estimation of groundwater level using a hybrid genetic algorithm-neural network

in this paper, we present an application of evolved neural networks using a real coded genetic algorithm for simulations of monthly groundwater levels in a coastal aquifer located in the shabestar plain, iran. after initializing the model with groundwater elevations observed at a given time, the developed hybrid genetic algorithm-back propagation (ga-bp) should be able to reproduce groundwater level variations using the external input variables, including rainfall, average discharge, temperature, evaporation and annual time series. to achieve this purpose, the hybrid ga-bp algorithm is first calibrated on a training dataset to perform monthly predictions of future groundwater levels using past observed groundwater levels and additional inputs. simulations are then produced on another data set by iteratively feeding back the predicted groundwater levels, along with real external data. this modelling algorithm has been compared with the individual back propagation model (ann-bp), which demonstrates the capability of the hybrid ga-bp model. the later provides better results in estimation of groundwater levels compared to the individual one. the study suggests that such a network can be used as a viable alternative to physical-based models in order to simulate the responses of the aquifer under plausible future scenarios, or to reconstruct long periods of missing observations provided past data for the influencing variables is available.