Supervised neural learning for the predator-prey delay differential system of Holling form-III

Naret Ruttanaprommarin; Zulqurnain Sabir; Salem Ben Said; Muhammad Asif Zahoor Raja; Saira Bhatti; Wajaree Weera; Thongchai Botmart; Naret Ruttanaprommarin; Zulqurnain Sabir; Salem Ben Said; Muhammad Asif Zahoor Raja; Saira Bhatti; Wajaree Weera; Thongchai Botmart

doi:10.3934/math.20221101

AIMS Mathematics

2022, Volume 7, Issue 11: 20126-20142. doi: 10.3934/math.20221101

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Research article

Supervised neural learning for the predator-prey delay differential system of Holling form-III

1.
Department of Science and Mathematics, Faculty of Industry and Technology, Rajamangala University of Technology Isan Sakonnakhon Campus, Sakonnakhon 47160, Thailand
2.
Department of Mathematical Sciences, United Arab Emirates University, P.O. Box 15551, Al Ain, UAE
3.
Department of Mathematics, Hazara University, Mansehra, Pakistan
4.
Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan
5.
Department of Mathematics, COMSATS University Islamabad, Abbottabad Campus, Abbottabad Pakistan
6.
Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

Received: 05 July 2022 Revised: 22 August 2022 Accepted: 30 August 2022 Published: 13 September 2022
MSC : 92B20, 34A34

The purpose of this work is to present the stochastic computing study based on the artificial neural networks (ANNs) along with the scaled conjugate gradient (SCG), ANNs-SCG for solving the predator-prey delay differential system of Holling form-III. The mathematical form of the predator-prey delay differential system of Holling form-III is categorized into prey class, predator category and the recent past effects. Three variations of the predator-prey delay differential system of Holling form-III have been numerical stimulated by using the stochastic ANNs-SCG procedure. The selection of the data to solve the predator-prey delay differential system of Holling form-III is provided as 13%, 12% and 75% for testing, training, and substantiation together with 15 neurons. The correctness and exactness of the stochastic ANNs-SCG method is provided by using the comparison of the obtained and data-based reference solutions. The constancy, authentication, soundness, competence, and precision of the stochastic ANNs-SCG technique is performed through the analysis of the correlation measures, state transitions (STs), regression analysis, correlation, error histograms (EHs) and MSE.

Keywords:

Citation: Naret Ruttanaprommarin, Zulqurnain Sabir, Salem Ben Said, Muhammad Asif Zahoor Raja, Saira Bhatti, Wajaree Weera, Thongchai Botmart. Supervised neural learning for the predator-prey delay differential system of Holling form-III[J]. AIMS Mathematics, 2022, 7(11): 20126-20142. doi: 10.3934/math.20221101

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Abstract

1. Introduction

In recent decades, researchers have paid considerable attention to nonlinear waves at the ocean surface. Phenomena of nonlinear waves play a key role in many fields of engineering and science, such as ocean engineering, plasma physics, Control theory, tsunami waves, communications industry, fluid dynamics, and coastal engineering, etc.

Non-linear PDEs have great potential for application in various fields; therefore, researchers pay special attention to their analytical and numerical solutions ^{[1,2,3,4,5,6,7,8]}. In the literature, many researchers in mathematics and physics have developed various methods to analyze the nonlinear evolution equations (NLEEs), such as the exp-function approach ^[9], the modified simple equation technique ^{[10,11,12,13,14]}, the generalized Kudryashov technique ^[15,16], the $\frac{G'}{G}$ -expansion approach ^{[17,18,19,20,21,22,23]}, the extended rational function expansion approach ^[23], the Hirota bilinear method ^{[24,25,26,27,28,29,30]}, the extended homoclinic approach ^[31], the traveling wave scheme ^[32], the Darboux transformation method ^{[32,33,34,35]}, the sine-cosine approach ^[36], the semi-inverse variational principle ^[37], He's variational iteration technique ^[38], the sine-Gordon method ^[39,40,41], A special kind of distributive product ^[42], the Lie symmetric method ^[43], the extended homogeneous principal method ^[44], the power series method ^[45] and some other methods ^[46,47].

The Boussinesq equation represents a long-wavelength and weakly nonlinear approximation used in numerical models, water waves, and coastal engineering for simulating water waves in shallow seas and harbors. A Scottish engineer named John Scott Russell closely observed solitary waves (also called translational waves and solitons). Joseph Boussinesq based his approximation on the obervation of John Scott Russell observation. In 1872, the simulation of one-dimensional water waves was determined by Boussinesq which states that the horizontal velocity is constant and the vertical velocity is linear in addition to the water depth, referred to as the Boussinesq equation ^[48]. Previously, the Boussinesq equation was investigated using various mathematical approaches ^{[49,50,51,52,53]}.

