Statistical prediction of the next great earthquake around Tehran,Iran

The analysis of seismic activity variations with space and time is a complex problem. Several statistical methods have been adopted to study these variations. One of the tasks that has attracted the attention of the seismological and statistical community is to explain seismicity patterns by statistical models and apply the results for earthquake prediction. Here the probability distribution of recurrence times as described by Exponential, Gamma, Lognormal, Pareto, Rayleigh and Weibull probability distributions and the idea of conditional probability has been applied to predict the next great (Ms ≥ 6.0 and Ms ≥ 6.5) earthquake around Tehran (r ≤ 200 km). Conditional probability specifies the likelihood that a given earthquake will happen within a specified time. This likelihood is based on the information about past earthquake occurrences in the given region and the basic assumption that future seismic activity will follow the pattern of past activity. The rapid growth of Tehran to approximately 12 million inhabitants has resulted in a much more rapid increase in its vulnerability to natural disasters, especially earthquakes. Several earthquakes affected this region in the past, mostly on the Mosha, Taleqan, Eyvankey and Garmsar faults. The estimated recurrence times for Exponential, Gamma, Lognormal, Pareto, Rayleigh and Weibull distributions has been computed to be 66.64, 14.79, 26.88, 2.37, 67.58 and 80.47, respectively. Accordingly, one may expect that a large damaging earthquake may occur around Tehran approximately every 10 years.     

Evaluation of total load sediment transport formulas using ANN

The calculated results from various sediment transport formulas often differ from each other and from measured data. Some parameters in the sediment transport formulas are more effective than others to estimate total sediment load. In this study, an Artificial Neural Network （ANN） model is trained using four dominant parameters of sediment transport formulas. ANN models are able to reveal hidden laws of natural phenomena such as sediment transport process. The results of ANN and some total bed material load sediment transport formulas have been compared to indicate the importance of variables which can be used in developing sediment transport formulas. To train ANN, average flow velocity, water surface slopes, average flow depth, and median particle diameter are used as dominant parameters to estimate total bed material load. Two hundreds and fifty samples are used to train the ANN model. Twenty-four sets of field data not used in the training nor calibration of ANN are used to compare or verify the accuracy of ANN and some well-known total bed material load formulas. The test results show that the ANN model developed in this study using minimum number of dominant factors is a reliable and uncomplicated method to predict total sediment transport rate or total bed material load transport rate. Results show that the accuracy of formulas in descending order are those by Yang （1973）, Laursen （1958）, Engelund and Hansen （1972）, Ackers and White （1973）, and Toffaleti （1969）. These results are similar to those made by ASCE （1982） based on laboratory and field data not used in this paper. Study results also show that the formulas based on physical laws of sediment transport, like those formulas that were developed based on power concept, are more accurate than other formulas for estimating total bed material sediment load in rivers.     

Probabilistic reservoir operation using Bayesian stochastic model and support vector machine

Available water resources are often not sufficient or too polluted to satisfy the needs of all water users. Therefore, allocating water to meet water demands with better quality is a major challenge in reservoir operation. In this paper, a methodology to develop operating strategies for water release from a reservoir with acceptable quality and quantity is presented. The proposed model includes a genetic algorithm (GA)-based optimization model linked with a reservoir water quality simulation model. The objective function of the optimization model is based on the Nash bargaining theory to maximize the reliability of supplying the downstream demands with acceptable quality, maintaining a high reservoir storage level, and preventing quality degradation of the reservoir. In order to reduce the run time of the GA-based optimization model, the main optimization model is divided into a stochastic and a deterministic optimization model for reservoir operation considering water quality issues.The operating policies resulted from the reservoir operation model with the water quantity objective are used to determine the released water ranges (permissible lower and upper bounds of release policies) during the planning horizon. Then, certain values of release and the optimal releases from each reservoir outlet are determined utilizing the optimization model with water quality objectives. The support vector machine (SVM) model is used to generate the operating rules for the selective withdrawal from the reservoir for real-time operation. The results show that the SVM model can be effectively used in determining water release from the reservoir. Finally, the copula function was used to estimate the joint probability of supplying the water demand with desirable quality as an evaluation index of the system reliability. The proposed method was applied to the Satarkhan reservoir in the north-western part of Iran. The results of the proposed models are compared with the alternative models. The results show that the proposed models could be used as effective tools in reservoir operation.     

