Fusion: Practice and Applications

Journal DOI

https://doi.org/10.54216/FPA

Submit Your Paper

2692-4048ISSN (Online) 2770-0070ISSN (Print)

Volume 14 , Issue 1 , PP: 129-137, 2024 | Cite this article as | XML | Html | PDF | Full Length Article

A Fusion of Multi-Criteria Decision-Making for Select Recharge Structure

Walter Culque Toapanta 1 * , Fausto Vizcaíno Naranjo 2 , Antonio Castillo Medina 3

  • 1 Docente de la carrera de Software de la Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador - (ua.walterculque@uniandes.edu.ec)
  • 2 Docente de la carrera de Software de la Universidad Regional Autónoma de los Andes (UNIANDES), Ecuador - (ua.faustovizcaino@uniandes.edu.ec)
  • 3 Docente de la carrera de Automotriz de la Universidad Regional Autónoma de los Andes (UNIANDES) Sede Santo Domingo, Ecuador - (ua.antoniocm83@uniandes.edu.ec)
  • Doi: https://doi.org/10.54216/FPA.140110

    Received: June 06, 2023 Revised: September 01, 2023 Accepted: November 22, 2023
    Abstract

    Groundwater recharge is essential in establishing reliable groundwater supplies in a region. Groundwater is a vital natural water resource, but its quantity and quality may vary significantly from one area to another. Growing urbanization and population increase have put a significant demand on groundwater supplies. Using Multi-Criteria Decision-Making (MCDM), several studies have identified good areas for recharging groundwater supplies. To help choose between several types of artificial recharge (AR) structures, we have developed an MCDM approach for this research. We used an MCDM fusion methodology to combine various AR criteria with the alternatives. This study collected eight criteria and eight alternatives. We used the average method to compute the weights of the criteria. Then, we used the COCOSO method as an MCDM fusion method to rank the alternatives. The results show that hydrological conditions are the best criteria, and stakeholder engagement is the lowest weight. The sensitivity analysis is performed to show the stability of the results in this study. 

    Keywords :

    Multi-Criteria Decision Making , Data Fusion , Recharge Structure , COCOSO Method.

    References

    [1]        M. Abraham and S. Mohan, “Effectiveness of artificial recharge structures in enhancing groundwater storage: A case study,” Indian journal of Science and technology, vol. 8, no. 20, pp. 1–10, 2015.

    [2]        Z. Tao, Z. Cui, J. Yu, and M. Khayatnezhad, “Finite difference modelings of groundwater flow for constructing artificial recharge structures,” Iranian Journal of Science and Technology, Transactions of Civil Engineering, vol. 46, no. 2, pp. 1503–1514, 2022.

    [3]        S. Ahirwar, M. S. Malik, R. Ahirwar, and J. P. Shukla, “Identification of suitable sites and structures for artificial groundwater recharge for sustainable groundwater resource development and management,” Groundwater for sustainable development, vol. 11, p. 100388, 2020.

    [4]        I. Gale, I. Neumann, R. Calow, and  dan M. Moench, “The effectiveness of Artificial Recharge of groundwater: a review,” 2002.

    [5]        S. Arya, T. Subramani, and D. Karunanidhi, “Delineation of groundwater potential zones and recommendation of artificial recharge structures for augmentation of groundwater resources in Vattamalaikarai Basin, South India,” Environmental Earth Sciences, vol. 79, pp. 1–13, 2020.

    [6]        I. Neumann, J. Barker, D. MacDonald, and I. Gale, “Numerical approaches for approximating technical effectivessness of artificial recharge structures,” 2004.

    [7]        A. S. Jasrotia, R. Kumar, A. K. Taloor, and A. K. Saraf, “Artificial recharge to groundwater using geospatial and groundwater modelling techniques in North Western Himalaya, India,” Arabian Journal of Geosciences, vol. 12, pp. 1–23, 2019.

    [8]        S. R. Kolanuvada, K. L. Ponpandian, and S. Sankar, “Multi-criteria-based approach for optimal siting of artificial recharge structures through hydrological modeling,” Arabian Journal of Geosciences, vol. 12, pp. 1–10, 2019.

