Neuro-fuzzy modeling and prediction of summer precipitation with application to different meteorological stations

Bukhari, Ayaz Hussain; Sulaiman, Muhammad; Islam, Saeed; Shoaib, Mohammed; Kumam, Poom; Raja, Muhammad Asif Zahoor

doi:10.1016/j.aej.2019.12.011

Cited by 69 publications

(25 citation statements)

References 41 publications

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“…In future one may explore the different characteristics of 3-D MHD flow of hybrid nanofluid with thermal radiation features through modern and advanced numerical computing skills based of artificial intelligence 60 – 66 .…”

Section: Discussionmentioning

confidence: 99%

Numerical investigation for rotating flow of MHD hybrid nanofluid with thermal radiation over a stretching sheet

Shoaib

Raja

Sabir

et al. 2020

Sci Rep

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183

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This research investigates the heat and mass transfer in 3-D MHD radiative flow of water based hybrid nanofluid over an extending sheet by employing the strength of numerical computing based Lobatto IIIA method. Nanoparticles of aluminum oxide (Al2O3) and silver (Ag) are being used with water (H2O) as base fluid. By considering the heat transfer phenomenon due to thermal radiation effects. The physical flow problem is then modeled into set of PDEs, which are then transmuted into equivalent set of nonlinear ODEs by utilizing the appropriate similarity transformations. The system of ODEs is solved by the computational strength of Lobatto IIIA method to get the various graphical and numerical results for analyzing the impact of various physical constraints on velocity and thermal profiles. Additionally, the heat transfers and skin friction analysis for the fluid flow dynamics is also investigated. The relative errors up to the accuracy level of 1e-15, established the worth and reliability of the computational technique. It is observed that heat transfer rate increases with the increase in magnetic effect, Biot number and rotation parameter.

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Section: Discussionmentioning

confidence: 99%

Numerical investigation for rotating flow of MHD hybrid nanofluid with thermal radiation over a stretching sheet

Shoaib

Raja

Sabir

et al. 2020

Sci Rep

Self Cite

183

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“…In the future, one may explore, investigate, or exploit the stochastic numerical computing approaches based on the artificial intelligence paradigm [37][38][39][40][41][42][43][44] for alternate, accurate, robust, and stable solutions, not only for the given biological fluidic model involving nano-materials, but also for other stiff nonlinear systems, which are still a challenge for traditional/classical numerical methods.…”

Section: Discussionmentioning

confidence: 99%

Heat transfer analysis of biological nanofluid flow through ductus efferentes

et al. 2020

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A theoretical investigation on heat transfer of nanofluid transport in a ciliated channel is presented. A mathematical model for cilia oriented nanofluid transport through the human male reproductive tract is exhibited. Considering the structure of tubules, the Reynolds number is considered very small. The concept of a long wavelength approximation is incorporated to model the nanofluid flow problem. The exact analytical expressions for the x-component of velocity, temperature, nanoparticle concentration, stream function, volume flow rate, and pressure gradient are calculated. The solution for the transverse component of velocity, pressure rise per wavelength, and friction force on the wall of the channel is explored numerically, while variations in crucial flow parameters are examined graphically.

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“…(6) via Meyer wavelet neural networks (MWNN) optimized with global search efficacy of genetic algorithms (GAs) and sequential quadratic programming (SQP), i.e., MWNN-GASQP. The solvers based on meta-heuristic intelligent computing have been extensively applied for the analysis of linear/nonlinear, singular/non-singular systems using neural networks optimized with evolutionary/swarming-based computing schemes (Lodhi 2019;Raja et al 2017a;Bukhari 2020;Waseem 2020;Ahmad 2020Ahmad ,2019. Some recent applications of the evolutionary/swarming-based numerical computing are Painlevé equation-based models in random matrix theory (Raja et al 2018a), nonlinear prey-predator models (Umar 2019), Bagley-Torvik systems in fluid mechanics.…”

Section: Problem Statement and Related Workmentioning

confidence: 99%

FMNEICS: fractional Meyer neuro-evolution-based intelligent computing solver for doubly singular multi-fractional order Lane–Emden system

et al. 2020

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In the present study, a novel fractional Meyer neuro-evolution-based intelligent computing solver (FMNEICS) is presented for numerical treatment of doubly singular multi-fractional Lane-Emden system (DSMF-LES) using combined heuristics of Meyer wavelet neural networks (MWNN) optimized with global search efficacy of genetic algorithms (GAs) and sequential quadratic programming (SQP), i.e., MWNN-GASQP. The design of novel FMNE-ICS for DSMF-LES is presented after derivation from standard Lane-Emden equation, and the singular points and shape factors along with fractional-order terms are analyzed. The MWNN modeling strength is used to represent the system model DSMF-LES in the meansquared error-based merit function and optimization of the networks is carried out with integrated optimization ability of GASQP. The verification, validation, and perfection of the FMNEICS for three different cases of DSMF-LES are established through comparative studies from reference solutions on convergence, robustness, accuracy, and stability measures. Moreover, the observations through the statistical analysis further authenticate the worth of proposed fractional MWNN-GASQP-based stochastic solver. Keywords Multi-fractional Lane-Emden model • Multi-singular systems • Artificial neural networks • Meyer wavelet neural networks • Sequential quadratic programming • Genetic algorithms Mathematics Subject Classification 34-XX • 34A08 • 34A34 • 82B31

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Neuro-fuzzy modeling and prediction of summer precipitation with application to different meteorological stations

Cited by 69 publications

References 41 publications

Numerical investigation for rotating flow of MHD hybrid nanofluid with thermal radiation over a stretching sheet

Numerical investigation for rotating flow of MHD hybrid nanofluid with thermal radiation over a stretching sheet

Heat transfer analysis of biological nanofluid flow through ductus efferentes

FMNEICS: fractional Meyer neuro-evolution-based intelligent computing solver for doubly singular multi-fractional order Lane–Emden system

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