Toward reliable calibration of aquifer hydrodynamic parameters: characterizing and optimization of arid groundwater system using swarm intelligence optimization algorithm

Despite advanced development in computational techniques, the issue of how to adequately calibrate and minimize misfit between system properties and corresponding measurements remains a challenging task in groundwater modeling. Two important features of the groundwater regime, hydraulic conductivity (k) and specific yield (S _y), that control aquifer dynamic vary spatially within an aquifer system due to geologic heterogeneity. This paper provides the first attempt in using an advanced swarm-intelligence-based optimization algorithm (cuckoo optimization algorithm, COA) coupled with a distributed hydrogeology model (i.e., MODFLOW) to calibrate aquifer hydrodynamic parameters (S _y and k) over an arid groundwater system in east Iran. Our optimization approach was posed in a single-objective manner by the trade-off between sum of absolute error and the adherent swarm optimization approach. The COA optimization algorithm further yielded both hydraulic conductivity and specific yield parameters with high performance and the least error. Estimation of depth to water table revealed skillful prediction for a set of cells located at the middle of the aquifer system whereas showed unskillful prediction at the headwater due to frequent water storage changes at the inflow boundary. Groundwater depth reduced from east toward west and southwest parts of the aquifer because of extensive pumping activities that caused a smoothening influence on the shape of the simulated head curve. The results demonstrated a clear need to optimize arid aquifer parameters and to compute groundwater response across an arid region.     

Adaptive neuro-fuzzy prediction of the optimum parameters of protective spur dike

This study proposes a new approach for determining the optimum dimensions of a protective spur dike to mitigate the amount of scour around existing spur dikes. Several parameters of a protective spur dike were studied to determine their optimum values, including length, angle, and distance. Also the effect of changes of flow intensity and sediment size were examined. The main objective of this article was to predict the optimum values of protective spur dikes to attain the best performance. To predict the parameters of protective spur dikes for controlling the scour around spur dikes, we used the adaptive neuro-fuzzy inference system method to construct a process that simulates the optimal parameters of a protective spur dike, including the actual length of the protective spur dike, the actual length of the main spur dikes, the distance between the protective spur dike and the first spur dike, the angle between the protective spur dike and the direction of flow, the intensity of the flow, and median size of the bed sediments. This intelligent estimator was implemented using MATLAB/Simulink, and the performances were investigated. The simulation results presented in this paper show the effectiveness of the developed method.     

Hybrid denoising-jittering data pre-processing approach to enhance multi-step-ahead rainfall–runoff modeling

Successful modeling of stochastic hydro-environmental processes widely relies on quantity and quality of accessible data and noisy data might effect on the functioning of the modeling. On the other hand in training phase of any Artificial Intelligence based model, each training data set is usually a limited sample of possible patterns of the process and hence, might not show the behavior of whole population. Accordingly in the present article first, wavelet-based denoising method was used in order to smooth hydrological time series and then small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smoothed time series to form different denoised-jittered training data sets, for Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling of daily and multi-step-ahead rainfall–runoff process of the Milledgeville station of the Oconee River and the Pole Saheb station of the Jighatu River watersheds, respectively located in USA and Iran. The proposed hybrid data pre-processing approach in the present study is used for the first time in modeling of time series and especially in modeling of hydrological processes. Furthermore, the impacts of denoising (smoothing) and noise injection (jittering) have been simultaneously investigated neither in hydrology nor in any other engineering fields. To evaluate the modeling performance, the outcomes were compared with the results of multi linear regression and Auto Regressive Integrated Moving Average models. Comparing the achieved results via the trained ANN and ANFIS models using denoised-jittered data showed that the proposed data pre-processing approach which serves both denoising and jittering techniques could improve performance of the ANN and ANFIS based single-step-ahead rainfall–runoff modeling of the Milledgeville station up to 14 and 12% and of the Pole Saheb station up to 22 and 16% in the verification phase. Also the results of multi-step-ahead modeling using the proposed data pre-processing approach showed improvement of modeling for both watersheds.     

Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters

The present study attempts to model the spatial variability of three groundwater qualitative parameters in Guilan Province, northern Iran, using artificial neural networks (ANNs) and support vector machines (SVMs). Data collected from 140 observation wells for the years 2002–2014 were used. Five variables, X and Y coordinates of the observation well, distance of the observation well from the shoreline, areal average 6-month rainfall depth, and groundwater level at the day of water quality sampling, were considered as primary input variables. In addition, nine qualitative variables were also considered as auxiliary input variables. Electrical conductivity (EC), sodium concentration (Na⁺), and sulfate concentration (SO₄ ^2?) of the groundwater in the region were estimated using ANNs and SVMs with different input combinations. The results showed that both ANNs and SVMs work well when the only primary input variable is the well location. The ANN yielded an RMSE of 1.03 mEq/l for SO₄ ^2?, 1.05 mEq/l for Na⁺, and 203.17 μS/cm for EC, using the X and Y coordinates of the observation wells in the study area. In the case of SVM, these values were, respectively, 0.87, 0.87, and 176.68. Considering the auxiliary input variables (pH, EC, and the concentrations of Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl^?, SO₄ ^2?, and HCO₃ ^?) resulted in a significant decrease in the RMSE of both ANNs (0.22, 0.30, and 33.04) and SVMs (0.26, 0.34, and 36.23). Comparing these RMSE values with those of cokriging interpolation technique (0.59, 0.98, and 177.59) indicated that ANNs and SVMs produced more accurate estimates of the three qualitative parameters. The relative importance of auxiliary input variables was also determined using Gamma test. The output uncertainty of ANNs and SVMs were determined using p-factor and d-factor. The results showed that SVMs have less uncertainty than ANNs.     

