Paper Information

Journal:   IRAN-WATER RESOURCES RESEARCH   WINTER 2016 , Volume 11 , Number 3 (34); Page(s) 85 To 99.
 
Paper: 

SPATIO-TEMPORAL GROUNDWATER LEVEL PREDICTION USING HYBRID GENETIC-KRIGING MODEL (CASE STUDY: HADISHAHR PLAIN)

 
 
Author(s):  HABIBI M.H., NADIRI A.A.*, ASGHARI MOGHADDAM A.
 
* DEPARTMENT OF EARTH SCIENCE, FACULTY OF THE NATURAL RESOURCES, UNIVERSITY OF TABRIZ, TABRIZ, IRAN
 
Abstract: 

In recent decades, the application of intelligent evolutionary methods and hybrid models for forecasting groundwater spatiotemporal fluctuations were more focused by researchers. Genetic algorithm and Neuro-Fuzzy are new methods which are applicable in single and hybrid forms for forecasting in complex and nonlinear problems. In this research, aforementioned methods were applied to study the Hadishahr plain aquifer. The Hadishahr plain is located in the north of East Azerbaijan province and it is a part of Julfa–Duzal study area. This aquifer suffers from groundwater level declination due to groundwater withdrawal increase. To achieve practical ways for spatio-temporal groundwater level forecasting, the artificial intelligence methods such as neuro–fuzzy (NF), genetic programming (GP) and combination their best model with geostatistical methods were used.
Precipitation and evaporation in t0 time step and groundwater table in t0-1 time step were the inputs to the Neuro-Fuzzy and Genetic Programming. The results showed that the average RMSE of selective piezometers for genetic programming and neuro-fuzzy were 19 and 23 centimeter in the test step, respectively. Then, genetic programming was used to present a hybrid model in combination with the geostatistical model (kriging). Finally, the hybrid model-genetic - kriging? were applied to predict the spatiotemporal prediction of the groundwater level. The simulated results were extended to the whole plain and the area with no groundwater level monitoring network.

 
Keyword(s): GROUNDWATER LEVEL, HADISHAHR PLAIN, NEURO-FUZZY, GENETIC PROGRAMMING, GEOSTATISTICS
 
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