Consider, the Boussinesq equation in the following form

$\begin{eqnarray} Q_{tt}-Q_{xx} = (Q^2)_{xx}+\lambda Q_{xxxx}, \end{eqnarray}$

(1.1)

where $Q = Q(x, t)$ represents the wave envelope containing x as a spatial variable and $t$ as a temporal variable. Here $\lambda$ is an arbitrary constant. This is called frequency dispersion phenomenon when water waves of different wavelengths are related, and in the case of an infinitesimal wave amplitude it is also called a linear frequency dispersion. For this reason, this is a valid approximation. The Boussinesq equation allows for waves to propagate in different directions as well, but it is advantageous to consider waves that propagate in the same direction. To form strong and reliable solitons of the Boussinesq equation using (SSET) ^[54,55,56], the following traveling wave transformation is used.

$\begin{eqnarray} X(x, t) = X(\beta), \, \, \, \, \ \beta = \eta x+\chi t. \end{eqnarray}$

(1.2)

Here $\eta$ and $\chi$ are real constants. Applying Eq (1.2) in Eq (1.1), the following ordinary differential equation (ODE) is constructed.

$\begin{eqnarray} \chi^2 Q''-\eta^2 Q''-\eta^2(Q^2)''-\lambda\eta^4 Q^{(iv)} = 0. \end{eqnarray}$

(1.3)

Integrating Eq (1.3) twice with respect to $\beta$ and neglecting the integration constants, we obtain the following equation.

$\begin{eqnarray} (\chi^2-\eta^2) Q-\eta^2 Q^2-\lambda \eta^4 Q'' = 0. \end{eqnarray}$

(1.4)

Where $\chi$ and $\eta$ are the velocity and frequency of the propagating wave.

2. Mathematical description

We proceed with these main steps in this work:

Step 1:

$\begin{eqnarray} H(X, X_{x}, X_{t}, X_{xx}, X_{tt}, ...) = 0, \end{eqnarray}$

(2.1)

where $H$ is a polynomial of $X$ and $X(\beta) = X(x, t)$ is a unknown function. Consider the wave transformation

$\begin{eqnarray*} \beta = \eta x+\chi t, \end{eqnarray*}$

where $\chi\neq 0$ is a constant to be determined later.

Using the transformation, Eq (2.1) is converted to the following ODE.

$\begin{eqnarray} G = (Q, Q', Q'', ..., ) = 0. \end{eqnarray}$

(2.2)

Step 2: $G$ is a function of $Q(\beta)$ and prime express the derivatives in regard to $\beta$ .

Solution of Eq (2.2) can be formulated as

$\begin{eqnarray} Q(\beta) = \sum\limits_{l = 0}^N c_{l} M^l (\beta), \, \, \, \, \ c_{l}\neq 0, \end{eqnarray}$

(2.3)

where $c_l(0\leq l\leq N)$ are real constants and $M(\beta)$ satisfying the ODE in the form

$\begin{eqnarray} M'(\beta) = \sqrt{\xi+u M(\beta)^2+M(\beta)^4}. \end{eqnarray}$

(2.4)

Here $\xi$ and $u$ are real constants and Eq (2.4) presents the solutions as

Case I: If $u > 0$ and $\xi = 0$ , then

$\begin{eqnarray*} M_{1}^{\pm}(\beta) = \pm \sqrt{-pq u} \, \, {\rm{sech}}_{pq}(\sqrt{u}\beta), \\ M_{2}^{\pm}(\beta) = \pm \sqrt{pq u}\, \, {\rm{csch}}_{pq}(\sqrt{u}\beta), \\ \end{eqnarray*}$

where

$\text{sech}_{pq}(\beta) = \frac{2}{pe^{\beta}+qe^{-\beta}} , \, \, \, \text{csch}_{pq}(\beta) = \frac{2}{pe^{\beta}-qe^{-\beta}}$ .

Case II: If $u < 0$ and $\xi = 0$ , then

$\begin{eqnarray*} M_{3}^{\pm}(\beta) = \pm \sqrt{-pq u}\, \, \sec_{pq}(\sqrt{-u}\beta), \\ M_{4}^{\pm}(\beta) = \pm \sqrt{-pq u} \, \, \csc_{pq}(\sqrt{-u}\beta), \\\end{eqnarray*}$

where

$\sec_{pq}(\beta) = \frac{2}{pe^{\iota\beta}+qe^{-\iota\beta}}, \, \, \, \csc_{pq}(\beta) = \frac{2\iota}{pe^{\iota\beta}-qe^{-\iota\beta}}$ .

