Kaveh Ostad-Ali-Askari

Bio: Kaveh Ostad-Ali-Askari is an academic researcher from Isfahan University of Technology. The author has contributed to research in topics: Water resources & Surface irrigation. The author has an hindex of 23, co-authored 126 publications receiving 1411 citations. Previous affiliations of Kaveh Ostad-Ali-Askari include Islamic Azad University of Najafabad & Islamic Azad University, Isfahan.

Artificial neural network for modeling nitrate pollution of groundwater in marginal area of Zayandeh-rood River, Isfahan, Iran

Abstract: Excessive use of chemical fertilizers, especially nitrogen fertilizers to increase crop and improper purification, and delivery of municipal and industrial wastewater are proposed as factors that increase the amount of nitrate in groundwater in this area. Thus, investigation of nitrate contamination as one of the most important environmental problems in groundwater is necessary. In the present study, modeling and estimation of nitrate pollution in groundwater of marginal area of Zayandeh-rood River, Isfahan, Iran, was investigated using water quality and artificial neural networks. 100 wells (77 agriculture well, 13 drinking well and 10 gardens well) in the marginal area of Zayandeh-rood River, Isfahan, Iran were selected. MATLAB software and three-layer Perceptron network were used. The back-propagation learning rule and sigmoid activation function were applied for the training process. After frequent experiments, a network with one hidden layer and 19 neurons make the least error in the process of network training, testing and validation. ANN models can be applied for the investigation of water quality parameters.

Quantity and quality modelling of groundwater to manage water resources in Isfahan-Borkhar Aquifer

Abstract: Groundwater resources are increasingly exposed to significant overexploitation in many parts of the world, markedly in Iran, one of the most arid areas. Social, economic and environmental aspects including water quality and quantity concerns are necessary for sustainable management of water resources. The aim of the current study was to investigate the changes in groundwater levels and quality in the Isfahan-Borkhar aquifer. Groundwater fluctuation contour lines maps provided in Geographic Information System (GIS) during 1971–2005 in this area indicate that excessive extraction of wells has led to major decline in water levels and a marked increase in concentration of Total Dissolved Solids (TDS). The Groundwater Modeling System, a three-dimensional MODFLOW model paired with MT3D, was utilized to survey the aquifer characterization in the area. In the first step, usage high amount of hydrological and geological data, the conceptual model was developed and calibrated in both steady and transient states. The results of the calibration showed that the error between calculated and observed levels was in optimal level. Subsequently, since rainfall is decreasing annually in the study area and the aquifer is in danger of drought, and uncontrolled exploitation of wells has led it to a crisis, two scenarios were considered to simulate quantity changes in the aquifer: Simulation in drought and rainfall reduction. The results indicate that during both the drought period and increasing exploiting from the pumping wells, the level of water has fallen 0.5–0.1 m/s annually, and it will destroy the aquifer. Finally, the calculated hydraulic heads and velocity of flow of groundwater in the aquifer are recovered in the mass transport modelling package MT3D to find the concentration of TDS in the groundwater. Simulation results indicate that concentration of TDS is with far more amount in the areas around the lake located in central parts due to evaporation of Borkhar-Isfahan Aquifer and geological structure of the region. Scenarios considered for prediction by transport model show that concentration of TDS would enhance if the current situation continues; however, this is mainly influenced by hydrology and geology of the area.

Prediction of tunnelling impact on flow rates of adjacent extraction water wells

Abstract: The decline or drying up of groundwater sources near a tunnel route is damaging to groundwater users. Therefore, forecasting the impact of a tunnel on nearby groundwater sources is a challenging task in tunnel design. In this study, numerical and analytical approaches were applied to the Qomroud water conveyance tunnel (located in Lorestan province, Iran) to assess the impact of tunnelling on the nearby extraction water wells. Using simulation of groundwater-level fluctuation owing to tunnelling, the drawdown at the well locations was determined. From the drawdowns and using Dupuit9s equation, the depletion of well flow rates after tunnelling was estimated. To evaluate the results, observed well flow rates before and after tunnelling were compared with the predicted flow rates. The observed and estimated water well flows (before and after tunnelling) showed a regression factor of 0.64, pointing to satisfactory results

Subsurface drain spacing in the unsteady conditions by HYDRUS-3D and artificial neural networks

Abstract: The methodological process for defining the drainage retention capacity of surface layers under conditions of unsteady-state groundwater flow was demonstrated. An artificial neural network analyst model was advanced based on the information from the well-tested model HYDRUS-2D/3D. Artificial neural network knowledge is reported as an intermittent to physical-based modeling of subsurface water distribution from trickle emitters. Three options are prospected to create input-output functional relations from information created using a numerical model (HYDRUS-2D). Artificial neural networks are a tool for modeling of non-linear systems in various engineering fields. These networks are effective tools for modeling non-linear systems. Each artificial neural network includes an input layer and an output layer between which there are one or some hidden layers. In each layer, there are one or several processing elements or neurons. The neurons of the input layer are independent variables of the understudy issue and the neurons of the output layer are its dependent variables. An artificial neural system, through exerting weight on inputs and by using an activation function, attempts to achieve a desirable output. In this research, in order to calculate the drain spacing in an unsteady state in a region situated in the northeast of Ahwaz, Iran, with different soil properties and drain spacing, the artificial neural networks have been used. The neurons in the input layer were specific yield, hydraulic conductivity, depth of the impermeable layer, and height of the water table in the middle of the interval between the drains in two-time steps. The neurons in the output layer were drain spacing. The network designed in this research included a hidden layer with four neurons. The distance of drains computed via this method had a good agreement with real values and had a high precision in comparison with other methods. This was done for three types of linear activation functions and hyperbolic and sigmoid tangents. The mean error was 0.1455, 0.092, and 0.0491, respectively.

Petroleum Hydrocarbon-Degrading Bacteria for the Remediation of Oil Pollution Under Aerobic Conditions: A Perspective Analysis.

Abstract: With the sharp increase in population and modernization of society, environmental pollution resulting from petroleum hydrocarbons has increased, resulting in an urgent need for remediation. Petroleum hydrocarbon-degrading bacteria are ubiquitous in nature and can utilize these compounds as sources of carbon and energy. Bacteria displaying such capabilities are often exploited for the bioremediation of petroleum oil-contaminated environments. Recently, microbial remediation technology has developed rapidly and achieved major gains. However, this technology is not omnipotent. It is affected by many environmental factors that hinder its practical application, limiting the large-scale application of the technology. This paper provides an overview of the recent literature referring to the usage of bacteria as biodegraders, discusses barriers regarding the implementation of this microbial technology, and provides suggestions for further developments.