The Effect of Hydraulic Gradient and Pattern of Conduit Systems on Tracing Tests: Bench-Scale Modeling

Tracer breakthrough curves provide valuable information about the traced media, especially in inherently heterogeneous karst aquifers. In order to study the effect of variations in hydraulic gradient and conduit systems on breakthrough curves, a bench scale karst model was constructed. The bench scale karst model contains both matrix and a conduit. Eight tracing tests were conducted under a wide range of hydraulic gradients from 1 to greater than 5 for branchwork and network-conduit systems. Sampling points at varying distances from the injection point were utilized. Results demonstrate that mean tracer velocities, tracer mass recovery and linear rising slope of the breakthrough curves were directly controlled by hydraulic gradient. As hydraulic gradient increased, both one half the time for peak concentration and one fifth the time for peak concentration decreased. The results demonstrate the variations in one half the time for peak concentration and one fifth the time for peak concentration of the descending limb for different sampling points under differing hydraulic gradients are mainly controlled by the interactions of advection with dispersion. The results are discussed from three perspectives: different conduit systems, different hydraulic-gradient conditions, and different sampling points. The research confirmed the undeniable role of hydrogeological setting (i.e., hydraulic gradient and conduit system) on the shape of the breakthrough curve. The extracted parameters (mobile-fluid velocity, tracer-mass recovery, linear rising limb, one half the time for peak concentration, and one fifth the time for peak concentration) allow for differentiating hydrogeological settings and enhance interpretations the tracing tests in karst aquifers.     

Adaptive neuro-fuzzy inference system coupled with shuffled frog leaping algorithm for predicting river streamflow time series

ABSTRACT

Accurate runoff forecasting plays a key role in catchment water management and water resources system planning. To improve the prediction accuracy, one needs to strive to develop a reliable and accurate forecasting model for streamflow. In this study, the novel combination of the adaptive neuro-fuzzy inference system (ANFIS) model with the shuffled frog-leaping algorithm (SFLA) is proposed. Historical streamflow data of two different rivers were collected to examine the performance of the proposed model. To evaluate the performance of the proposed ANFIS-SFLA model, six different scenarios for the model input–output architecture were investigated. The results show that the proposed ANFIS-SFLA model (R² = 0.88; NS = 0.88; RMSE = 142.30 (m³/s); MAE = 88.94 (m³/s); MAPE = 35.19%) significantly improved the forecasting accuracy and outperformed the classic ANFIS model (R² = 0.83; NS = 0.83; RMSE = 167.81; MAE = 115.83 (m³/s); MAPE = 45.97%). The proposed model could be generalized and applied in different rivers worldwide.     

Spatial distribution of cadmium and lead in the sediments of the western Anzali wetlands on the coast of the Caspian Sea (Iran)

Spatial distribution patterns of total cadmium (Cd) and lead (Pb), their bioavailable fractions and total organic matter in sediment from Anzali wetlands are provided. Total sediment Pb was higher than Cd (34.95 versus 0.024 μg/g dry weight). The geoaccumulation index indicated that the sediment was “uncontaminated”, but some stations were categorized as “unpolluted” to “moderately polluted”. Less than 0.01 of Pb existed in exchangeable and carbonate fractions. The sum of exchangeable and carbonate-bound fractions of Cd was 42%, suggesting that Cd poses high risk to the aquatic ecosystems. Total Cd and Pb exhibited positive relationships with total organic matter. Considering spatial distribution maps of total and bioavailable fractions of metals suggested that high concentrations of metals does not necessarily indicate high bioavailable fraction. The methodologies we used in this study can be in more effective management of aquatic ecosystems, as well as ecological risk assessment of metals, and remediation programs.     

Comparison of univariate and multivariate geographically weighted regression for estimating air temperature over Iran

Station recording air temperature (T_a) has limited spatial coverage, especially in unpopulated areas. Since temperature can change greatly both spatially and temporally, stations data are often inadequate for meteorology and subsequently climatology studies. Time series of moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (T_s) and normalized difference vegetation index (NDVI) products, combined with digital elevation model (DEM), albedo from Era-Interim and meteorological data from 2006 to 2015, were used to estimate daily mean air temperature over Iran. Geographically weighted regression was applied to compare univariate and multivariate model accuracy. In the first model, which only interfered with land surface temperature (LST), the results indicate a weak performance with coefficient of determination up to 91% and RMSE of 1.08 to 2.9 °C. The mean accuracy of a four-variable model (which used LST, elevation, slope, NDVI) slightly increased (6.6% of the univariate model accuracy) when compared to univariate model. RMSE dropped by 19% of the first model. By addition albedo in the third model, the coefficient of determination increased significantly. This increase was 32% of the univariate model and 23.75% of the 4-variable model accuracy. The statistical comparison between the three models revealed that there is significant improvement in air estimation by applying the geographically weighted regression (GWR) method with interfering LST, NDVI, elevation, slope, and albedo with mean absolute RMSE of 0.62 °C and mean absolute R² of 0.99. In order to better illustrate the third model, t values were spatially mapped at 0.05 level.     

