Extracting of prospective groundwater potential zones using remote sensing data,GIS, and a probabilistic approach in Bojnourd basin,NE of Iran

Various groundwater potential zones for the assessment of groundwater availability in the Bojnourd basin have been investigated using remote sensing, GIS, and a probabilistic approach. Five independent groundwater factors, including topography, ground slope, stream density, geology units, lineament density, and a groundwater productivity factor, i.e., springs’ discharge, were applied. Discharge rates of 226 springs over the area were collected, and the probabilistic model was designed by the discharge rates of springs as the dependent variable. For training the probabilistic model, a ratio of 70/30% of springs’ discharge was applied and discharge rates of 151 springs were selected to randomly train the model. The frequency ratio for each factor was calculated, and the groundwater potential zones were extracted by summation of frequency ratio maps. The groundwater potential map was also classified into four classes, viz., “very good” (with a frequency ratio of >6.75), “good” (5.5FR6.75), “moderate” (4.75FR5.5), and “poor” (FR4.75). Then, the model was verified based on a success-rate curve method which resulted in obtaining an accuracy ratio of 75.77%. Finally, sensitivity analysis was applied by a factor removal method in five steps. Results reveal that topography factor has the biggest effect on the groundwater potential map and removing this factor eventuates in the lowest accuracy of the final map (AUC = 63. 73%). The groundwater potential map is fairly affected by removing the lineament density factor with an accuracy of 68.80%. Removing the lineament density factor has the lowest effect on the final map with accuracy of 68.80%.     

Predicting longitudinal dispersion coefficient using ANN with metaheuristic training algorithms

A reliable prediction of dispersion coefficient can provide valuable information for environmental scientists and river engineers as well. The main objective of this study is to apply intelligence techniques for predicting longitudinal dispersion coefficient in rivers. In this regard, artificial neural network (ANN) models were developed. Four different metaheuristic algorithms including genetic algorithm (GA), imperialist competitive algorithm (ICA), bee algorithm (BA) and cuckoo search (CS) algorithm were employed to train the ANN models. The results obtained through the optimization algorithms were compared with the Levenberg–Marquardt (LM) algorithm (conventional algorithm for training ANN). Overall, a relatively high correlation between measured and predicted values of dispersion coefficient was observed when the ANN models trained with the optimization algorithms. This study demonstrates that the metaheuristic algorithms can be successfully applied to make an improvement on the performance of the conventional ANN models. Also, the CS, ICA and BA algorithms remarkably outperform the GA and LM algorithms to train the ANN model. The results show superiority of the performance of the proposed model over the previous equations in terms of DR, R ² and RMSE.     

Monthly stream flow forecasting via dynamic spatio-temporal models

In this research, a dynamic linear spatio-temporal model (DLSTM) was developed and evaluated for monthly streamflow forecasting. For parameter estimation, coupled expectation–maximization (EM) algorithm and Kalman filter was adopted. This combination enables the model to estimate the state vector and parameters concurrently. Different forecast scenarios including various combinations of upstream stations were considered for downstream station streamflow forecasting. Several statistical criteria, nonparametric and visual tests were used for model evaluation. Results indicated that the spatio-temporal model performed acceptably in almost all scenarios. The dynamic model was able to capitalize on coupled spatial and temporal information provided that there is spatial connectivity in the studied hydrometric stations network. Moreover, threshold level method was used for model evaluation in drought and wet periods. Results indicated that, in validation phase, the model was able to forecast the drought duration and volume deficit/over threshold, although volume deficit/over threshold could not be accurately simulated.     

Scour and flow field around a spur dike in a 90° bend

Spur dike is an important element in fiver training that creates rapid variations in flow field, sediment transport and bed topography. The mechanism of flow and sediment transport in a channel bend is very complex, especially when a spur dike is constructed in a bend. Most of previous investigations on flow behavior and scour around spur dike were carried out in straight channels. In this paper results of experiments on flow field and scour around a spur dike in a 90 degree channel bend are presented, Sand with uniform grain size was used as the bed material, Experiments were conducted for different locations and different lengths of spur dikes at the bend with different values of discharge, The three dimensional flow fields around a spur dike were investigated, The maximum depth of scour was correlated to the Froude numbers, lengths and the locations of spur dike in the bend.     

Innovative mitigation method for buried pipelines crossing faults

A new remediation technique is proposed to mitigate large deformations imposed on buried pipeline systems subject to permanent ground deformation. With this technique, low-density gravel(LDG) with high porosity, such as pumice,is used as backfill in the trench containing the pipe near an area susceptible to PGD. This countermeasure decreases soil resistance, soil-pipe interaction forces and strain on the pipe as the pipeline deformation mechanism changes to a more desirable shape. Expanded polys...     

Secondary circulation induced by flow curvature and Coriolis effects around headlands and islands

Previous studies have shown that flow curvature in river bends generates a secondary circulation in the plane normal to the mean flow direction. A similar circulation pattern is shown to exist in oceanic situations when flows are subject to curvature, mainly due to interaction with topographic features. However, it is shown that, due to differences between oceanic conditions and river bends, theory and prediction methods based on the assumptions for river bends are invalid for oceanic flows. Via scaling arguments based on the equations of motion, that include both the effects of flow curvature and the Coriolis force, parameters that govern the different flow regimes are identified. The maximum strength of the secondary flow is derived for each flow regime and is verified using a three-dimensional (3-D) numerical model applied to an idealized island. It is also shown that upwelling, due to the generation of secondary flow, occurs off the tips of the headland or island, and its influence can extend far downstream.Responsible Editor: Richard Signell