Weibull distribution under indeterminacy with applications

Mohammed Albassam; Muhammad Ahsan-ul-Haq; Muhammad Aslam; Mohammed Albassam; Muhammad Ahsan-ul-Haq; Muhammad Aslam

doi:10.3934/math.2023545

AIMS Mathematics

2023, Volume 8, Issue 5: 10745-10757. doi: 10.3934/math.2023545

Previous Article Next Article

Research article Special Issues

Weibull distribution under indeterminacy with applications

1.
Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21551
2.
College of Statistical Sciences, University of the Punjab, Pakistan

Received: 03 September 2022 Revised: 09 February 2023 Accepted: 22 February 2023 Published: 06 March 2023
MSC : 62A86

The Weibull distribution has always been important in numerous areas because of its vast variety of applications. In this paper, basic properties of the neutrosophic Weibull distribution are derived. The effect of indeterminacy is studied on parameter estimation. The application of the neutrosophic Weibull distribution will be discussed with the help of two real-life datasets. From the analysis, it can be seen that the neutrosophic Weibull model is adequate, reasonable, and effective to apply in an uncertain environment.
- Weibull distribution,
- indeterminacy,
- wind speed,
- neutrosophy,
- estimation
Citation: Mohammed Albassam, Muhammad Ahsan-ul-Haq, Muhammad Aslam. Weibull distribution under indeterminacy with applications[J]. AIMS Mathematics, 2023, 8(5): 10745-10757. doi: 10.3934/math.2023545

Related Papers:

Abstract

The Weibull distribution has always been important in numerous areas because of its vast variety of applications. In this paper, basic properties of the neutrosophic Weibull distribution are derived. The effect of indeterminacy is studied on parameter estimation. The application of the neutrosophic Weibull distribution will be discussed with the help of two real-life datasets. From the analysis, it can be seen that the neutrosophic Weibull model is adequate, reasonable, and effective to apply in an uncertain environment.