Case III: If $u < 0$ and $\xi = \frac{u^{2}}{4}$ , then

$\begin{array}{l} M_{5}^{\pm}(\beta) = \pm{\sqrt{\frac{-u}{2}}}\tanh_{pq}({\sqrt{\frac{-u}{2}}}\beta), \\ M_{6}^{\pm}(\beta) = \pm{\sqrt{\frac{-u}{2}}}\coth_{pq}({\sqrt{\frac{-u}{2}}}\beta), \\ M_{7}^{\pm}(\beta) = \pm{\sqrt{\frac{-u}{2}}}(\tanh_{pq}(\sqrt{-2u}\beta)\pm\iota\sqrt{pq}\, \, {\rm{sech}}_{pq}(\sqrt{-2u}\beta)), \\ M_{8}^{\pm}(\beta) = \pm{\sqrt{\frac{-u}{2}}}(\coth_{pq}(\sqrt{-2u}\beta)\pm\sqrt{pq}\, \, {\rm{csch}}_{pq}(\sqrt{-2u}\beta)), \\ M_{9}^{\pm}(\beta) = \pm{\sqrt{\frac{-u}{8}}}(\tanh_{pq}({\sqrt{\frac{-u}{8}}}\beta)+\coth_{pq}({\sqrt{\frac{-u}{8}}}\beta)), \end{array}$

where

$\tanh_{pq}(\beta) = \frac{pe^{\beta}-qe^{-\beta}}{pe^{\beta}+qe^{-\beta}}, \, \, \,\coth_{pq}(\beta) = \frac{pe^{\beta}+qe^{-\beta}}{pe^{\beta}-qe^{-\beta}}$ .

Case IV: If $u > 0$ and $\xi = \frac{u^{2}}{4}$ , then

$\begin{array}{l} M_{10}^{\pm}(\beta) = \pm{\sqrt{\frac{u}{2}}}\tan_{pq}({\sqrt{\frac{u}{2}}}\beta), \\ M_{11}^{\pm}(\beta) = \pm{\sqrt{\frac{u}{2}}}\cot_{pq}({\sqrt{\frac{u}{2}}}\beta), \\ M_{12}^{\pm}(\beta) = \pm{\sqrt{\frac{u}{2}}}(\tan_{pq}(\sqrt{2u}\beta)\pm\sqrt{pq}\, \, \sec_{pq}(\sqrt{2u}\beta)), \\ M_{13}^{\pm}(\beta) = \pm{\sqrt{\frac{u}{2}}}(\cot_{pq}(\sqrt{2u}\beta)\pm\sqrt{pq}\, \, \csc_{pq}(\sqrt{2u}\beta)), \\ M_{14}^{\pm}(\beta) = \pm{\sqrt{\frac{u}{8}}}(\tan_{pq}({\sqrt{\frac{u}{8}}}\beta)+\cot_{pq}({\sqrt{\frac{u}{8}}}\beta)), \end{array}$

where

$\tan_{pq}(\beta) = -\iota{\frac{pe^{\iota\beta}-qe^{-\iota\beta}}{pe^{\iota\beta}+qe^{-\iota\beta}}}, \, \, \,\cot_{pq}(\beta) = \iota{\frac{pe^{\iota\beta}+qe^{-\iota\beta}}{pe^{\iota\beta}-qe^{-\iota\beta}}}.$

These are generalized trigonometric and hyperbolic functions with parameters p and q. If we take $p = q = 1$ , they become known trigonometric and hyperbolic functions.

Step 3: We calculate the integer $N$ by balancing the capital. Substituting Eq (2.3) into Eq (2.2) we obtain an algebraic equation in the form of $M^{l}(\beta)$ , which we balance by equating the powers of $M^{l}(\beta)$ l = (0, 1, 2, ...) to zero thus obtaining a set of algebraic equations.

Step 4: This set of equations leads to the required parameters and the exact solution of the given equation.

