Monitoring groundwater level network of Dezful-Andimeshk plain

Document Type : Case-study Article

Authors

1 Ph.D./ Department of Soil and Water Research, Chaharmahal and Bakhtiari Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shahrekord, Iran

2 Assistant Professor/ Department of Electrical Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

3 Assistant Professor/ Department of Computer Engineering, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran

Abstract

Introduction
Preservation and proper management of water resources are one of the essential fields of study in the world. In arid and semi-arid regions like Iran, quantitative and qualitative management of underground water resources is particularly important. In most hydrological issues and groundwater resources studies, groundwater statistics and information availability are critical. To collect information without side effects, comprehensive and sufficient data collection with the help of a groundwater monitoring network is very important. In line with the sustainable management of renewable water resources, the need for a network of underground water observation (monitoring) wells to accurately measure the water level is necessary and necessary. Considering the complexities of the underground water environment and the high costs of conventional monitoring methods, inventing new technologies and using advanced methods in this matter will significantly help improve the underground water systems. One of the parameters of particular importance in monitoring groundwater quantity is the groundwater level. Therefore, this parameter should be measured or estimated as accurately as possible. In recent decades, the use of computer and calculation models to monitor the level of underground water has developed significantly. Considering the importance of underground water resources and network monitoring, to save time and money, in this research, principal component analysis and Shannon's entropy theory were used to monitor the underground water network of the Dezful-Andimeshk Plain.
 
Materials and Methods
This research used monthly groundwater level information from 77 observation wells in the Dezful-Andimeshk Plain during 2018-2019. Groundwater level information is collected twice a month. Principal component analysis and Shannon entropy methods were used for monitoring. In the current research, the number of statistical periods for each well is 24, less than the total number of observation wells. Twenty-four observation wells around it were used to monitor each well. In groundwater level monitoring, the relative importance of each well is defined by the ratio of the number of times that well is recognized as a compelling well to the number of times that well is included in the analysis of the main components. This ratio shows the importance of each well compared to other wells. Therefore, to save time and costs, less important wells can be removed in the monitoring of the underground water level. In 1948, Shannon showed that events with a high probability of occurrence show less information, and on the contrary, the lower the probability of an event, the more information it provides.  In this method, the weight of each well was obtained using Shannon's entropy theory. Any well that has a higher Shannon entropy weight contains more important and unpredictable information and should be preserved. On the contrary, a well that has a lower Shannon entropy weight can be removed from the network. Principal component analysis and Shannon's entropy method in the current research were done with the help of coding in Matlab software due to the high volume of calculations.
 
Results and Discussion
To rank the wells, the threshold limits are equal to zero, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9 and one considered. At threshold one, only wells that have a rank of one remain (wells that are recognized as effective wells in all analyses) and threshold zero includes all wells (effective and ineffective). According to the obtained results, increasing the error in the threshold zero to 0.7 is gradual, but in the thresholds 0.8, 0.9, and one, the error value increases with a high slope. So, the amount of error in the thresholds of 0.7, 0.8, 0.9, and 1 has been calculated as 12.2, 17.7, 25.3 and 34.2 respectively. Therefore, the threshold limit in the current research is considered to be 0.7. However, the number of wells effective in monitoring the underground water level is reduced from 77 to 32. Shannon's entropy weight values were also calculated for all wells. 11 wells have the highest value of Shannon's entropy weight, which shows that they contain the most information.
 
Conclusion
The general comparison of the results of the two methods showed that all 11 wells with the highest weight in the Shannon entropy method were also observed as effective wells in the principal component analysis method. By knowing the effective wells in the region, firstly, in the face of lack of time and money, it is possible to use known effective wells for monitoring secondly, by removing ineffective wells, there will be little change in the average level of underground water. It is not possible, or in other words, the tracking error does not increase significantly. Comparing the results of the two methods showed that the remaining wells in Shannon's entropy theory are among the wells identified in the principal component analysis method. Also, considering that the wells in the region were built by the Khuzestan Water and Electricity Organization considering the types of uses, removing the ineffective wells will not affect the process of using the information of the wells. It is recommended to use principal component analysis and Shannon entropy for groundwater quality monitoring in the study area. Additionally, it is suggested to monitor the quality of the underground water network in the study area using the methods used in future research.