    [9]        Myvizhi M., Neutrosophic MCDM Model for Evaluation and Selection best 5G Network Architecture, International Journal of Advances in Applied Computational Intelligence, Vol. 4 , No. 1 , (2023) : 08-18 (Doi   :  https://doi.org/10.54216/IJAACI.040101).

    [10]      Y. Zhu, S. Zeng, Z. Lin, and K. Ullah, “Comprehensive evaluation and spatial-temporal differences analysis of China’s inter-provincial doing business environment based on Entropy-CoCoSo method,” Frontiers in Environmental Science, vol. 10, p. 1088064, 2023.

    [11]      Tamer H. M. Soliman, Neutrosophic Multi-Criteria Decision Making COMET Method for Evaluation Sustainable Electricity Generation Considering Renewable Energy Sources, International Journal of Advances in Applied Computational Intelligence, Vol. 4 , No. 1 , (2023) : 19-27 (Doi   :  https://doi.org/10.54216/IJAACI.040102).

    [12]      X. Peng and F. Smarandache, “A decision-making framework for China’s rare earth industry security evaluation by neutrosophic soft CoCoSo method,” Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7571–7585, 2020.

    [13]      A. Gamal and M. Mohamed, “A Hybrid MCDM Approach for Industrial Robots Selection for the Automotive Industry,” Neutrosophic Systems with Applications, vol. 4, pp. 1–11, 2023.

    [14]      K. K. Panchagnula, J. P. Sharma, K. Kalita, and S. Chakraborty, “CoCoSo method-based optimization of cryogenic drilling on multi-walled carbon nanotubes reinforced composites,” International Journal on Interactive Design and Manufacturing (IJIDeM), vol. 17, no. 1, pp. 279–297, 2023.

    [15]      Mustafa Altaee, A. Jawad, Mohammed Abdul Jalil, Sanaa Al-Kikani, Ahmed Oleiwi, Hatıra Günerhan,  A Multi-level Fusion System for Intelligent Capture and Assessment of Student Activity in Physical Training based on Machine Learning,  Journal of Intelligent Systems and Internet of Things,  Vol. 9 ,  No. 1, pp: 08-23 (Doi   :  https://doi.org/10.54216/JISIoT.090101

    [16]      A. Barua, S. Jeet, D. K. Bagal, P. Satapathy, and P. K. Agrawal, “Evaluation of mechanical behavior of hybrid natural fiber reinforced nano sic particles composite using hybrid Taguchi-CoCoSo method,” International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 10, pp. 3341–3345, 2019.

    [17]      Mohammed Hasan Aldulaimi, Ibrahim Najem, Tabarak Ali Abdulhussein, M. H. Ali, Asaad Shakir Hameed, M. Altaee, Hatira Günerhan,  Intelligent Load Identification of Household-Smart Meters Using Multilevel Decision Tree and Data Fusion Techniques,  Journal of Intelligent Systems and Internet of Things,  Vol. 9 ,  No. 1 ,  (2023) : 24-35 (Doi   :  https://doi.org/10.54216/JISIoT.090102)

    [18]      K. M. Sallam and A. W. Mohamed, “Neutrosophic MCDM Methodology for Evaluation Onshore Wind for Electricity Generation and Sustainability Ecological,” Neutrosophic Systems with Applications, vol. 4, pp. 53–61, 2023.

    [19]      D. K. Tripathi, S. K. Nigam, P. Rani, and A. R. Shah, “New intuitionistic fuzzy parametric divergence measures and score function-based CoCoSo method for decision-making problems,” Decision Making: Applications in Management and Engineering, vol. 6, no. 1, pp. 535–563, 2023.

    [20]      S. J. Ghoushchi, S. M. Jalalat, S. R. Bonab, A. M. Ghiaci, G. Haseli, and H. Tomaskova, “Evaluation of wind turbine failure modes using the developed SWARA-CoCoSo methods based on the spherical fuzzy environment,” IEEE Access, vol. 10, pp. 86750–86764, 2022.

