Integrated geophysical, hydrogeochemical and artificial intelligence techniques for groundwater study in the Langat Basin, Malaysia / Mahmoud Khaki

Geophysical, hydrogeochemical and artificial intelligence techniques were used to study the groundwater characteristics and their associated problems in Langat Basin, Malaysia. Resistivity surveys and geochemical analyses were used to delineate regions of Langat Basin that are contaminated by brackish water. Hydrogeochemical data of groundwater samples collected from seventeen wells from 2008 to 2013 were analysed. Ninety eight geoelectrical resistivity survey measurements were conducted to obtain subsurface resistivity data. The Wenner array was selected because of its sensitivity in detecting vertical changes in subsurface resistivity. The resistivity imaging results show that the upper layer is usually clay and below this layer is an aquifer with various depths of 10 to 30 m, and the layer thickness changes from 10 to 45 m, respectively, from east to west across the study area. The depth to bedrock varies from 30 m up to 65 m. The results learned from the resistivity survey confirmed the pattern of a continuous structure of layers, as detected from the borehole and geological information. Chemical analyses show the total dissolved solid exceeds 1000 mg/L in the west and is less than 1000 mg/L in the east of study area. Furthermore, the results of the resistivity survey and those from the hydrogeochemical analyses show that the groundwater within the study area is a mixture of brackish and freshwater zones. A novel investigation in modelling of groundwater level and quality using Artificial Neural Networks (ANNs) and Adaptive neuro fuzzy inference systems (ANFIS) methods was developed in the study area. Water table modelling based on ANNs and ANFIS technology were developed to simulate the water table fluctuations based on the relationship between the variations of rainfall, humidity, evaporation, minimum and maximum temperature and water table depth. The mean square errors and correlation coefficient of the water table depth models for 84 months were between 0.0043 to 0.107 and 0.629 to 0.99 respectively for all models. Evaluating the results of the various kinds iii of models show the earned results of the ANFIS model are superior to those gained from ANNs in which they are both more precise and with less error. Furthermore, four common training functions; Gradient descent with momentum and adaptive learning rate back propagation, Levenberg-Marquardt algorithm, Resilient back propagation, Scaled conjugate gradient were compared for the modelling of groundwater level. These results confirm that, for all the networks the Levenberg-Marquardt algorithm is the most effective algorithm to model the groundwater level. This study also developed the potential of the ANFIS and ANN to simulate total dissolved solid (TDS) and electrical conductivity (EC) by employing the values of other existing water quality parameters from five sampling stations over six years from 2008 to 2013. A good agreement between simulate values and their respective measured values in the quality of the groundwater were found. TDS and EC values predicted from the model accompanying obtained result present increasing in concentration continually in the future whereas for well no 3 (the nearest well to costal line) reaches at 32625.51 mg/L and 69501.76 μS/cm in 2025, respectively

Artificial neural network technique for modeling of groundwater level in Langat Basin, Malaysia

Forecasting of groundwater level variations is a significantly needed in groundwater resource management. Precise water level prediction assists in practical and optimal usage of water resources. The main objective of using an artificial neural network (ANN) was to investigate the feasibility of feed-forward, Elman and Cascade forward neural networks with different algorithms to estimate groundwater levels in the Langat Basin from 2007 to 2013. In order to examine the accuracy of monthly water level forecasts, effectiveness of the steepness coefficient in the sigmoid function of a developed ANN model was evaluated in this research. The performance of the models was evaluated using the mean squared error (MSE) and the correlation coefficient (R). The results indicated that the ANN technique was well suited for forecasting groundwater levels. All models developed had shown acceptable results. Based on the observation, the feed-forward neural network model optimized with the Levenberg-Marquardt algorithms showed the most beneficial results with the minimum MSE value of (0.048) and maximum R value of (0.839), obtained for simulation of groundwater levels. The present research conclusively showed the capability of ANNs to provide excellent estimation accuracy and valuable sensitivity analyses

Combined postconditioning with ischemia and cyclosporine-A restore oxidative stress and histopathological changes in reperfusion injury of diabetic myocardium

Objective(s): Chronic diabetes impedes cardioprotection in reperfusion injury and hence protecting the diabetic heart would have important outcomes. In this study, we evaluated whether combined postconditioning with ischemia and cyclosporine-A can restore oxidative stress and histopathological changes in reperfusion injury of the diabetic myocardium. Materials and Methods: Streptozocin-induced diabetic rats’ hearts and nondiabetic controls in eight subgroups (with or without receiving ischemic-postconditioning (IPostC), cyclosporine-A, an inhibitor of mitochondrial permeability transition, or both of them) suffered from 30 min regional ischemia followed by 45 min reperfusion on an isolated-heart Langendorff system. The levels of lactate dehydrogenase (LDH) in the coronary effluent, and the levels of oxidative stress markers including 8-isoprostane, superoxide dismutase (SOD), glutathione peroxidase (GPX), and total antioxidant capacity (TAC) in myocardial supernatant prepared from the ischemic zone were measured using specific kits, spectrophotometrically. Histopathological studies were performed through the hematoxylin-eosin staining method. Results: Administration of IPostC and cyclosporine-A (alone or together) in nondiabetic hearts potentially reduced the severity of histological changes and level of LDH release as compared with untreated-controls (P0.1). However, the combined postconditioning with ischemia and CsA exerted significant protective effects in diabetic hearts (

Attenuated Lead Induced Apoptosis in Rat Hepatocytes in the Presence of Lycopersicon Esculentum

Lead (Pb), has, for decades, being known for its adverse effects on various body organs and systems. In the present study, the damage of Pb on the Liver tissue apoptosis was investigated, and Lycopersicon esculentum as an antioxidants source was administered orally to prevent the adverse effects of Pb. Eighteen Wistar rats, randomized into three groups (n=6), were used for this study. Animals in Group A served as the control and were drinking distilled water. Animals in Groups B and C were drinking 1%Lead acetate (LA). Group C animals were, in addition to drinking LA, treated with 1.5 ml/day of Lycopersicon esculentum. Treatments were for three months. The obtained results showed that lead acetate caused significant reductions in the liver weight, plasma and tissue superoxide dismutase and catalase activity, but a significant increase in plasma and tissue malondialdehyde concentration but Lycopersicon esculentum have an inhibitory effect on LA liver adverse effect. So, it can be concluded that Lycopersicon esculentum have a significant protective effect on liver lead acetate adverse effects as well as, lead acetate -induced oxidative stress