Climatology
Ataollah Nadiri; Keyvan Naderi; Asghar Asghari Moghaddam; Mohammad Hasan Habibi
Abstract
No permanent surface water resources in many parts of the country resulted in overdraft of limited underground water resources. Duzduzan plain is one of the UromiaLake sub basins. In this area, indiscriminate harvesting of groundwater resources has caused an average decline of 76 centimeters per year. ...
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No permanent surface water resources in many parts of the country resulted in overdraft of limited underground water resources. Duzduzan plain is one of the UromiaLake sub basins. In this area, indiscriminate harvesting of groundwater resources has caused an average decline of 76 centimeters per year. The purpose of this research is Groundwater level spatiotemporal predicting using Artificial intelligence models and Geostatistics model. To predict the groundwater level in the duzduzan plain, initially the piezometera in the plain were classified. The groundwater level in each piezometers category were introduced as output for each of AI models and input of these models include a evaporation and a precipitation and grounwater level of the considered piezometers with one time delay (t0-1), respectively. Ann's model and Sugeno fuzzy (SF) model applied to predict groundwater level. The resulted values of Groundwater level were evaluated by statistical measures, includes root mean square error and correlation coefficient. The obtained results showed ANNs model has better performance. Then the result of ANNs model, including two year monthly groundwater level prediction data in selected piezometers, were used as inputs of geostatistics model (Kriging and Co Kriging) for predating spatially ground water level in the study area. Obtained results showed Co Kriging model has better performance.
Swywd Hossein Mirmousavi; Mina Mirain
Volume 16, Issue 38 , February 2012, , Pages 153-178
Abstract
Ggiven that assessment data often point to be made, are necessary to generalize to the entire region, Interpolation operation have been done on areas of precipitation. In this study using Kriging and inverse weight method, interpolation of rainfall in KermanProvince has been attempted. For this purpose, ...
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Ggiven that assessment data often point to be made, are necessary to generalize to the entire region, Interpolation operation have been done on areas of precipitation. In this study using Kriging and inverse weight method, interpolation of rainfall in KermanProvince has been attempted. For this purpose, the monthly rainfall statistics for 9 synoptic stations in Kerman province and 11 synoptic stations neighboring provinces have been used.
The results of this study indicate that Kriging method with lower error levels is more appropriate for the interpolation of rainfall in this region. Models based on fitted Semivariogram models, Spherical, linear and exponential models provide better facilities for the preparation of a precipitation isomap. Between models in the spherical model for the months January to June and also in December, the exponential model for the month of July and the exponential model for the months August to November show the most appropriate change model views that are detected. Based on maps prepared for different months, while the highest rainfall occurred in winter time change the amount of the highest range 42-13 mm in the season. Spatial gradients of changes in precipitation decrease trend are from south to north. Other seasons in the low average range of precipitation changes also showed no significant fluctuations.