Comparison of LLR,MLP, Elman,NNARX and ANFIS Models—with a case study in solar radiation estimation

Despite the widespread application of nonlinear mathematical models, comparative studies of different models are still a huge task for modellers. This is because a large number of trial and error processes are needed to develop each model, so the workload will be multiplied into an unmanageable level if many types of models are involved. This study presents an efficient approach by using the Gamma test (GT) to select the input variables and the training data length, so that the trial and error workload can be greatly reduced. The methodology is tested in estimating solar radiation at the Brue catchment, UK. Several nonlinear models have been developed efficiently with the aid of the GT, including local linear regression, multi-layer perceptron (MLP), Elman neural network, neural network auto-regressive model with exogenous inputs (NNARX) and adaptive neuro-fuzzy inference system (ANFIS). This work is only feasible within the time and resources constraint, due to the GT in reducing huge workload of the trial and error process.     

Nonlinear dynamic tests of a reinforced concrete frame building at different damage levels

This paper discusses the dynamic tests of a two-story infilled reinforced concrete (RC) frame building using an eccentric-mass shaker. The building, located in El Centro, CA, was substantially damaged prior to the tests due to the seismic activity in the area. During the testing sequence, five infill walls were removed to introduce additional damage states and to investigate the changes in the dynamic properties and the nonlinear response of the building to the induced excitations. The accelerations and displacements of the structure under the forced and ambient vibrations were recorded through an array of sensors, while lidar scans were obtained to document the damage. The test data provide insight into the nonlinear response of an actual building and the change of its resonant frequencies and operational shapes due to varying damage levels and changes of the excitation amplitude, frequency, and orientation.     

Earthquake-induced landslide prediction using back-propagation type artificial neural network: case study in northern Iran

Natural Hazards - Landslides can cause extensive damage, particularly those triggered by earthquakes. The current study used back propagation of an artificial neural network (ANN) to conduct risk...     

Geothermometry and circulation depth of groundwater in Semnan thermal springs,Northern Iran

Semnan thermal springs with high TDS and moderate temperature are located northwest of Semnan, the northern part of Iran. The spatial and temporal variations of physicochemical characteristics of the thermal and cold springs were investigated for the recognition of origin and dominant hydrogeochemical processes. Results show that the thermal springs have the same origin, but due to different ascending flow paths and different conductive cooling mechanism, their temperatures vary. The chemical composition of thermal waters is controlled by dolomite, halite and sulfate minerals dissolution and calcite precipitation and bacterial sulfate reduction. The concentration of major and trace elements in the thermal springs does not change in wet and dry seasons notably because they are derived from old groundwater with deep circulation and high temperature. Seasonal change in the concentration of some trace elements is due to the seasonal variation of pH, Eh, temperature and dilution by shallow waters. Decreasing SO₄ and carbonate saturation index and increasing Na/Cl ratios and Ca content in the dry season show dilution effect caused by the previous heavy rainfall events. The temperature of the heating reservoir based on K–Mg, chalcedony, quartz and chemical equilibrium approach was approximately estimated in the range of 60–80 °C. Hydrogeologically, a conceptual model was suggested for the thermal springs. The general groundwater flow direction is probably from the dolomite Lar Formation in Chenaran anticline toward the adjacent syncline in a confined condition, and then a thrust fault acts as a conduit and redirects the thermal water to the emerging springs at the surface.     

Application of the Dynamic Cone Penetrometer (DCP) for determination of the engineering parameters of sandy soils

Determination of the in situ engineering properties of foundation materials has always been a challenge for geotechnical engineers and, thus, several methods have been developed so far. Dynamic Cone Penetration (DCP) test is one of the most versatile amongst them. In the present research, a light weight simple DCP device was developed and used for evaluation of the engineering properties of sandy soils in laboratory conditions. The device consisted of an 8-kg hammer that drops over a height of 575 mm, and drives a 60° cone tip with 20 mm base diameter into the ground. To control the validation of the results, laboratory direct shear and plate load tests were used as reference tests. The soil sample was a poorly graded sandy soil (SP) taken from alluvial deposits of the Tehran plain. All DCP tests and PLTs were undertaken on compacted soil in a mould with 700 mm diameter and 700 mm height. Based on the results of the experiments, the relationships between Dynamic Penetration Index (DPI), relative density (D_r), modulus of elasticity (E), shear modulus (G), modulus of subgrade reaction (K_S), and the friction angle of the soil were obtained with a high coefficient of determination (> 90%). The repeatability of the test results was also evaluated by calculating the coefficient of variations (C_v), which was less than 30% for all tests.