جستجوی مقالات مرتبط با کلیدواژه "observation wells" در نشریات گروه "جغرافیا"

groundwater forms part of the water cycle and is a reliable source of water for human consumption, as well as in Iran, most of the water used in the drinking, agricultural and industrial sectors is supplied from groundwater. Due to the condition of Iran, due to the deficit surface water resources, the use of groundwater resources for water supply has been considered. 

1) Use of the Radial basis function of the neural network
If a generalized regression function of the neural network, PNN / GRNN, is selected, all of the network weights can be calculated as probable. In RBF, a Gaussian transmission function is used which is similar to a ring (GRNN) One of the benefits of these networks is its rapid learning of other networks, including the multi-layered perceptron network of MLPs. The Gaussian networks of the transfer function network are of an unidentified learning type, but the output is a controlled learning type. The network is very practical in simulating hydrological and hydrological issues, due to its rapid training, generalizability and ease of use.
 
2) Use a support vector machine
  A support vector machine is proposed based on the principle of minimizing structural error. A support vector machine can be used both for categorization issues and for the estimation of functions. used a new error function called ε-insensitive for machine application in regression problems, so that this function ignores errors that are at a given distance from actual values. This function is defined as How to design a network.
In this study, the data used were 95 piezo metric wells in the Amol-Babol Plain. Data were used with a mean average of 30 years. In order to simulate the depth of the groundwater table, effective factors such transmissivity of aquifer formations, precipitation values and distance from water resources. For the design of the network, for both models, there are two classes of training and testing data. One important criterion for training a network is the number of repetitions or epoch during training. The higher the number of replays, the error decreases so that training data can be converted, which will increase the number of unsuccessful repetitions at that time. for network optimization purposes, the goal of network training is to reduce network error, which can improve the relationship between the input and output of the model. Due to the lack of specific rules for designing artificial neural networks (ANNs), various structures have been investigated to optimize the design. Select the number and type of input parameters for the model is important. For this reason, seven design input patterns are given. Which was carried out in the software of the NeuroSolutions. 

for the optimal simulation model based on all parameters and the provision of all its input data will require a great deal of time and cost, a method based on the main parameters of input (optimal inputs) is modeled and validated. it was observed that the predicted aquifer level for both models is about to its actual value.

The results obtained with the Radial basis function and the support vector machine represent this point where the support vector machine and the radial function have the ability to have approximately the same ability to predict and modeling, although generally the results of the Radial basis function are more acceptable. The results of the model test are shown. As the results of the survey are presented, among the methods implemented in the model, using effective factors such as transmissivity of aquifer formations, precipitation values and distance from water resources to predict level of level The aquifer was used. The results of the test showed that the Radial basis function of the support vector machine with a correlation coefficient of 0.82 and a mean absolute error of 1.94 is an appropriate tool for prediction of water resource management.

During recent years, high exploitation from the aquifer declines the quantity and quality of this water resource. In this regard, it seems that the hydrogeology cal management studies are needed for this aquifer. Hydro geological aspects of the Kalachoo plain are studied using the results of the pumping test, geological logs of the observation and exploration wells, and field observation. The unit hydrograph of the aquifer is drowning using the information of the 12 observation wells. Because of non suitable distribution of observation wells, central part of the plain is selected for model design.Then water level through October 2002 is selected for steady state condition in the aquifer at (2002-2004). Using the available data, needed packages by VISUAL MODFLOW 2.6 are completed and conceptual model is constructed. Then this conceptual model is calibrated, manually, for steady state condition while the considered parameters are hydraulic conductivity, inflow and outflow of margins as virtual wells. In order to optimize the value of specific yields and recharge parameters, calibration process is continued under unsteady state condition. Calibration period length is one year (October 2002 to October 2003). Model verification is performed for five years. Verification results indicate that the calibration model is capable for management practices. After the artificial recharge on the Tang-e-Hygoon and Tang-e-Sapoo in model, the result has been showed the positive effects on the recovery of ground water level in Kalacho plain.