نوع مقاله : پژوهشی
نویسندگان
1 دانشگاه هرمزگان
2 گروه منابع طبیعی، دانشگاه هرمزگان، دانشکده کشاورزی و منابع طبیعی
3 گروه تحقیقات اب منطقه ای هرمزگان
4 گروه تحقیقات آب منطقه ای هرمزگان
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
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. This resources are very important in arid and semi-arid regions that are facing with a remarkable decrease in rainfall, 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 the quantitative and qualitative characteristics of groundwater tables and their monitoring is necessary. Continuous monitoring of the groundwater network rare expensive and time consuming. Therefore, optimizing the monitoring network can be useful help for the mitigation of the challenges mentioned in above. Therefore, the main aim of the current research is optimization of the groundwater monitoring network in the Gahkom Saadatabad plain, Hormozgan province, using statistical methods.
Materials and Methods
In the first step of research, we selected nine criteria (consisting of the average underground water level, average annual drop of underground water, hydraulic conductivity, density of exploitation wells, distance from the river, Geological formation, land use, distance from the fault and distance from the spring) according to guidelines of the Ministry of Energy, and as well as using the opinions of groundwater experts. including: The weight of the each of the criteria determined using the Analytical Hierarchy (AHP) method, and then a spatial map prepared for each criteria. In this research, TOPSIS and WASPAS multi criteria decision making models were employed to implement the process of locating observed wells. To evaluate the current monitoring network, the location results of the Thyssen network were drawn for each aquifer, and then the average score of the location maps in each Thyssen polygon was obtained. For each well and each polygon, wells with an average score of less than 0.3 were considered as unsuitable wells. Next, in order to ensure the correctness of the selection of unsuitable wells, Petit's homogeneity test and standard normality test were employeds. In the next step, the principal component analysis (PCA) method was used to determine the relative importance of 40 wells. After identifying the wells with low relative importance, they were excluded from further analysis, and 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. Both of MCDM models (e.g., TOPSIS and WASPAS) provided the same accuracy. Overall, the comparison between the current monitoring network and optimal network, revealed that 3 wells was identified as unsuitable wells, and according to the P value of the Petit test and the standard normal test, it was determined that the selection of all three wells as wells Heterogeneous has been correct. The results of the PCA method revealed that six wells consising ofwells 8, 9, 15, 10, 32 and 37, were included in the group of wells with relative negative importance, and the mentioned wells were removed. Further, the value of the standard error after removing the less important wells showed an increase of only 14%, which did not make a noticeable difference. Therefore, less important wells can be removed from the network. Finally, 40 wells located in the Gahkom Saadatabad plain was classified into 5 clusters.
Conclusion and Suggestions
The main contribution of the current research is optimizing the groundwater monitoring network quantitatively and qualitatively by statistical and MCDM models. The results revealed that MCDM methods has high performance and accuracy and provided valuable results. 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, findings of this research can be use by watershed managers to develop monitoring network and issue new licenses to drill new wells in suitable and inappropriate places. Therefore, considering the importance of water monitoring in coastal areas, it is recommended to use the mentioned methods for other plains of Hormozgan province. Also, 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. Also, in future studies for the construction of underground water monitoring network, it is 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.
کلیدواژهها [English]
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