Comprehensive modeling of monthly mean soil temperature using multivariate adaptive regression splines and support vector machine

Soil temperature (T _s) and its thermal regime are the most important factors in plant growth, biological activities, and water movement in soil. Due to scarcity of the T _s data, estimation of soil temperature is an important issue in different fields of sciences. The main objective of the present study is to investigate the accuracy of multivariate adaptive regression splines (MARS) and support vector machine (SVM) methods for estimating the T _s. For this aim, the monthly mean data of the T _s (at depths of 5, 10, 50, and 100 cm) and meteorological parameters of 30 synoptic stations in Iran were utilized. To develop the MARS and SVM models, various combinations of minimum, maximum, and mean air temperatures (T _min, T _max, T); actual and maximum possible sunshine duration; sunshine duration ratio (n, N, n/N); actual, net, and extraterrestrial solar radiation data (R _s, R _n, R _a); precipitation (P); relative humidity (RH); wind speed at 2 m height (u ₂); and water vapor pressure (V_p) were used as input variables. Three error statistics including root-mean-square-error (RMSE), mean absolute error (MAE), and determination coefficient (R ²) were used to check the performance of MARS and SVM models. The results indicated that the MARS was superior to the SVM at different depths. In the test and validation phases, the most accurate estimations for the MARS were obtained at the depth of 10 cm for T _max, T _min, T inputs (RMSE = 0.71 °C, MAE = 0.54 °C, and R ² = 0.995) and for RH, V _p, P, and u ₂ inputs (RMSE = 0.80 °C, MAE = 0.61 °C, and R ² = 0.996), respectively.     

Assessment of a spatial multi-criteria evaluation to site selection underground dams in the Alborz Province,Iran

Most part of Iran is arid and semi-arid; thus in most parts of the region, groundwater is the only source of water. This research presents a method based on a spatial multi-criterion evaluation (SMCE) for designing possible sites of underground dams and ranks them according to their suitability. The method was tested for siting underground dams in the Alborz Province, Iran. At first, screening algorithm was applied using exclusionary criteria, and thirty-one potential areas were recognized in the study area. In the next step, a suitable gorge or valley was recognized using the combination of basic maps and extensive field surveys (long axis of tank level) in each potential area. Subsequently, the analytical hierarchy process was used as a powerful tool for decision-making in the SMCE in order to evaluate different criteria for underground dam sites. SMCE techniques were then applied to combine the criteria, and obtain a suitability map in the study area. These sites were then compared and ranked according to their main criteria such as water, storage, axis and socio-economics. All these criteria were assessed through geographical information system modelling. This method shows passable results and could be used for site selection of underground dams in other regions of Iran.     

Semi Automatic Road Extraction by Fusion of High Resolution Optical and Radar Images

Two new methods for fusion of high-resolution optical and radar satellite images have been proposed to extract roads in high quality in this paper. Two fusion methods, including neural network and knowledge-based fusion are introduced. The first proposed method consists of two stages: (i) separate road detection using each dataset and (ii) fusion of the results obtained using a neural network. In this method, the neural networks are separately applied on high-resolution IKONOS and TerraSAR-X images for road detection, using a variety of texture parameters. The outputs of two neural networks, as well as the spectral features of optical image, are used in a third neural network as inputs. The second method is a knowledge-based fusion using thresholds of narrow roads and vegetation gray levels. First roads are extracted from each source separately. The outputs are then compared and advantages and disadvantages of each data source are investigated . The results obtained from accuracy assessment show the efficiency of the proposed methods. Furthermore, the comparison of the results showed the superiority of the first algorithm.     

Comparison of Bayesian estimates of peak ground acceleration (<Emphasis Type="Italic">A</Emphasis>max) with PSHA in Iran

Bayesian probability theory is an appropriate and useful method for estimating parameters in seismic hazard analysis. The analysis in Bayesian approaches is based on a posterior belief, also their special ability is to take into account the uncertainty of parameters in probabilistic relations and a priori knowledge. In this study, we benefited the Bayesian approach in order to estimate maximum values of peak ground acceleration (A_max) also quantiles of the relevant probabilistic distributions are figured out in a desired future interval time in Iran. The main assumptions are Poissonian character of the seismic events flow and properties of the Gutenberg-Richter distribution law. The map of maximum possible values of A_max and also map of 90% quantile of distribution of maximum values of A_max on a future interval time 100 years is presented. According to the results, the maximum value of the A_max is estimated for Bandar Abbas as 0.3g and the minimum one is attributed to Esfahan as 0.03g. Finally, the estimated values in Bayesian approach are compared with what was presented applying probabilistic seismic hazard (PSH) methods based on the conventional Cornel (1968) method. The distribution function of A_max for future time intervals of 100 and 475 years are calculated for confidence limit of probability level of 90%.     

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.     

Post‐stack diffraction imaging in vertical transverse isotropy media using non‐hyperbolic moveout approximations

Diffractions carry valuable information about local discontinuities and small‐scale objects in the subsurface. They are still not commonly used in the process of geological interpretation. Many diffraction imaging techniques have been developed and applied for isotropic media, whereas relatively few techniques have been developed for anisotropic media. Ignoring anisotropy can result in low‐resolution images with wrongly positioned or spurious diffractors. In this article, we suggest taking anisotropy into account in two‐dimensional post‐stack domain by considering P‐wave non‐hyperbolic diffraction traveltime approximations for vertical transverse isotropy media, previously developed for reflection seismology. The accuracy of the final images is directly connected to the accuracy of the diffraction traveltime approximations. We quantified the accuracy of six different approximations, including hyperbolic moveout approximation, by the application of a post‐stack diffraction imaging technique on two‐dimensional synthetic data examples.     

Investigating extreme scenarios with multiple-point geostatistics and variance maximization

Stochastic Environmental Research and Risk Assessment - In many geoscience applications, the data extracted from environmental variables are very limited. Multiple-point geostatistical (MPS)...     

Effects of rotational components of earthquake on seismic response of arch concrete dams

The present study deals with dynamic analysis of arch concrete dams, taking rotational components of earthquakes into account. A modified methodology was used to evaluate the rotational components of the earthquake. The translational components of the earthquake have been used in to obtain the rotational components of the earthquake, based on the intersecting isotropic elastic wave propagation. Two rotational components of Taft, Tabas and San-Fernando earthquakes are evaluated based on the translational components of the earthquakes and considering frequency dependencies of incident angle and wave velocity. Finally, dynamic analyses of Morrow Point Dam are presented to evaluate the effects of combined translational and rotational components on the seismic response of the dam. Various conditions of reservoirs, including full and empty state, are considered in the analyses. Fluid–structure interaction was completely taken into account. It was realized that incorporating rotational components increased the maximum compressive and tensile stresses in both empty and full reservoir analyses. Distribution of maximum tensile stresses is very sensitive to the rotational components of the earthquake. Also, it can be concluded that the segregated effect of the rocking component on the response of concrete dams is more effective than the sole effect of the torsional component.