Evaluation of Groundwater Vulnerability of Miandoab Plain to Nitrate Using Genetic Algorithm

Introduction:
Limitation of surface water resources and excessive water utilization from aquifers, as well as the pollutants intrusion through the agricultural, urban and industrial activities, cause irreparable damage to groundwater. Groundwater system doesn’t respond quickly to contaminants and contaminants reaching time to the groundwater and its release into the aquifer is usually long. Remediation of contaminated groundwater and re-use often takes a lot of time and money and sometimes finding an alternative source of water is not possible. Therefore, the best and most effective solution is preventing the contamination entry to this valuable source. Determining the groundwater contamination level is one of the most important hydrogeological studies, which in this regard, the identification of susceptible areas and aquifer vulnerability assessment has great importance. Determining groundwater contamination is one of the most important hydrogeological studies. In this regard, identification of the contamination risk areas the aquifer vulnerability assessment has great importance. The Miandoab study area as a most important plain of the Urmia lake basin is one of the agricultural areas in Iran, especially in grape production, therefore, because of overuse of various chemical and animal fertilizers, it can be a nitrate-contaminated plain. Therefore, considering the importance of groundwater in the Miandoab region, which is also used for drinking, the vulnerability assessment of this plain is necessary. In the present study, the study of contaminant risk areas using DRASTIC vulnerability method has been investigated and the vulnerability map has been optimized using a genetic algorithm.
2. Materials and Methods
The Miandoab Plain with an area of approximately 1150 Km2 is located in the south of Urmia Lake and is a part of the Alborz-Azarbayjan structural zone from the geological viewpoint. The average annual rainfall, based on the thirty years (1989-2018) data from Malekan and Miandoab synoptic stations is about 284 mm per year. This region, based on empirical Emberger method (1952) and using the statistics meteorology data, has a cold and semi-arid climate. Figure 1 shows the location of the study area.

Fig 1. Geographic location of the study area
The DRASTIC model has been used to mapping the groundwater vulnerability to pollution in many areas. Since this method is used in different places without any changes, it cannot consider the effects of pollution type and characteristics. Therefore, the method needs to be calibrated and corrected for specific aquifer and pollution. The DRASTIC model was improved with several methods such as the artificial neural network. In this study, the genetic algorithm is proposed for groundwater vulnerability.
Results and discussion:
The DRASTIC map was created by applying the weights for each parameter and integrating and overlaying the layers. According to the results of the DRASTIC model for plain, 15, 10, 17, 33 and 25 percent of the plain, respectively located in areas with very low, moderate, high and very high vulnerability. In the genetic algorithm method, the optimal weights of the parameters were obtained by maximizing the objective function. Based on the genetic algorithm method, groundwater depth, hydraulic conductivity, and unsaturated medium have the most effect on the vulnerability of groundwater in the region, respectively. Fig 2 shows the vulnerability map of Miandoab plain based on DRASTIC and optimized DRASTIC. The optimized map using the genetic algorithm method shows that about 18, 11, 28, 26 and 17 percent of the plains are located in very low, moderate, high and very vulnerable areas. According to the results of the model, the central parts of the Miandoab plain have been introduced as more vulnerable areas.

A)

B)
Fig 2. Vulnerability map using: A) General DRASTIC, B) optimized DRASTIC using genetic algorithm

Conclusion:
In the present study, the study of contaminating risk areas using DRASTIC vulnerability method has been investigated and the vulnerability map has been optimized using a genetic algorithm. The optimized map using the genetic algorithm method shows that about 18, 11, 28, 26 and 17 percent of the plains are located in very low, moderate, high and very vulnerable areas. According to the results of the model, the central parts of the Miandoab plain have been introduced as more vulnerable areas. Based on the results of correlation index (CI), optimized DRASTIC using genetic algorithm has the highest priority in identifying areas at high contaminate risk due to having the highest correlation coefficient (CI) with nitrate. In general, identifying the susceptible areas to contamination using appropriate methods, sources and contributing factors can be used for proper management and monitoring of groundwater.

Conclusion:
In the present study, the study of contaminating risk areas using DRASTIC vulnerability method has been investigated and the vulnerability map has been optimized using a genetic algorithm. The optimized map using the genetic algorithm method shows that about 18, 11, 28, 26 and 17 percent of the plains are located in very low, moderate, high and very vulnerable areas. According to the results of the model, the central parts of the Miandoab plain have been introduced as more vulnerable areas. Based on the results of correlation index (CI), optimized DRASTIC using genetic algorithm has the highest priority in identifying areas at high contaminate risk due to having the highest correlation coefficient (CI) with nitrate. In general, identifying the susceptible areas to contamination using appropriate methods, sources and contributing factors can be used for proper management and monitoring of groundwater.
Conclusion:
In the present study, the study of contaminating risk areas using DRASTIC vulnerability method has been investigated and the vulnerability map has been optimized using a genetic algorithm. The optimized map using the genetic algorithm method shows that about 18, 11, 28, 26 and 17 percent of the plains are located in very low, moderate, high and very vulnerable areas. According to the results of the model, the central parts of the Miandoab plain have been introduced as more vulnerable areas. Based on the results of correlation index (CI), optimized DRASTIC using genetic algorithm has the highest priority in identifying areas at high contaminate risk due to having the highest correlation coefficient (CI) with nitrate. In general, identifying the susceptible areas to contamination using appropriate methods, sources and contributing factors can be used for proper management and monitoring of groundwater.