Yaser Bageri; Esfandiar Abbas Novinpour; A Nadiri; Keiwan Naderi
Abstract
Most of the country's geographically area is located in dry and semi-dry zone with low rainfall. The growing population, the limitation of water resources and the prevalence of groundwater resources in most parts of the country requirement to accurate prediction of the amount of these resources due to ...
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Most of the country's geographically area is located in dry and semi-dry zone with low rainfall. The growing population, the limitation of water resources and the prevalence of groundwater resources in most parts of the country requirement to accurate prediction of the amount of these resources due to the importance of these resources in optimal planning and management. In this research, in order to estimate the fluctuations of groundwater level in the Baruq aquifer, the artificial intelligence models including fuzzy, support vector machine and neural network models were used by the data of depth from 7 piezometers with long-term data of 14 years, as well as changes in temperature and precipitation in this period. Despite the inherent abilities of each models in predicting groundwater level, the heterogeneity of the study area prevented the high efficiency of these models. Therefore, SOM-AI modeling combined the self-organized maps (SOM) classification method and each model that is increased the efficiency of each composite model in different parts of the aquifer by dividing the study area into homogeneous regions. The results showed that the proposed method can be an effective method in the modeling of heterogeneous and even multi-layered aquifers.
