| Autor: |
مهتاب بادکوبه هز, محسن نجارچی, محمدرضا جلالی, حسین مظاهری, سعید شعبانلو |
| Zdroj: |
Iranian Journal of Soil & Water Researches (IJSWR); Jul2024, Vol. 55 Issue 4, p569-586, 18p |
| Abstrakt: |
The aim of this research is to predict fluctuations in the equivalent thickness of groundwater using GRACE satellite data and modeling it using artificial intelligence hybrid models. The study area of this research is the basin area of Lake Urmia located in the northwest of Iran. For this purpose, 180 GRACE satellite data between April 2002 and March 2017 were used. The output of GRACE satellites includes 6 pixels located on the selected watershed, of which 2 points that overlapped the most with the watershed area were selected for modeling with artificial intelligence tools. The GA-ANN, ICA-ANN and PSO-ANN hybrid models were used for this purpose. The results showed that the output of the ICA-ANN model had the best fit with the observation data with a correlation coefficient equal to 0.915 and 0.942 in the two selected pixels 2 and 5 in the test phase, and the results of this model had the best and closest distribution of points. Considering the importance of knowing the changes in the equivalent thickness of groundwater as one of the most important parameters of the water budget, the artificial intelligence models used in this research can be recommended, especially for areas without basic statistics or in situations where it is not possible to use mathematical models. Without the need for complex relationships and equations to investigate the effect of surface and groundwater interaction and only based on satellite data, the equivalent thickness of groundwater can be predicted in the studied plain in dry and wet periods with great accuracy. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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