Highlights

References

Ahmadi Siyavoshani, F. (2019). Determining the optimal number of river quality monitoring stations using discrete information transfer entropy. M.Sc. Thesis, Technical and Engineering Faculty of Qom University. [In Persian]

Anonymous. (2013). Groundwater quality monitoring guidelines. Publications of the Organization of Management and Planning of the country, Publication No. 620, Tehran. [In Persian]

Babaei Hesar, S., Hamdami, Gh., & Ghasemieh, H. (2017). Dentification of effective wells in determining the depth of underground water in Urmia plain using principal component analysis. Water and Soil Journal, 30(1), 40-50. doi: 10.22067/jsw.v31i1.48750. [In Persian]

Hooshangi, N., Ale Sheikh, A.A., & Nadiri A.A. (2016). Optimizing the number of piezometers in predicting the groundwater level with PCA and geostatistical methods. Knowledge Of Water And Soil, 25(4-2), 53-66. https://www.sid.ir/paper/147848/en [In Persian]

Hoseini Zadeh, A., Seyyed Kaboli, H., Zarei, H., & Akhound Ali, A.M. (2017). Analysis of drought severity and return period in future climate change conditions (case study: Dezful Andimeshk plain). Irrigation Science and Engineering, 39(1), 33-43. doi: 10.22055/jise.2016.12010. [In Persian]

Jolliffe, I.T. (2002). Principal Component Analysis. Springer series in statics. 488 pages.

Jung, H., Koh, D.C., Kim, Y., Ha, K., & Lee, J. (2016). Interpretation of Groundwater Level Variations in Jeju Island by Principal Component Analysis. 21st EGU General Assembly, EGU2019, Proceedings from the conference held 7-12 April, in Vienna, Austria.

Khan, S., Gabriel, H.F. & Rana, T. (2008). Standard precipitation index to track drought and assess impact of rainfall on water tables in irrigation areas. Irrigation Drainage Systems, 22, 159-177. doi: 10.1007/s10795-008-9049-3

Khashei Siuki, A., Shahidi, A., & Rahnama, S. (2021). Comparison of Birjand aquifer chromium monitoring network using principal component analysis (PCA) and entropy theory. Journal of Environment and Water Engineering, 7(2), 220–231. doi:10.22034/jewe.2020.254396.1448. [In Persian]

Li, S., Heng, S., Siev, S., Yoshimura, C., Oliver, C. Saavedra, V., & Sarann, L.y. (2019). Multivariate interpolation and information entropy for optimizing raingauge network in the Mekong River Basin. Hydrological Sciences Journal, 64(12), 1439-1452. doi: 10.1080/02626667.2019.1646426

Maryanaji, Z., & Ramezani, A. (2020). Examining the influence of factors affecting the flooding of Hamadan province using Shannon's entropy model and geographic information system. Hydrogeomorphology, 23(6), 185-207. doi: 10.22034/hyd.2020.11120. [In Persian]

Mondal, N., & Singh, V. (2012). Evaluation of groundwater monitoring network of Kodaganar River basin from Southern India using entropy. Environmental Earth Sciences, 66(4), 1183-1193. doi: 10.1007/s12665-011-1326-z

Nouri Gheydari, M.H. (2014). Determining the effective wells in determining the level of underground water by analyzing the main components. Journal of Water and Soil Sciences, 17(64), 158-149. dor: 20.1001.1.24763594.1392.17.64.5.5 [In Persian].

Parsamehr, A.H., Maleki Nezad, H. & Khosravani, Z. (2018). Investigation of Shannon's entropy theory in weighting the quality index (case study: Meqan plain). Iranian Water Research Journal, 12(2), 101-110. https://www.sid.ir/
paper/159784/en [In Persian]

Pearson, K. (1901). On lines and plans of closest fit to systems of points in Space. Philosophical Magazine, 2(6), 559-572. doi: 10.1080/14786440109462720

Petersen, W. (2001). Process identification by principal component analysis of river water-quality data. Ecological Modelling, 138(1-3), 193-213. doi: 10.1016/S0304-3800(00)00402-6

Rahnama, S., Khashei Siuki, A., Shahidi, A., & Noferesti, A.M. (2021). Designing a quality monitoring network of gonabad aquifer using principal component analysis (PCA) method. Water Harvesting Research, 4(1), 68-75. doi: 10.22077/jwhr.2021.4632.1044

