Performance examination of optimization model of groundwater monitoring network based on Gray wolf and Neural network (GNM) (Case study: Birjand plain)

Document Type : Original Article

Authors

Assistant Professor of Water Engineering, University of Birjand, Iran

Abstract

Quantity monitoring of groundwater with assessment and detection of main origins of the aquifer regime, has important role in management of groundwater in any region. Hence monitoring network of groundwater is importance for spacial and temporal variations of water table. This study proposes a new method named Gray wolf and Neural network Monitoring (GNM) to quantity monitoring of Birjand plain and detection of optimal location of piezometers.  In this proposed method used of neural network and Gray Wolf Algorithm (GWA) to estimate of water table and placement of piezometer location respectively. Water table with one to three month delay lag, topographic elevation, aquifer discharge and coordinate were considered as input of water table estimator (Neural Network) of GNM. The observation values of input components were interpolated on Birjand aquifer using of Geostatistical Analysis in GIS.  Also R2 and RMSE indices were used to skill evaluation of Neural Network. Exponential error between observed and simulated water table was assumed as fitness function in placement of piezometer location. Also used of Polytope algorithm to rise of accuracy and exploration of new optimal points. Results indicated that estimator of groundwater table of GNM has a noticeable performance. The value of R2 and RMSE indices in validation section achieved 0.1 and 0.9 m respectively. Also examine of comparison results of observation and simulation groundwater table showed that GNM has well skill in placement of piezometers location. So that the value of fitness function was decreased to 0.0007 m. The location of 10 new piezometers ultimately was recognized using of GNM. Also results of Polytope algorithm application indicated that this method can to have high capable to new optimal pints. So that using of this method could to reduce fitness function to 0.0001 m. Estimation skill of groundwater table in proposed piezometer network by GNM examined with comparison of observation and simulated groundwater table between 1390 to 1392. The values of R2 and RMSE indices were evaluated for any piezometer of new piezometer network. Results showed that proposed piezometer network has noticeable accuracy in estimation of water table.

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