Biomonitoring of Heavy Metals Using the Bivalve Molluscs in Sunderban Mangrove Wetland,Northeast Coast of Bay of Bengal (India): Possible Risks to Human Health

The suitability of using four bivalve molluscs (Sanguinolaria acuminata, Anadara granosa, Meretrix meretrix, and Pelecyora trigona) in biomonitoring of heavy metals (Cu, Pb, Cd, Zn, and Hg) collected from intertidal regions of the Sunderban mangrove wetland, northeastern part of the Bay of Bengal, were evaluated. Both speciesdependent variability and temporal variations were pronounced. A high degree of organ specificity was evident in the bivalves where gill and mantle exhibited higher metal accumulation due to ion exchange property of the mucous layer covering these organs while shells represent very poor accumulation. Elevated values of Zn and Cu reflect high potential for biomagnification through marine food chain. Metal concentrations in different body size groups of the bivalves do not follow uniform trend. Correlation coefficient between different metal couplings as tested statistically revealed significant coupling for Pb–Zn, Pb–Cu, Zn–Cu, and Hg–Cu. Concentrations of all the metals in specific organs (visceral mass, mantle and gill) of the bivalves exceeded the safe levels according to the international standards for metals compiled by Food and Agricultural Organization of the United Nations and would be of great risk for human consumption. It is concluded that the mussel and clam are suitable biomonitors to employ in programs designed to assess changes in metal pollution in the Sunderban mangrove wetland.     

Simulation of relationship between river discharge and sediment yield in the semi-arid river watersheds

Complex and variable nature of the river sediment yield caused many problems in estimating the long-term sediment yield and problems input into the reservoirs. Sediment Rating Curves (SRCs) are generally used to estimate the suspended sediment load of the rivers and drainage watersheds. Since the regression equations of the SRCs are obtained by logarithmic retransformation and have a little independent variable in this equation, they also overestimate or underestimate the true sediment load of the rivers. To evaluate the bias correction factors in Kalshor and Kashafroud watersheds, seven hydrometric stations of this region with suitable upstream watershed and spatial distribution were selected. Investigation of the accuracy index (ratio of estimated sediment yield to observed sediment yield) and the precision index of different bias correction factors of FAO, Quasi-Maximum Likelihood Estimator (QMLE), Smearing, and Minimum-Variance Unbiased Estimator (MVUE) with LSD test showed that FAO coefficient increases the estimated error in all of the stations. Application of MVUE in linear and mean load rating curves has not statistically meaningful effects. QMLE and smearing factors increased the estimated error in mean load rating curve, but that does not have any effect on linear rating curve estimation.     

Flow and erosion at a bend in the braided Jamuna River

The braided Jamuna River frequently changes its courses.Sometimes the secondary channel in a braided river acts as a single thread meandering channel.In the present study an attempt has been made to investigate the flow patterns and to estimate the rate of bank erosion in a bend along the Jamuna River.The three dimensional(3D) flow velocities were measured using Acoustic Doppler Current Profiler(ADCP).It is found that the near bank velocity is amplified by 1.1 to 1.3 times as compared with the section averaged velocity.A dominant secondary current is found in the upstream bend.The evolution as well as decay of the secondary current is not as clear as it is found in the laboratory experiments.It is revealed from the analysis of the flow process that the causes of higher rate of erosion at the study bend are the oblique flow near bankline,six times amplified shear velocity than critical shear velocity near bankline and the secondary current which acts as a sediment transporting agent from the outer bank towards the inner bank or the sand bar.Based on the flow processes,a simplified erosion prediction model is developed and applied to estimate the rate of erosion at a selected bend.Finally the predicted results have been compared with the observed data at the bend and all the available data at other bends along the Jamuna River.     

Agricultural land partitioning model based on irrigation efficiency using a multi‐objective artificial bee colony algorithm

In the process of agricultural land consolidation, the land parcels are optimally redesigned and rearranged in such a way that the dimensions of the resulting parcels are proportional to agricultural criteria such as irrigation discharge, soil texture, and cropping pattern. Besides these criteria, spatial factors like slope, road accessibility, volume of earthwork, and geometrical factors such as size and shape of parcels are also included in the design process of agricultural land partitioning. In this study, a land partitioning model was proposed using a multi‐objective artificial bee colony algorithm (MOABC‐LP) taking into consideration the mentioned factors. Initially, a feasible dimension range of parcels in a block was calculated based on irrigation efficiency. Two partitioning layouts were defined according to the topography and geometry of blocks. The proposed method was applied to a real study area and the results suggest that the land partitioning plan obtained by the MOABC‐LP model, in comparison with a designer's plan, not only makes the shape and size of parcels more compatible with the topographical and agricultural conditions of each block, but also reduces their cut‐and‐fill ratio.     

Environmental public health applications using remotely sensed data

We describe a remote sensing and geographic information system (GIS)-based study that has three objectives: (1) characterize fine particulate matter (PM_2.5), insolation and land surface temperature (LST) using NASA satellite observations, Environmental Protection Agency (EPA) ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making.