Rajaee, T., Masoumi, F., & Ahmadi Siyavoshani, F. (2021). Optimal location of river system water quality monitoring stations using discrete information transfer entropy. Iranian Journal of Irrigation and Drainage, 2(15), 295-306. dor: 20.1001.1.20087942.1400.15.2.4.6. [In Persian]

Sayadi Shahraki, A., Naseri, A.A., Boroomand Nasab, S., & Soltani Mohammadi, A. (2021). Designing the underground water level monitoring network using principal component analysis technique. Journal of Water Resources Engineering, 13(1), 29-36. dor: 20.1001.1.20086377.1399.13.44.3.4. [In Persian]

Sauquet, E. (2000). Mapping mean monthly runoff pattern using EOF analysis. Hydrology and Earth System Sciences, 4(1), 79-93. doi: 10.5194/hess-4-79-2000

Shabbir, R. & Ahmad, S.S. (2015). Use of geographic information system and water quality index to assess groundwater quality in Rawalpindi and Islamabad. Arabian Journal for Science and Engineering, 40, 2033-2047. doi: 10.1007/s13369-015-1697-7

Shannon, C.E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(4), 623-656. doi: 10.1002/j.1538-7305.1948.tb01338.x

Shyu, G.S., Cheng, B.Y., Chiang, C.T., Yao, P.H., & Chang, T.K. (2011). Applying Factor analysis combined with kriging and information entropy theory for mapping and evaluating the stability of groundwater quality variation in Taiwan. International Journal of Environment Research and Public Health, 8, 1084-1109. doi: 10.3390/ijerph8041084

Stigter, T.Y., Ribeiro, L., & Dill, A.M.M. (2006). Evaluation of an intrinsic and a specific vulnerability assessment method in comparison with ground waters a linistio nand nitrate contamination levels in two agricultural regions in the south of Portugal. Hydrogeology Journal, 14(1-2), 79-99. doi: 10.1007/s10040-004-0396-3

