Volume 12 Issue 4
Jul.  2021
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Saman Javadi, Masoud Saatsaz, S. Mehdy Hashemy Shahdany, Aminreza Neshat, Sami Ghordoyee Milan, Sara Akbari. A new hybrid framework of site selection for groundwater recharge[J]. Geoscience Frontiers, 2021, 12(4): 101144. doi: 10.1016/j.gsf.2021.101144
Citation: Saman Javadi, Masoud Saatsaz, S. Mehdy Hashemy Shahdany, Aminreza Neshat, Sami Ghordoyee Milan, Sara Akbari. A new hybrid framework of site selection for groundwater recharge[J]. Geoscience Frontiers, 2021, 12(4): 101144. doi: 10.1016/j.gsf.2021.101144

A new hybrid framework of site selection for groundwater recharge

doi: 10.1016/j.gsf.2021.101144

The cooperation by the Regional Water Company of Yasouj in collecting statistics and information is hereby sincerely appreciated.

  • Received Date: 2020-05-04
  • Rev Recd Date: 2021-01-03
  • Since incorrect site selection has sometimes led to the failure of artificial recharge projects, it is necessary to increase the effectiveness of such projects and minimize their failure by employing new techniques. Therefore, the present research used a combination of decision-making models, numerical groundwater modeling and clustering technique to determine suitable sites for implementation of an artificial recharge project. This hybrid approach was employed for the Yasouj aquifer located in southwestern Iran. In the first stage, by employing an AHP decision-making model, hydraulic conductivity, specific yield, slope, land use, depth to groundwater, and aquifer thickness were selected from 21 criteria used in previous research. The selected criteria were then entered as input into the classical k-means clustering model. Using the output, aquifer was divided into seven different regions or clusters. These clusters were then matched with the land use map, and some of the abandoned land areas were selected as the final option for implementing the artificial recharge project. Finally, the MODFLOW code in the GMS software was used to simulate the groundwater level and cluster the sites selected, with regards to increase in groundwater level. Results indicated that the most significant increases in groundwater level (43 and 27 cm) were those of Clusters 2 and 6 in the northern and western parts of the aquifer, respectively. Therefore, this approach can be used in other similar aquifers in arid and semi-arid regions to select the best sites for artificial recharge and to prevent loss of floodwaters.

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  • [1]
    Al-Ismaily, S.S., Al-Maktoumi, A.K., Kacimov, A.R., Al-Saqri, S.M., Al-Busaidi, H.A., 2013. Impact of a recharge dam on the hydropedology of arid zone soils in Oman:anthropogenic formation factor. J. Hydrol. Eng. 20 (4), 04014053. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000886.
    Amineh, Z.B.A., Hashemian, S.J.A.D., Magholi, A., 2017. Integrating Spatial Multi Criteria Decision Making (SMCDM) with Geographic Information Systems (GIS) for delineation of the most suitable areas for aquifer storage and recovery (ASR). J. Hydrol. 551, 577-595.
    Bhuiyan, C., 2015. An approach towards site selection for water banking in unconfined aquifers through artificial recharge. J. Hydrol. 523, 465-474. https://doi.org/10.1016/j.jhydrol.2015.01.052.
    Bordbar, M., Neshat, A., Javadi, S., 2019. Modification of the GALDIT framework using statistical and entropy models to assess coastal aquifer vulnerability. Hydrol. Sci. J. 64(9), 1117-1128.
    Chenini, I., Mammou, A.B., El May, M., 2010. Groundwater recharge zone mapping using GIS-based multi-criteria analysis:a case study in Central Tunisia (Maknassy Basin). Water Resour. Manag. 24 (5), 921-939. https://doi.org/10.1007/s11269-009-9479-1.
    Davies, D.L., Bouldin, D.W., 1979. A cluster separation measure. IEEE Trans. Pattern Anal.Mach. Intell. 2 (1), 224-227. https://doi.org/10.1109/TPAMI.1979.4766909.
    Doan, C.D., Liong, S.Y., Dulakshi, S., Karunasinghe, K., 2005. Derivation of effective and efficient data set with subtractive clustering method and genetic algorithm. J. Hydroinf. 7 (4), 219-233. https://doi.org/10.2166/hydro.2005.0020.
