Negar Akbari; Masoud Nasri; Seyyedhassan Mirhashemi
Abstract
In this study, with using the data mining algorithm prediction, more efficient management of the aquifer of Tirvan and Karvan can be done. Seven different human and natural factors affecting aquifer depth changes were used in this manuscript. Initially, the predictions of three tree algorithms CART, ...
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In this study, with using the data mining algorithm prediction, more efficient management of the aquifer of Tirvan and Karvan can be done. Seven different human and natural factors affecting aquifer depth changes were used in this manuscript. Initially, the predictions of three tree algorithms CART, CHAID and MP5 in aquifer changes were evaluated using statistical indices. The CAHID algorithm performed better than the CART and MP5 algorithms with respect to the regression coefficient of 0.82 and absolute error mean of 0.12. The highest aquifer rise in December, January, February and March, when the amount of precipitation was between 0.08 to 0.72 million cubic meters and the air humidity percentage was more than 72% and also the highest aquifer drawdown in month August and September, when air temperature more than 25 centigrade and the volume of water discharged from agricultural wells were more than 1.32 million cubic meters, were predicted by the CHAID algorithm tree diagram. From natural factors, air temperature and human factors, the volume of water harvested from agricultural wells had the greatest impact on aquifer depth changes in the plain. The two factors of air humidity percentage and precipitation volume were the only factors that had a direct relationship with the aquifer depth elevation. The most influential factors in predicting the depth changes of the aquifer of Tirvan and Karvan were air temperature, volume of water harvested from agricultural wells, and Precipitation volume and other parameters, respectively.
