The vulnerability assessment of Dehgolan aquifer ground water using drastic method and wavelet neural network

Authors

1 M. Sc., (graduated), Dept. of Geography, Tabriz University, Tabriz, Iran

2 Assist. Prof., Dept. of Geology, Urmia University, Urmia, Iran

3 Assist. Prof., Dept. of Water Engineering, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran

Abstract

The East part of Kurdistan province has got considerable potentials for saving ground water. However, the reservoir volume of Dehgolan aquifer has significantly decreased due to excessive consumption and low precipitation during past two decades. As a result, it has been classified as a forbidden plain. In this research, the transfer and spread of pollution in Dehgolan plain aquifer was investigated using the drastic method. Wavelet neural network and Chicken Swarming Algorithm model were used to compare the study method with other smart methods. Drastic method is one of Sensitivity map overlay methods which is produced with seven main parameters including ground water depth, recharge, topography, soil environment, unsaturated environment and hydraulic conduction. In smart methods, drastic parameters and index were respectively defined as model inputs and outputs. Accordingly, 7 raster layers with a scale of 1:25000 were produced in Arc GIS, and after ranking and weighing, the drastic index was obtained between 71 and 153. The results indicate that northern parts of the aquifer have moderate and high pollution potentials, so they need more protections. Based on evaluation criteria the obtained vulnerability maps using neural network model had a better performance than Chicken Swarming Algorithm so that R2 and RMSE were obtained as (R2=0.98 and RMSE= 0.82), (R2=0.8 and RMSE= 1.51), (R2=0.96 and RMSE= 0.69), (R2=0.92 and RMSE= 1. 2) for western, southern, eastern and central parts respectively. Moreover, R2 and RMSE were obtained by Chicken Swarming Algorithm as (R2=0.8 and RMSE= 4.51), (R2=0.88 and RMSE= 5.38), (R2=0.66 and RMSE= 4.31), (R2=0.84 and RMSE= 6. 01) for western, southern, eastern and central parts respectively. Based on nitrate distribution data, vulnerability index had better results in predicting polluted parts.

Keywords

References

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History

  • Receive Date: 07 November 2022
  • Revise Date: 13 April 2023
  • Accept Date: 19 April 2023

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