    Donovan, D.J., Katzer, T., Brothers, K., Cole, E., Johnson, M., 2002. Costbenefit analysis of artificial recharge in Las Vegas Valley, Nevada. J. Water Resour. Plann. Manage. 128 (5), 356-365. https://doi.org/10.1061/(ASCE)0733-9496(2002)128:5(356).
    Gambolati, G., Teatini, P., 2015. Geomechanics of subsurface water withdrawal and injection. Water Resour. Res. 51 (6), 3922-3955. https://doi.org/10.1002/2014WR016841.
    Gesim, N.A., Okazaki, T., 2018. Identification of groundwater artificial recharge sites in Herat city, Afghanistan, using Fuzzy logic. Intern. J. Eng. Tech. Res. 8 (2), 40-45.
    Han, J., Kamber, M., 2006. Data Mining Concepts and Techniques. Third ed. Morgan Kaufman Publisher, U.S.A, San Francisco, p. 110.
    Han, Z., 2003. Groundwater resources protection and aquifer recovery in China. Environ.Geol. 44 (1), 106-111. https://doi.org/10.1007/s00254-002-0705-x.
    Harbaugh, A.W., Banta, E.R., Hill, M.C., Mcdonald, M.G., 2000. Modflow-2000, the U.S. Geological Survey Modular Ground-water Model-User Guide to Modularization Concepts and the Ground-water Flow Process. Open-file Report. U.S. Geological survey, Reston, Virginia, p. 134.
    Hashemy, S.M., Monem, M.J., 2012. Facilitation of operation and maintenance activities of irrigation networks using ak-means clustering method:case study of the ghazvin irrigation network. Irrig. Drain. 61 (1), 31-38. https://doi.org/10.1002/ird.617.
    Hayashi, T., Tokunaga, T., Aichi, M., Shimada, J., Taniguchi, M., 2009. Effects of human activities and urbanization on groundwater environments:an example from the aquifer system of Tokyo and the surrounding area. Sci. Total Environ. 407 (9), 3165-3172.https://doi.org/10.1016/j.scitotenv.2008.07.012.
    Heil, J., Häring, V., Marschner, B., Stumpe, B., 2019. Advantages of fuzzy k-means over kmeans clustering in the classification of diffuse reflectance soil spectra:a case study with West African soils. Geoderma 337, 11-21. https://doi.org/10.1016/j.geoderma.2018.09.004.
    Jafari, F., Javadi, S., Golmohammadi, G., Mohammadi, K., Khodadadi, A., Mohamazade, M., 2016. Groundwater risk mapping prediction using mathematical modeling and the Monte Carlo technique. Environ. Earth Sci. 75 (6), 491. https://doi.org/10.1007/s12665-016-5335-9.
    Javadi, S., Hashemy, S.M., Mohammadi, K., Howard, K.W.F., Neshat, A., 2017. Classification of aquifer vulnerability using K-means cluster analysis. J. Hydrol. 549, 27-37. https://doi.org/10.1016/j.jhydrol.2017.03.060.
    Kanungo, D.P., Nayak, J., Naik, B., Behera, H.S., 2016. Hybrid clustering using elitist teaching learning-based optimization:an improved hybrid approach of TLBO. Intern.J. Rough Sets Data Anal. (IJRSDA) 3 (1), 1-19. https://doi.org/10.4018/IJRSDA.2016010101.
    Kavuri, M., Boddu, M., Annamdas, V.G.M., 2011. New methods of artificial recharge of aquifers:a review. Poster presented at the 4th International Perspective on Water Resources & the Environment (IPWE). National University of Singapore (NUS), Singapore, pp. 4-6.
    Kim, D.W., Lee, K.H., Lee, D., 2004. On cluster validity index for estimation of the optimal number of fuzzy clusters. Pattern Recogn. 37 (10), 2009-2025. https://doi.org/10.1016/j.patcog.2004.04.007.
    Lalehzari, R., Tabatabaei, S.H., 2015. Simulating the impact of subsurface dam construction on the change of nitrate distribution. Environ. Earth Sci. 74, 3241-3249. https://doi.org/10.1007/s12665-015-4362-2.