References

[1]	K. Reinhardt, W. Kern, Handbook of silicon wafer cleaning technology, William Andrew, 2018.
[2]	L. Lazzari, Statistical analysis of corrosion data, Eng. Tools Corros., 2017,131–148. https://doi.org/10.1016/B978-0-08-102424-9.00008-2 doi: 10.1016/B978-0-08-102424-9.00008-2
[3]	Q. Zhou, C. Wang, G. Zhang, Hybrid forecasting system based on an optimal model selection strategy for different wind speed forecasting problems, Appl. Energy, 250 (2019), 1559–1580. https://doi.org/10.1016/j.apenergy.2019.05.016 doi: 10.1016/j.apenergy.2019.05.016
[4]	J. Heng, Y. Hong, J. Hu, S. Wang, Probabilistic and deterministic wind speed forecasting based on non-parametric approaches and wind characteristics information, Appl. Energy, 306 (2022), 118029. https://doi.org/10.1016/j.apenergy.2021.118029 doi: 10.1016/j.apenergy.2021.118029
[5]	K. Bagci, T. Arslan, H. E. Celik, Inverted Kumaraswamy distribution for modeling the wind speed data: Lake Van, Turkey, Renew. Sust. Energ. Rev., 135 (2021), 110110. https://doi.org/10.1016/j.rser.2020.110110 doi: 10.1016/j.rser.2020.110110
[6]	Y. M. Kantar, I. Usta, Analysis of the upper-truncated Weibull distribution for wind speed, Energy Convers. Manag., 96 (2015), 81–88. https://doi.org/10.1016/j.enconman.2015.02.063 doi: 10.1016/j.enconman.2015.02.063
[7]	M. Ahsan-ul-Haq, G. S. Rao, M. Albassam, M. Aslam, Marshall-Olkin power Lomax distribution for modeling of wind speed data, Energy Rep., 6 (2020), 1118–1123. https://doi.org/10.1016/j.egyr.2020.04.033 doi: 10.1016/j.egyr.2020.04.033
[8]	M. Shoaib, S. I. Dar, M. Ahsan-ul-Haq, R. M. Usman, A sustainable generalization of inverse Lindley distribution for wind speed analysis in certain regions of Pakistan, Model. Earth Syst. Env., 8 (2022), 625–637. https://doi.org/10.1007/s40808-021-01114-7 doi: 10.1007/s40808-021-01114-7
[9]	X. Y. An, Z. Yan, J. M. Jia, A new distribution for modeling wind speed characteristics and evaluating wind power potential in Xinjiang, China, Energ. Source. Part A, 2020, 1–19. https://doi.org/10.1080/15567036.2020.1758250 doi: 10.1080/15567036.2020.1758250
[10]	M. Ahsan-ul-Haq, S. M. Choudhary, A. H. Al-Marshadi, M. Aslam, A new generalization of Lindley distribution for modeling of wind speed data, Energy Rep., 8 (2022), 1–11. https://doi.org/10.1016/j.egyr.2021.11.246 doi: 10.1016/j.egyr.2021.11.246
[11]	A. Pak, G. A. Parham, M. Saraj, Reliability estimation in Rayleigh distribution based on fuzzy lifetime data, Int. J. Syst. Assur. Eng. Manag., 5 (2014), 487–494. https://doi.org/10.1007/s13198-013-0190-5 doi: 10.1007/s13198-013-0190-5
[12]	D. Alok, S. B. Singh, Application of fuzzy Rayleigh distribution in the nonisothermal pyrolysis of loose biomass, Acta Environ. Univ. Comen., 24 (2016), 14–22. https://doi.org/10.1515/aeuc-2016-0008 doi: 10.1515/aeuc-2016-0008
[13]	T. Van Hecke, Fuzzy parameter estimation of the Rayleigh distribution, J. Stat. Manag. Syst., 21 (2018), 1391–1400. https://doi.org/10.1080/09720510.2018.1519162 doi: 10.1080/09720510.2018.1519162
[14]	A. Pak, G. A. Parham, M. Saraj, Inference for the Rayleigh distribution based on progressive type-II fuzzy censored data, J. Mod. Appl. Stat. Meth., 13 (2014), 287–304. https://doi.org/10.22237/jmasm/1398917880 doi: 10.22237/jmasm/1398917880
[15]	M. Shafiq, M. Atif, R. Viertl, Parameter and reliability estimation of three-parameter lifetime distributions for fuzzy life times, Adv. Mech. Eng., 9 (2017), 1–9. https://doi.org/10.1177/1687814017716887 doi: 10.1177/1687814017716887
[16]	A. Chaturvedi, S. K. Singh, U. Singh, Statistical inferences of type-II progressively hybrid censored fuzzy data with Rayleigh distribution, Aust. J. Stat., 47 (2018), 40–62. https://doi.org/10.17713/ajs.v47i3.752 doi: 10.17713/ajs.v47i3.752
