Optimizing the Quantitative and Qualitative Groundwater Monitoring Network Using Statistical and MCDM Methods

Document Type : Research

Authors

1 Professor, Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran

2 Associate professor, Department of Electrical and Computer Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran and Associate professor, Deep Learning Research Group, University of Hormozgan, Bandar Abbas, Hormozgan, Iran

3 Ph.D., Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran

4 M.Sc., Hormozgan Regional Water Company, Bandar-Abbas, Hormozgan, Iran

10.22092/wmrj.2024.367105.1595

Abstract

Introduction and Goal
Groundwater plays a key role in supplying the requirements of the communities in various sectors such as drinking, industry and agriculture, etc. The important of this vital resource is even greater in arid and semi-arid regions that face decrease in precipitation. Because aquifers are considered as the main sources of water supply for communities in these areas. Also, due to their location, these resources in the coastal areas are impacting by climatic changes in the interaction of land and sea, and also, at the same time they are influencing by human activities. Therefore, understanding of the quantitative and qualitative characteristics of groundwater tables and their monitoring is necessary. Continuous monitoring of the groundwater network requires spending money, time, labor, and manpower, and optimizing the monitoring network can be a useful solution to reduce the aforementioned problems. Therefore, the research was conducted with the aim of optimization the groundwater monitoring network in the Gahkom Saadatabad plain, Hormozgan province, using statistical methods.
Materials and Methods
In this research, first, by reviewing sources, Ministry of Energy guidelines, and using the perspective of groundwater experts, 9 criteria (consisting of the mean ground water level, mean annual drop of ground water, hydraulic conductivity, density of exploitation wells, distance from the river, Geological formation, land-use, distance from the fault and distance from the spring) were selected. Then, using the Analytical Hierarchy (AHP) method, each of the criteria was weighted, and a map was prepared for each of them to carry out the location process. In this research, TOPSIS and WASPAS multi criteria decision making models were employed to implement the process of locating observed wells. Then, to evaluate the current monitoring network with location results, the Thyssen network for each aquifer was drawn. Also, the mean score of the location maps in each Thyssen polygon was obtained. For each well and each polygon, wells with the meam score of less than 0.3 were considered as unsuitable wells. Next, in order to ensure the accuracy of the selection of unsuitable wells, Pettitt’s homogeneity test and standard normality test were performed. In the next step, the principal component analysis (PCA) method was used to determine the relative importance of 40 wells in the region, and wells with low relative importance were eliminated. Then, the Kriging interpolation method was used to find out the standard error value due to the removal of the wells. Finally, clustering analysis was used to group wells with the similar characteristics.
Results and Discussion
The results of the AHP showed that among the 9 criteria used in the research, the density of exploitation wells with a weight = 0.217 and the distance from the fault with a weight = 0.031 were ranked as first and last ranks, respectively. Comparison of multi-criteria decision-making models for the location process also indicated the same accuracy of both TOPSIS and WASPAS models. Therefore, the mean map of the two models was prepared and used to carry out the next steps of the research. Based on the results of the evaluation of the current monitoring network with the results of location (optimal network), 3 wells were identified as unsuitable wells. Also, based on the P value of the Petit test and the standard normal test, it was determined that all three wells were correctly selected as Heterogeneous wells. Based on the findings obtained from the PCA method 6 wells (numbers 8, 9, 15, 10, 32, and 37), were included in the group of wells with negative relative importance, and this wells were removed. The size of the standard error increased by 14% after removing the unimportant wells, and there was no significant difference. Therefore, based on the results of this study, less important wells can be removed from the monitoring network. Also, 40 wells located in the Gahkom Saadatabad plain was classified into 5 clusters.
Conclusion and Suggestions
The main objective of this research was to optimize the groundwater monitoring network from a quantitative and qualitative perspective using statistical and MCDM models. The results revealed the high efficiency and accuracy of MCDM methods. Based on the results of the location map of observed wells generated by the mentioned models, the study area was classified into 5 groups from very low priority to very high priority. Therefore, watershed managers can use the findings of this study to develop a monitoring network and issue new permits to drill new wells in suitable and unsuitable locations. Also, considering the importance of water monitoring in coastal areas, it is recommended to use the mentioned methods for other plains of Hormozgan province. In order to compare different statistical, hydrological and data mining methods such as machine learning models, it is suggested to use them simultaneously in future studies. On the other hand, in future studies for construction of ground water monitoring network, it is also recommended to use other effective criteria such as economic analysis criteria and the cost of constructing the monitoring network, criteria of distance and proximity to the sea, and criteria of distance and proximity to communication routes.

Keywords

References

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History

  • Receive Date: 29 September 2024
  • Revise Date: 14 November 2024
  • Accept Date: 19 December 2024

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