Mehdi Panahi; Farhad Misagi; Parisa Asgari
Volume 16, Issue 1 , April 2018, , Pages 1-14
Abstract
Introduction: In recent decades, climate change and the lack of adequate and timely rainfall, due to the lack of water supply for irrigated plants, the limitation of renewable land and water resources, the heterogeneous distribution of spatial and temporal distribution of fresh water, as well as the ...
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Introduction: In recent decades, climate change and the lack of adequate and timely rainfall, due to the lack of water supply for irrigated plants, the limitation of renewable land and water resources, the heterogeneous distribution of spatial and temporal distribution of fresh water, as well as the rapid growth of the world population, including Iran, There are increasing problems in providing water resources for various uses. One of the main requirements for meeting the world's food needs is access to sustainable agriculture and its development. In order to achieve such a goal, it should be avoided by adopting appropriate measures to prevent damage to the system of limited resources in each region. Groundwater is always a source of water supplies as a source of fresh water. Underground water management in the first stage requires the identification and operation of aquifers in natural conditions. In most hydrological issues and underground water resources studies, the availability of groundwater resources data and statistics is of great importance. In order to assess the effects of development in existing conditions and provide management methods for groundwater resources, both quantitatively and qualitatively, mathematical and computer simulation of these resources is a powerful tool in optimal utilization of these resources. Maerials and methods: Numerical modeling of groundwater in aquifers is an important tool for the management of water resources. This model can be used to estimate the hydraulic parameters as well as water resources management. The aim of this study was to simulate the fluctuations in groundwater levels in the aquifer by GMS software Zanjan located at a latitude of 47 degrees 50 minutes 49 ° East and latitude 36 degrees 20 minutes north and 37 degrees is located. This model is a combination of code Modflow and GIS using the finite difference method, the underground water level simulation. For this research the information needed to run the model include: Positions border aquifers, at different heights above ground level bed rock aquifer, the amount of withdrawals from wells tapping position, the position and the water level in observation wells, hydraulic conductivity layer and nutrition, were introduced to the model. After preparation of the conceptual model and the determination of initial and boundary conditions, the simulation will be discussed. The flow simulation model was calibrated in a steady flow. Results and discution: The results of the calibration results in a steady state a reasonable balance between the observed and calculated in October 2002 showed. After calibration nutritional value 0/000321 meters per day was that this amount is 1/98 times the amount of power input to the model. The amount of horizontal hydraulic conductivity layers of sand, silt and sand, gravel 28.57 and 40.4 respectively km per day and then unsteady flow model calibration mode for distances between 2002 and 2007, using trial and error to achieve the best balance between the level observed daily and were calculated and projected groundwater levels drop, based on the implementation of the model for 2007 and 2015 and 2022 was performed. The root mean square error, mean absolute error and mean error, respectively, 17/41, 15/22 and -0.6, respectively. After calibration nutritional value 0.000321 meters per day was that this amount is 1/98 times the amount of input supply model. The amount of horizontal hydraulic conductivity layers of sand, silt and sand, gravel 28/57 and 40/4 respectively km per day and then unsteady flow model calibration mode for distances between 2002 and 2007, using trial and error to achieve the best result among the observed daily balance and balance was calculated. The root mean square error and average error with respect to the amount equal to 26/29 and -8/43 showed good accuracy model. The water level calculated for the converter, groundwater flow is from the southeast to the northwest of the current slope correspond area. Conclusion: The model was implemented with the assumption of the rate of groundwater abatement and constant nutrition for a 15-year interval from 2007 to 2022 Maps obtained for the groundwater level in the years reflects a sharp drop in the water level continued indiscriminate harvesting of underground water aquifer Zanjan.
Mehdi Panahi; Seyyed Hassan Mirhashemi
Volume 13, Issue 4 , January 2016, , Pages 53-58
Abstract
Due to recent droughts and global warming, in the modeling and prediction of climatic parameters it seems inevitable that the approach of using data mining algorithms to predict climatic elements has been widely used .Therefore, in this study the use of the CARD and CHAID data mining algorithms to predict ...
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Due to recent droughts and global warming, in the modeling and prediction of climatic parameters it seems inevitable that the approach of using data mining algorithms to predict climatic elements has been widely used .Therefore, in this study the use of the CARD and CHAID data mining algorithms to predict the air temperature at Arak synoptic stations was evaluated. The data provided are the average monthly data from the Arak weather station, including: “sunshine hours”, “dew point”, “relative humidity”, “average wind speed” and “saturation vapor pressure deficit” over a forty-six-year period from 1960 to 2005. The output variable used was “average temperature months later” on a monthly basis. After introducing the weather data as a monthly mean to the algorithm as an input variable of air temperature months later. Then to the CART and CHAID algorithms were evaluated using correlation coefficient (R2) and mean absolute error (MAE). According to the two statistical indices, the CHIAD Tree model has a better function by R2 =0.915 and MAE= 2.77 in forecasting the monthly average temperature for the months later.