    [21]      G. Demir, M. Damjanović, B. Matović, and R. Vujadinović, “Toward sustainable urban mobility by using fuzzy-FUCOM and fuzzy-CoCoSo methods: the case of the SUMP podgorica,” Sustainability, vol. 14, no. 9, p. 4972, 2022.

    [22]      H. Lai, H. Liao, Y. Long, and E. K. Zavadskas, “A hesitant Fermatean fuzzy CoCoSo method for group decision-making and an application to blockchain platform evaluation,” International Journal of Fuzzy Systems, vol. 24, no. 6, pp. 2643–2661, 2022.

    [23]      P. P. Dwivedi and D. K. Sharma, “Application of Shannon entropy and CoCoSo methods in selection of the most appropriate engineering sustainability components,” Cleaner Materials, vol. 5, p. 100118, 2022.

    [24]      M. Abouhawwash and M. Jameel, “Evaluation Factors of Solar Power Plants to Reduce Cost Under Neutrosophic Multi-Criteria Decision-Making Model,” Sustainable Machine Intelligence Journal, vol. 2, 2023.

    [25]      M. Yazdani, P. Zarate, E. Kazimieras Zavadskas, and Z. Turskis, “A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems,” Management Decision, vol. 57, no. 9, pp. 2501–2519, 2019.

    [26]      A. Ulutaş, C. B. Karakuş, and A. Topal, “Location selection for logistics center with fuzzy SWARA and CoCoSo methods,” Journal of Intelligent & Fuzzy Systems, vol. 38, no. 4, pp. 4693–4709, 2020.

    [27]      M. Popović, “An MCDM approach for personnel selection using the CoCoSo method,” Journal of process management and new technologies, vol. 9, no. 3–4, pp. 78–88, 2021.

    [28]      H. Lai, H. Liao, Z. Wen, E. K. Zavadskas, and A. Al-Barakati, “An improved CoCoSo method with a maximum variance optimization model for cloud service provider selection,” Engineering Economics, vol. 31, no. 4, pp. 411–424, 2020.

    [29]      M. Deveci, D. Pamucar, and I. Gokasar, “Fuzzy Power Heronian function based CoCoSo method for the advantage prioritization of autonomous vehicles in real-time traffic management,” Sustainable Cities and Society, vol. 69, p. 102846, 2021.

    Cite This Article As :
    Culque, Walter. , Vizcaíno, Fausto. , Castillo, Antonio. A Fusion of Multi-Criteria Decision-Making for Select Recharge Structure. Fusion: Practice and Applications, vol. , no. , 2024, pp. 129-137. DOI: https://doi.org/10.54216/FPA.140110
    Culque, W. Vizcaíno, F. Castillo, A. (2024). A Fusion of Multi-Criteria Decision-Making for Select Recharge Structure. Fusion: Practice and Applications, (), 129-137. DOI: https://doi.org/10.54216/FPA.140110
    Culque, Walter. Vizcaíno, Fausto. Castillo, Antonio. A Fusion of Multi-Criteria Decision-Making for Select Recharge Structure. Fusion: Practice and Applications , no. (2024): 129-137. DOI: https://doi.org/10.54216/FPA.140110
    Culque, W. , Vizcaíno, F. , Castillo, A. (2024) . A Fusion of Multi-Criteria Decision-Making for Select Recharge Structure. Fusion: Practice and Applications , () , 129-137 . DOI: https://doi.org/10.54216/FPA.140110
    Culque W. , Vizcaíno F. , Castillo A. [2024]. A Fusion of Multi-Criteria Decision-Making for Select Recharge Structure. Fusion: Practice and Applications. (): 129-137. DOI: https://doi.org/10.54216/FPA.140110
    Culque, W. Vizcaíno, F. Castillo, A. "A Fusion of Multi-Criteria Decision-Making for Select Recharge Structure," Fusion: Practice and Applications, vol. , no. , pp. 129-137, 2024. DOI: https://doi.org/10.54216/FPA.140110