Keywords

Main Subjects

References

References
Ahmadi Siyavoshani, F. (2019). Determining the optimal number of river quality monitoring stations using discrete information transfer entropy. M.Sc. Thesis, Technical and Engineering Faculty of Qom University. [In Persian]
Anonymous. (2013). Groundwater quality monitoring guidelines. Publications of the Organization of Management and Planning of the country, Publication No. 620, Tehran. [In Persian]
Babaei Hesar, S., Hamdami, Gh., & Ghasemieh, H. (2017). Dentification of effective wells in determining the depth of underground water in Urmia plain using principal component analysis. Water and Soil Journal, 30(1), 40-50. doi: 10.22067/jsw.v31i1.48750. [In Persian]
Hooshangi, N., Ale Sheikh, A.A., & Nadiri A.A. (2016). Optimizing the number of piezometers in predicting the groundwater level with PCA and geostatistical methods. Knowledge Of Water And Soil, 25(4-2), 53-66. https://www.sid.ir/paper/147848/en [In Persian]
Hoseini Zadeh, A., Seyyed Kaboli, H., Zarei, H., & Akhound Ali, A.M. (2017). Analysis of drought severity and return period in future climate change conditions (case study: Dezful Andimeshk plain). Irrigation Science and Engineering, 39(1), 33-43. doi: 10.22055/jise.2016.12010. [In Persian]
Jolliffe, I.T. (2002). Principal Component Analysis. Springer series in statics. 488 pages.
Jung, H., Koh, D.C., Kim, Y., Ha, K., & Lee, J. (2016). Interpretation of Groundwater Level Variations in Jeju Island by Principal Component Analysis. 21st EGU General Assembly, EGU2019, Proceedings from the conference held 7-12 April, in Vienna, Austria.
Khan, S., Gabriel, H.F. & Rana, T. (2008). Standard precipitation index to track drought and assess impact of rainfall on water tables in irrigation areas. Irrigation Drainage Systems, 22, 159-177. doi: 10.1007/s10795-008-9049-3
Khashei Siuki, A., Shahidi, A., & Rahnama, S. (2021). Comparison of Birjand aquifer chromium monitoring network using principal component analysis (PCA) and entropy theory. Journal of Environment and Water Engineering, 7(2), 220–231. doi:10.22034/jewe.2020.254396.1448. [In Persian]
Li, S., Heng, S., Siev, S., Yoshimura, C., Oliver, C. Saavedra, V., & Sarann, L.y. (2019). Multivariate interpolation and information entropy for optimizing raingauge network in the Mekong River Basin. Hydrological Sciences Journal, 64(12), 1439-1452. doi: 10.1080/02626667.2019.1646426
Maryanaji, Z., & Ramezani, A. (2020). Examining the influence of factors affecting the flooding of Hamadan province using Shannon's entropy model and geographic information system. Hydrogeomorphology, 23(6), 185-207. doi: 10.22034/hyd.2020.11120. [In Persian]
Mondal, N., & Singh, V. (2012). Evaluation of groundwater monitoring network of Kodaganar River basin from Southern India using entropy. Environmental Earth Sciences, 66(4), 1183-1193. doi: 10.1007/s12665-011-1326-z
Nouri Gheydari, M.H. (2014). Determining the effective wells in determining the level of underground water by analyzing the main components. Journal of Water and Soil Sciences, 17(64), 158-149. dor: 20.1001.1.24763594.1392.17.64.5.5 [In Persian].
Parsamehr, A.H., Maleki Nezad, H. & Khosravani, Z. (2018). Investigation of Shannon's entropy theory in weighting the quality index (case study: Meqan plain). Iranian Water Research Journal, 12(2), 101-110. https://www.sid.ir/
paper/159784/en [In Persian]
Pearson, K. (1901). On lines and plans of closest fit to systems of points in Space. Philosophical Magazine, 2(6), 559-572. doi: 10.1080/14786440109462720
Petersen, W. (2001). Process identification by principal component analysis of river water-quality data. Ecological Modelling, 138(1-3), 193-213. doi: 10.1016/S0304-3800(00)00402-6
Rahnama, S., Khashei Siuki, A., Shahidi, A., & Noferesti, A.M. (2021). Designing a quality monitoring network of gonabad aquifer using principal component analysis (PCA) method. Water Harvesting Research, 4(1), 68-75. doi: 10.22077/jwhr.2021.4632.1044
Rajaee, T., Masoumi, F., & Ahmadi Siyavoshani, F. (2021). Optimal location of river system water quality monitoring stations using discrete information transfer entropy. Iranian Journal of Irrigation and Drainage, 2(15), 295-306. dor: 20.1001.1.20087942.1400.15.2.4.6. [In Persian]
Sayadi Shahraki, A., Naseri, A.A., Boroomand Nasab, S., & Soltani Mohammadi, A. (2021). Designing the underground water level monitoring network using principal component analysis technique. Journal of Water Resources Engineering, 13(1), 29-36. dor: 20.1001.1.20086377.1399.13.44.3.4. [In Persian]
Sauquet, E. (2000). Mapping mean monthly runoff pattern using EOF analysis. Hydrology and Earth System Sciences, 4(1), 79-93. doi: 10.5194/hess-4-79-2000
Shabbir, R. & Ahmad, S.S. (2015). Use of geographic information system and water quality index to assess groundwater quality in Rawalpindi and Islamabad. Arabian Journal for Science and Engineering, 40, 2033-2047. doi: 10.1007/s13369-015-1697-7
Shannon, C.E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(4), 623-656. doi: 10.1002/j.1538-7305.1948.tb01338.x
Shyu, G.S., Cheng, B.Y., Chiang, C.T., Yao, P.H., & Chang, T.K. (2011). Applying Factor analysis combined with kriging and information entropy theory for mapping and evaluating the stability of groundwater quality variation in Taiwan. International Journal of Environment Research and Public Health, 8, 1084-1109. doi: 10.3390/ijerph8041084
Stigter, T.Y., Ribeiro, L., & Dill, A.M.M. (2006). Evaluation of an intrinsic and a specific vulnerability assessment method in comparison with ground waters a linistio nand nitrate contamination levels in two agricultural regions in the south of Portugal. Hydrogeology Journal, 14(1-2), 79-99. doi: 10.1007/s10040-004-0396-3
Water and Soil Management and Modelling
Volume 4, Issue 1
January 2024
Pages 326-337

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History

  • Receive Date: 25 February 2023
  • Revise Date: 28 March 2023
  • Accept Date: 29 March 2023

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