Paper Information

Journal:   IRRIGATION SCIENCES AND ENGINEERING (JISE) (SCIENTIFIC JOURNAL OF AGRICULTURE)   SUMMER 2017 , Volume 40 , Number 2 #A0038; Page(s) 27 To 37.
 
Paper: 

AN APPLICATION OF COMBINED GEOSTATISTICS WITH OPTIMIZED ARTIFICIAL NEURAL NETWORK BY GENETIC ALGORITHM IN ESTIMATION OF GROUNDWATER LEVEL (CASE STUDY: DEZFUL AND ZEIDOON PLAINS)

 
 
Author(s):  ZAMANI AHMAD MAHMOODI R., AKHOND ALI A.M.*, ZAREI H.
 
* DEPARTMENT OF HYDROLOGY AND WATER RESOURCES, SHAHID CHAMRAN UNIVERSITY OF AHVAZ, IRAN
 
Abstract: 

Since the withdrawal of the observation wells at the plains done for the point, it is necessary to calculate the average groundwater level and also generalization the estimated water level from collected point to the surface of plain. The aim of this study is an investigation on the application of combined geostatistics method with optimized artificial neural networks by genetic algorithm in interpolation of groundwater level over Dezful and Zeidoon plains located in the Khozestan province. The obtained results from Cokriging, Kriging and IDW methods indicated that Cokriging with the Gaussian variogram and cross-variogram in Dezful Plain, and Kriging with the Gaussian variogram in Zeidoon plain are the best geostatistical methods for estimation the groundwater level and combined with artificial neural networks. Also the results of combination these two models showed that optimized model by genetic algorithm have better evaluation criteria than geostatistical methods and proposed as an effective combined model for estimation of the groundwater level. So that an application of this optimized combined method in Zeidoon plain with fewer observation wells was better than Dezful plain.

 
Keyword(s): GROUNDWATER, GENETIC ALGORITHM, GEOSTATISTICS, ARTIFICIAL NEURAL NETWORK
 
References: 
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