    Lalehzari, R., Tabatabaei, S.H., Kholghi, M., Yarali, N., Saba, A.A., 2014. Evaluation of scenarios in artificial recharge with treated wastewater on the quantity and quality of the Shahrekord Aquifer. Int. J. Environ. Stud. Intern. 40 (1), 52-55.
    Laspidou, C., Papageorgiou, E., Kokkinos, K., Sahu, S., Gupta, A., Tassiulas, L., 2015. Exploring patterns in water consumption by clustering. Procedia Eng. 119, 1439-1446.https://doi.org/10.1016/j.proeng.2015.08.1004.
    Li, T., Sun, G., Yang, C., Liang, K., Ma, S., Huang, L., 2018. Using self-organizing map for coastal water quality classification:Towards a better understanding of patterns and processes. Sci. Total Environ. 628, 1446-1459. https://doi.org/10.1016/j.scitotenv.2018.02.163.
    Lifshitz, R., Ostfeld, A., 2018. Clustering for analysis of water distribution systems. J. Water Resour. Plan. Manag. 144 (5), 04018016. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000917.
    Malekmohammadi, B., Mehrian, M.R., Jafari, H.R., 2012. Site selection for managed aquifer recharge using fuzzy rules:integrating geographical information system (GIS) tools and multi-criteria decision making. Hydrogeol. J. 20 (7), 1393-1405. https://doi.org/10.1007/s10040-012-0869-8.
    Manap, M., Sulaiman, W.N.A., Ramli, M.F., Pradhan, B., Surip, N., 2013. A knowledgedriven GIS modeling technique for groundwater potential mapping at the Upper Langat Basin, Malaysia. Arab. J. Geosci. 6 (5), 1621-1637.
    Monem, M.J., Hashemy, S.M., 2011. Extracting physical homogeneous regions out of irrigation networks using fuzzy clustering method:a case study for the Ghazvin canal irrigation network. J. Hydroinf. 13 (4), 652-660. https://doi.org/10.2166/hydro.2010.058.
    Mu, E., Pereyra-Rojas, M., 2017. Understanding the analytic hierarchy process. Practical Decision Making. SpringerBriefs in Operations Research. Springer, Cham, pp. 7-22 https://doi.org/10.1007/978-3-319-33861-3_2.
    Neshat, A., Pradhan, B., Dadras, M., 2014. Groundwater vulnerability assessment using an improved DRASTIC method in GIS. Resour. Conserv. Recycl. 86, 74-86. https://doi.org/10.1016/j.resconrec.2014.02.008.
    Olman, V., Mao, F., Wu, H., Xu, Y., 2009. Parallel clustering algorithm for large data sets with applications in bioinformatics. IEEE/ACM Trans. Comput. Biol. Bioinform. 6 (2), 344-352. https://doi.org/10.1109/TCBB.2007.70272.
    Prabhu, M.V., Venkateswaran, S., 2015. Delineation of artificial recharge zones using geospatial techniques in Sarabanga Sub Basin Cauvery River, Tamil Nadu. Aquat. Pr. 4, 1265-1274.
    Predescu, A., Negru, C., Mocanu, M., Lupu, C., 2018. Real-time clustering for priority evaluation in a water distribution system. 2018 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), pp. 1-6 https://doi.org/10.1109/AQTR.2018.8402760.
    Rai, S.P., Sharma, N., Lohani, A.K., 2019. Novel approach for issues identification in transboundary water management using fuzzy c-means clustering. Appl Water Sci 9 (1), 11. https://doi.org/10.1007/s13201-018-0889-1.
    Saaty, R.W., 1987. The analytic hierarchy process-what it is and how it is used. Math.Modell. 9 (3-5), 161-176. https://doi.org/10.1016/0270-0255(87)90473-8.
    Sallwey, J., Bonilla Valverde, J.P., Vásquez López, F., Junghanns, R., Stefan, C., 2018. Suitability maps for managed aquifer recharge:a review of multi-criteria decision analysis studies. Environ. Rev. 27 (2), 138-150. https://doi.org/10.1139/er-2018-0069.