Assessing building vulnerability to tsunami using the PTVA-3 model: A case study of Chabahar Bay,Iran

Chabahar Bay, in southeastern Iran, lies at the north of the Gulf of Oman and close to the Makran Subduction Zone, which makes it a region that is susceptible to tsunamis. This bay has an increasingly important role in Iran’s international trade, and therefore the assessment of the regional vulnerability to the effects of a tsunami is vital. Based on both the details of historical events and the results of numerical modeling of the propagation pattern of a tsunami in this region, this study assessed the vulnerability of buildings within the Chabahar Bay region to a tsunami event. The Papathoma Tsunami Vulnerability Assessment (PTVA) model was used to calculate a relative vulnerability index (RVI) for the affected buildings based on their physical and structural characteristics. The results showed that in a postulated worst-case-scenario tsunami event in the Chabahar Bay area, approximately 60 % of the residential buildings would be affected, a level of damage that is categorized as “Average” in the RVI classification. Overall, the economic losses related to the damage of residential buildings due to a tsunami in the Chabahar Bay area are anticipated to be the equivalent of US$ 16.5 million.     

Seismic evaluation of rocking structures through performance assessment and fragility analysis

Numerical studies have been conducted for low- and medium-rise rocking structures to investigate their efficiency as earthquake-resisting systems in comparison with conventional structures. Several non-linear time-history analyses have been performed to evaluate seismic performance of selected cases at desired ground shaking levels, based on key parameters such as total and flexural story drifts and residual deformations. The Far-field record set is selected as input ground motions and median peak values of key parameters are taken as best estimates of system response. In addition, in order to evaluate the probability of exceeding relevant damage states, analytical fragility curves have been developed based on the results of the incremental dynamic analysis procedure. Small exceedance probabilities and acceptable margins against collapse, together with minor associated damages in main structural members, can be considered as superior seismic performance for medium-rise rocking systems. Low-rise rocking systems could provide significant performance improvement over their conventional counterparts notwithstanding certain weaknesses in their seismic response.     

Genesis of Tertiary Magnetite–Apatite Deposits,Southeast of Zanjan,Iran

Magnetite–apatite deposits in the Alborz volcano–plutonic belt, southeast Zanjan, in Iran, have blade, lenzoid, and vein forms, which extend in an E‐W direction. There are many magnetite–apatite veins and veinlets in this region, and some of them are economically important, such as Zaker, Morvarid, Sorkheh–Dizaj, and Aliabad. The sizes of the vein orebodies vary between 2 and 16 m in width, 10–100 m in length, and 5–40 m in depth. Microscopic examination of thin sections and polishes indicate that they are composed of magnetite and apatite, with minor amounts of goethite, hematite, actinolite, quartz, muscovite–illite, talc, dolomite, and calcite. The geochemistry and mineralogy of the granitic host rock reveals that it is calc‐alkaline and I‐type. Field observations, mineral paragenesis, the composition of the orebodies, and the composition of the fluid inclusions in the apatite minerals with low salinity (less than 20 wt.% NaCl equivalent) indicate that these magnetite veins were hydrothermally generated at about 200–430°C and are not related to silica–iron oxide immiscibility, as are the major Precambrian magnetite deposits in central Iran.     

Downslope soil detachment–transport on steep slopes via rain splash

This study developed a one‐dimensional model of downslope rain splash transport based on field experiments and previous studies. The developed model considers soil detachment processes, ground cover, probability densities, and the effect of overland run‐off in preventing detachment. Field monitoring was conducted to observe precipitation run‐off, ground cover, and sediment production on steep hillslopes. Field‐observed data were used to develop the splash detachment rate equation, probability densities for splash transport, and the maximum splash transport distance. Observed and estimated splash transport showed overall agreement, with some differences for small storm events or events with relatively low intensity, probably caused by variation of overland run‐off depth and connectivity as well as differences in soil surface cohesion at various degrees of wetness. Our model can provide insights on the interactions among rainfall intensity, soil surface condition, soil wetness, and splash transport on forested hillslopes. Copyright © 2011 John Wiley & Sons, Ltd.     

Adaptive neuro-fuzzy selection of the optimal parameters of protective spur dike

This study proposes a new approach for determining optimum dimensions of protective spur dike to mitigate scour amount around existing spur dikes. The main objective of this article was to predict the most optimum values of the protective spur dikes to reach the best performance. To predict the protective spur dike parameters for scour controlling around spur dikes, this paper constructed a process which selects the optimal protective spur dike parameters in regard to actual length of the protective spur dike, actual length of the main spur dikes, distance between the protective spur dike and the first spur dike, angle between protective spur dike and flow direction, flow intensity and median size of bed sediments with adaptive neuro-fuzzy (ANFIS) method. To build a protective spur dike with the best features, it is desirable to select and analyze factors that are truly relevant or the most influential to the spur dike. This procedure is typically called variable selection, and it corresponds to finding a subset of the full set of recorded variables that exhibits good predictive abilities. In this study, architecture for modeling complex systems in function approximation and regression was used, based on using ANFIS. Variable searching using the ANFIS network was performed to determine how the five factors affect the protective spur dike. Experimental model of the protective spur dike was used to generate training and checking data for the ANFIS network.