3. Execution of Sardar sub-equation technique

In this section, SSET is applied to the Boussinesq equation to construct the traveling wave solution. By the equilibrium rule Eq (2.3) reduces into

$\begin{eqnarray} Q(\beta) = c_0+c_1 M(\beta )+c_2 M(\beta )^2, \end{eqnarray}$

(3.1)

where $c_{0}$ , $c_{1}$ , $c_{2}$ are constants. Substitute Eq (3.1), Eq (1.4) into Eq (2.4), we obtain a polynomial in the form of $M^{l}(\beta)$ and equate powers of $M^{l}(\beta)$ to zero resulting in algebraic equations in $c_{0}$ , $c_{1}$ , $c_{2}$ , $\chi$ , $\eta$ and $\lambda$ .

Set of algebraic equations are as:

$\begin{eqnarray*} -2 c_2 \eta ^4 \lambda \xi -c_0^2 \eta ^2-c_0 \eta ^2+c_0 \chi ^2 = 0, \end{eqnarray*}$

$\begin{eqnarray*} -2 c_0 c_1 \eta ^2-c_1 \eta ^2+c_1 \eta ^4 \lambda (-u)+c_1 \chi ^2 = 0, \end{eqnarray*}$

$\begin{eqnarray*} -2 c_0 c_2 \eta ^2-c_2 \eta ^2-c_1^2 \eta ^2-4 c_2 \eta ^4 \lambda u+c_2 \chi ^2 = 0, \end{eqnarray*}$

$\begin{eqnarray*} -2 c_1 \eta ^4 \lambda -2 c_1 c_2 \eta ^2 = 0, \end{eqnarray*}$

$-6 c_2 \eta ^4 \lambda -c_2^2 \eta ^2 = 0.$

(3.2)

With the help of Mathematica software, the following parameters are determined.

$c_0 = \frac{1}{3} \left(c_2 u-\sqrt{c_2^2 u^2-3 c_2^2 \xi }\right), \, \, \,c_{1} = 0, \, \, \,c_2 = c_2$ ,

$\begin{eqnarray} \label{e11} \chi = -\frac{\sqrt{3 \eta ^2-2 \eta ^2 \sqrt{c_2^2 \left(u^2-3 \xi \right)}}}{\sqrt{3}}, \, \, \, \lambda = -\frac{c_2}{6 \eta ^2}. \end{eqnarray}$

(3.3)

Using Eqs (2.4), (3.1) and (3.3) with Eq (1.2), following solutions are constructed.

Case I: If $u > 0$ and $\xi = 0$ , then

$\begin{eqnarray} Q_{1}^{\pm}(x, t) = \frac{1}{3} \left(c_2 u-\sqrt{c_2^2 u^2-3 c_2^2 \xi }\right)+c_2 \left(\pm \sqrt{-p q u}\right)^2 {\rm{sech}}_{pq}\left(\sqrt{u} (t \chi +\eta x)\right)^2, \end{eqnarray}$

(3.4)

$\begin{eqnarray} Q_{2}^{\pm}(x, t) = \frac{1}{3} \left(c_2 u-\sqrt{c_2^2 u^2-3 c_2^2 \xi }\right)+c_2 \left(\pm \sqrt{p q u}\right)^2 {\rm{csch}}_{{pq}}\left(\sqrt{u} (t \chi +\eta x)\right)^2. \end{eqnarray}$

(3.5)

Case II: If $u < 0$ and $\xi = 0$ , then

$\begin{eqnarray} Q_{3}^{\pm}(x, t) = \frac{1}{3} \left(c_2 u-\sqrt{c_2^2 u^2-3 c_2^2 \xi }\right)+c_2 \left(\pm \sqrt{-p q u}\right)^2 \sec_{{pq}}\left(\sqrt{-u} (t \chi +\eta x)\right)^2, \end{eqnarray}$

(3.6)

$\begin{eqnarray} Q_{4}^{\pm}(x, t) = \frac{1}{3} \left(c_2 u-\sqrt{c_2^2 u^2-3 c_2^2 \xi }\right)+c_2 \left(\pm \sqrt{-p q u}\right)^2 \csc_{{pq}}\left(\sqrt{-u} (t \chi +\eta x)\right)^2. \end{eqnarray}$

(3.7)

Case III: If $u < 0$ and $\xi = \frac{u^{2}}{4}$ , then

$\begin{eqnarray} Q_{5}^{\pm}(x, t) = \frac{1}{3} \left(c_2 u-\sqrt{c_2^2 u^2-3 c_2^2 \xi }\right)+c_2 \left(\pm \frac{\sqrt{-u}}{\sqrt{2}}\right)^2 \tanh _{p q}\left(\frac{\sqrt{-u} (t \chi +\eta x)}{\sqrt{2}}\right)^2, \end{eqnarray}$