    Sandoval, J.A., Tiburan Jr., C.L., 2019. Identification of potential artificial groundwater recharge sites in Mount Makiling Forest Reserve, Philippines using GIS and Analytical Hierarchy Process. Appl. Geogr. 105, 73-85. https://doi.org/10.1016/j.apgeog.2019.01.010.
    Sargaonkar, A.P., Rathi, B., Baile, A., 2010. Identifying potential sites for artificial groundwater recharge in sub-watershed of River Kanhan, India. Environ. Earth. Sci. 62 (5), 1099-1108.
    Senanayake, I.P., Dissanayake, D.M.D.O.K., Mayadunna, B.B., Weerasekera, W.L., 2016. An approach to delineate groundwater recharge potential sites in Ambalantota, Sri Lanka using GIS techniques. Geosci. Front. 7 (1), 115-124.
    Sheikhipour, B., Javadi, S., Banihabib, M.E., 2018. A hybrid multiple criteria decisionmaking model for the sustainable management of aquifers. Environ. Earth Sci. 77(19), 712. https://doi.org/10.1007/s12665-018-7894-4.
    Shi, X., Jiang, S., Xu, H., Jiang, F., He, Z., Wu, J., 2016. The effects of artificial recharge of groundwater on controlling land subsidence and its influence on groundwater quality and aquifer energy storage in Shanghai, China. Environ. Earth Sci. 75 (3), 195.https://doi.org/10.1007/s12665-015-5019-x.
    Singh, L.K., Jha, M.K., Chowdary, V.M., 2017. Multi-criteria analysis and GIS modeling for identifying prospective water harvesting and artificial recharge sites for sustainable water supply. J. Clean. Prod. 142, 1436-1456.
    Singh, A., Panda, S.N., Kumar, K.S., Sharma, C.S., 2013. Artificial groundwater recharge zones mapping using remote sensing and GIS:a case study in Indian Punjab. Environ.Manage. 52 (1), 61-71.
    Thangarajan, M., 2007. Groundwater models and their role in assessment and management of groundwater resources and pollution. Groundwater, 189-236 https://doi.org/10.1007/978-1-4020-5729-8_8.
    Theodoridis, S., Koutroumbas, K., 2003. Pattern Recognition. 2nd ed. Elsevier Academic Press.
    Tu, Y.C., Ting, C.S., Tsai, H.T., Chen, J.W., Lee, C.H., 2011. Dynamic analysis of the infiltration rate of artificial recharge of groundwater:a case study of Wanglong Lake, Pingtung, Taiwan. Environ. Earth Sci. 63 (1), 77-85. https://doi.org/10.1007/s12665-010-0670-8.
    Vaidya, O., Kumar, S., 2006. Analytic hierarchy process:an overview of applications. Eur.J. Oper. Res. 169 (1), 1-29. https://doi.org/10.1016/j.ejor.2004.04.028.
    Valente, J.O., Pedrycz, W., 2007. Advances in Fuzzy Clustering and its Applications. John Wiley & Sons Ltd, England, p. 435.
    Valverde, J.P., Blank, C., Roidt, M., Schneider, L., Stefan, C., 2016. Application of a GIS multicriteria decision analysis for the identification of intrinsic suitable sites in Costa Rica for the application of managed aquifer recharge (MAR) through spreading methods.Water 8 (9), 391.
    Voudouris, K., Diamantopoulou, P., Giannatos, G., Zannis, P., 2006. Groundwater recharge via deep boreholes in the Patras Industrial Area aquifer system (NW Peloponnesus, Greece). Bull. Eng. Geol. Environ. 65 (3), 297-308. https://doi.org/10.1007/s10064-005-0036-8.
    Water Budget Report, 2014. The Regional Water Company of Yasouj. http://wrs.wrm.ir/amar/login.asp (in Pesian).
    Zaree, M., Javadi, S., Neshat, A., 2019. Potential detection of water resources in karst formations using APLIS model and modification with AHP and TOPSIS. J. Earth Syst.Sci. 128 (76), 2019. https://doi.org/10.1007/s12040-019-1119-4.
    Zhang, J., Wu, G., Hu, X., Li, S., Hao, S., 2013. A parallel clustering algorithm with mpimkmeans. J. Comput. 8 (1), 10-17.
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