[17]	J. L. Zeema, D. F. X. Christopher, Evolving optimized neutrosophic C means clustering using behavioral inspiration of artificial bacterial foraging (ONCMC-ABF) in the prediction of Dyslexia, J. King Saud Univ. Inf. Sci., 34 (2022), 1748–1754. https://doi.org/10.1016/j.jksuci.2019.09.008 doi: 10.1016/j.jksuci.2019.09.008
[18]	I. R. Sumathi, C. A. C. Sweety, New approach on differential equation via trapezoidal neutrosophic number, Complex Intell. Syst., 5 (2019), 417–424. https://doi.org/10.1007/s40747-019-00117-3 doi: 10.1007/s40747-019-00117-3
[19]	I. Maiti, T. Mandal, S. Pramanik, Neutrosophic goal programming strategy for multi-level multi-objective linear programming problem, J. Amb. Intel. Hum. Comp., 11 (2020), 3175–3186. https://doi.org/10.1007/s12652-019-01482-0 doi: 10.1007/s12652-019-01482-0
[20]	M. Abdel-Basset, R. Mohamed, M. Elhoseny, V. Chang, Evaluation framework for smart disaster response systems in uncertainty environment, Mech. Syst. Signal Process., 145 (2020), 1–18. https://doi.org/10.1016/j.ymssp.2020.106941 doi: 10.1016/j.ymssp.2020.106941
[21]	M. Abdel-Basset, A. Gamal, R. K. Chakrabortty, M. J. Ryan, Evaluation approach for sustainable renewable energy systems under uncertain environment: A case study, Renew. Energy, 168 (2021), 1073–1095. https://doi.org/10.1016/j.renene.2020.12.124 doi: 10.1016/j.renene.2020.12.124
[22]	F. Smarandache, Neutrosophy: Neutrosophic probability, set, and logic: Analytic synthesis & Synthetic analysis, Ann Arbor, Michigan, USA, 1998.
[23]	M. Abdel-Basset, A. Atef, F. Smarandache, A hybrid neutrosophic multiple criteria group decision making approach for project selection, Cogn. Syst. Res., 57 (2019), 216–227. https://doi.org/10.1016/j.cogsys.2018.10.023 doi: 10.1016/j.cogsys.2018.10.023
[24]	X. Peng, J. Dai, Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function, Neural Comput. Appl., 29 (2018), 939–954. https://doi.org/10.1007/s00521-016-2607-y doi: 10.1007/s00521-016-2607-y
[25]	A. I. Shahin, Y. Guo, K. M. Amin, A. A. Sharawi, A novel white blood cells segmentation algorithm based on adaptive neutrosophic similarity score, Heal. Inf. Sci. Syst., 6 (2018), 1–12. https://doi.org/10.1007/s13755-017-0038-5 doi: 10.1007/s13755-017-0038-5
[26]	F. Smarandache, Introduction to neutrosophic statistics, 2014. Available from: http://arXiv.org/abs/1406.2000.
[27]	R. Alhabib, M. M. Ranna, H. Farah, A. A. Salama, Some neutrosophic probability distributions, Neutrosophic Sets Sy., 22 (2018), 30–38.
[28]	K. F. H. Alhasan, F. Smarandache, Neutrosophic Weibull distribution and neutrosophic family Weibull distribution, Infinite Study, 2019.
[29]	S. K. Patro, F. Smarandache, The neutrosophic statistical distribution, more problems, more solutions, Infinite Study, 2016.
[30]	M. Aslam, Neutrosophic Rayleigh distribution with some basic properties and application, In Neutrosophic Sets in Decision Analysis and Operations Research, IGI Global, 2020,119–128. https://doi.org/10.4018/978-1-7998-2555-5.ch006
[31]	R. A. K. Sherwani, M. Naeem, M. Aslam, M. A. Raza, M. Abid, S. Abbas, Neutrosophic Beta distribution with properties and applications, Neutrosophic Sets Sy., 41 (2021), 209–214.
[32]	M. Ahsan-ul-Haq, Neutrosophic Kumaraswamy distribution with engineering application, Neutrosophic Sets Sy., 49 (2022), 1–8.
[33]	M. Aslam, Testing average wind speed using sampling plan for Weibull distribution under indeterminacy, Sci. Rep., 11 (2021), 1–9. https://doi.org/10.1038/s41598-020-79139-8 doi: 10.1038/s41598-020-79139-8
[34]	M. Ahsan-ul-Haq, J. Zafar, A new one-parameter discrete probability distribution with its neutrosophic extension: Mathematical properties and applications, Int. J. Data Sci. Anal., 2023, 1–11. https://doi.org/10.1007/s41060-023-00382-z doi: 10.1007/s41060-023-00382-z
[35]	M. Teimouri, S. Nadarajah, MPS: An R package for modelling shifted families of distributions, Aust. N. Z. J. Stat., 64 (2022), 86–108. https://doi.org/10.1111/anzs.12359 doi: 10.1111/anzs.12359

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)