.45) reduces the mean summer temperatures in the densely built-up environment by approximately 0.25 °C. Applying high reflectivity materials (roof albedo up to 0.7) leads to average cooling in densely built-up area of approximately 0.5 °C. The green roofs yield a heat load reduction in similar order of magnitude as the high reflectivity materials. However, only 45 % of roof area in Vienna is suitable for greening and the green roof potential mostly applies to industrial areas in city outskirts and is therefore not sufficient for substantial reduction of the UHI effect, particularly in the city centre which has the highest heat load. The strongest cooling effect can be achieved by combining the green roofs with high reflectivity materials. In this case, using 50 or 100 % of the green roof potential and applying high reflectivity materials on the remaining surfaces have a similar cooling effect.     

A GIS‐based multi‐criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks

This study proposes multi‐criteria group decision‐making to address seismic physical vulnerability assessment. Granular computing rule extraction is combined with a feed forward artificial neural network to form a classifier capable of training a neural network on the basis of the rules provided by granular computing. It provides a transparent structure despite the traditional multi‐layer neural networks. It also allows the classifier to be applied on a set of rules for each incoming pattern. Drawbacks of original granular computing (GrC) are covered, where some input patterns remained unclassified. The study was applied to classify seismic vulnerability of the statistical units of the city of Tehran, Iran. Slope, seismic intensity, height and age of the buildings were effective parameters. Experts ranked 150 randomly selected sample statistical units with respect to their degree of seismic physical vulnerability. Inconsistency of the experts' judgments was investigated using the induced ordered weighted averaging (IOWA) operator. Fifty‐five classification rules were extracted on which a neural network was based. An overall accuracy of 88%, κ = 0.85 and R² = 0.89 was achieved. A comparison with previously implemented methodologies proved the proposed method to be the most accurate solution to the seismic physical vulnerability of Tehran.     

Trends in monthly precipitation over the northwest of Iran (NWI)

Increasing global temperatures during the last century have had their own effects on other climatic conditions, particularly on precipitation characteristics. This study was meant to investigate the spatial and temporal monthly trends of precipitation using the least square error (LSE) approach for the northwest of Iran (NWI). To this end, a database was obtained from 250 measuring stations uniformly scattered all over NWI from 1961 to 2010. The spatial average of annual precipitation in NWI during the period of study was approximately 220.9–726.7 mm. The annual precipitation decreased from southwest to northeast, while the large amount of precipitation was concentrated in the south-west and in the mountainous areas. All over NWI, the maximum and minimum precipitation records occurred from March to May and July to September, respectively. The coefficient of variation (CV) is greater than 44 % in all of NWI and may reach over 76 % in many places. The greatest range of CV, for instance, occurred during July. The spatial variability of precipitation was consistent with a tempo-spatial pattern of precipitation trends. There was a considerable difference between the amounts of change during the months, and the negative trends were mainly attributed to areas concentrated in eastern and southern parts of NWI far from the western mountain ranges. Moreover, limited areas with positive precipitation trends can be found in very small and isolated regions. This is observable particularly in the eastern half of NWI, which is mostly located far from Westerlies. On the other hand, seasonal precipitation trends indicated a slight decrease during winter and spring and a slight increase during summer and autumn. Consequently, there were major changes in average precipitation that occurred negatively in the area under study during the observation period. This finding is in agreement with those findings by recent studies which revealed a decreasing trend of around 2 mm/year over NWI during 1966–2005.