The study of environmental and human factors affecting aquifer depth changes using tree algorithm

Abstract

In recent years, more attention has been paid to water resources due to the development of agriculture and proper planning for better management of aquifers. Considering the effect of different factors on aquifer depth changes, in this study, human and environmental factors affecting the depth of aquifer changes in Qazvin plain were used. The Classification And Regression Tree (CART) algorithm was adopted to investigate and predict changes in aquifer depth. According to the results, the highest probability of the aquifer drop observed in July, August and September was 86.5%, and the highest probability of uprising aquifer depth arisen in December, January, February and March was 71.2%. According to sensitivity analysis by CART algorithm, the most important human and environmental factors affecting the number of aquifer depth changes in Qazvin plain were groundwater withdrawal from the agricultural abstraction well and the air temperature, respectively. Therefore, predicting the amount of aquifer depth changes by CART model, planning and managing groundwater resources is possible.