(3.8)

$\begin{eqnarray} Q_{6}^{\pm}(x, t) = \frac{1}{3} \left(c_2 u-\sqrt{c_2^2 u^2-3 c_2^2 \xi }\right)+c_2 \left(\pm \frac{\sqrt{-u}}{\sqrt{2}}\right)^2 \coth _{p q}\left(\frac{\sqrt{-u} (t \chi +\eta x)}{\sqrt{2}}\right)^2, \end{eqnarray}$

(3.9)

$\begin{eqnarray} Q_{7}^{\pm}(x, t) = &&\frac{1}{3} \left(c_2 u-\sqrt{c_2^2 u^2-3 c_2^2 \xi }\right)+c_2 \left(\pm \frac{\sqrt{-u}}{\sqrt{2}}\left(\tanh _{p q}\left(\sqrt{2} \sqrt{-u} (t \chi +\eta x)\right.\right.\right)\\&&\pm i \sqrt{p q} {\rm{sech}}_{p q}\left.\left.\left(\sqrt{2} \sqrt{-u} (t \chi +\eta x)\right)\right)\right)^2, \end{eqnarray}$

(3.10)

$\begin{eqnarray} Q_{8}^{\pm}(x, t) = &&\frac{1}{3} \left(c_2 u-\sqrt{c_2^2 u^2-3 c_2^2 \xi }\right)+c_2 \left(\pm \frac{\sqrt{-u}}{\sqrt{2}}\left(\coth _{p q}\left(\sqrt{2} \sqrt{-u} (t \chi +\eta x)\right)\right.\right.\\&&\pm \sqrt{p q} {\rm{csch}}_{p q}\left.\left.\left(\sqrt{2} \sqrt{-u} (t \chi +\eta x)\right)\right)\right)^2, \end{eqnarray}$

(3.11)

$\begin{eqnarray} Q_{9}^{\pm}(x, t) = &&\frac{1}{3} \left(c_2 u-\sqrt{c_2^2 u^2-3 c_2^2 \xi }\right)+c_2 \left(\pm \frac{\sqrt{-u}}{2 \sqrt{2}}\left(\tanh _{p q}\left(\frac{\sqrt{-u} (t \chi +\eta x)}{2 \sqrt{2}}\right)\right.\right.+\\&&\coth _{p q}\left.\left.\left(\frac{\sqrt{-u} (t \chi +\eta x)}{2 \sqrt{2}}\right)\right)\right)^2. \end{eqnarray}$

(3.12)

Case IV: If $u > 0$ and $\xi = \frac{u^{2}}{4}$ , then

$\begin{eqnarray} Q_{10}^{\pm}(x, t) = \frac{1}{3} \left(c_2 u-\sqrt{c_2^2 u^2-3 c_2^2 \xi }\right)+c_2 \left(\pm \frac{\sqrt{u}}{\sqrt{2}}\right)^2 \tan _{p q}\left(\frac{\sqrt{u} (t \chi +\eta x)}{\sqrt{2}}\right)^2, \end{eqnarray}$

(3.13)

$\begin{eqnarray} Q_{11}^{\pm}(x, t) = \frac{1}{3} \left(c_2 u-\sqrt{c_2^2 u^2-3 c_2^2 \xi }\right)+c_2 \left(\pm \frac{\sqrt{u}}{\sqrt{2}}\right)^2 \cot _{p q}\left(\frac{\sqrt{u} (t \chi +\eta x)}{\sqrt{2}}\right)^2, \end{eqnarray}$

(3.14)

$\begin{eqnarray} Q_{12}^{\pm}(x, t) = &&\frac{1}{3} \left(c_2 u-\sqrt{c_2^2 u^2-3 c_2^2 \xi }\right)+c_2 \left(\pm \frac{\sqrt{u}}{\sqrt{2}}\left(\tan _{p q}\left(\sqrt{2} \sqrt{u} (t \chi +\eta x)\right)\right.\right.\\&& \pm \sqrt{p q} \sec _{p q}\left.\left.\left(\sqrt{2} \sqrt{u} (t \chi +\eta x)\right)\right)\right)^2, \end{eqnarray}$

(3.15)

$\begin{eqnarray} Q_{13}^{\pm}(x, t) = &&\frac{1}{3} \left(c_2 u-\sqrt{c_2^2 u^2-3 c_2^2 \xi }\right)+c_2 \left(\pm \frac{\sqrt{u}}{\sqrt{2}}\left(\cot _{p q}\left(\sqrt{2} \sqrt{u} (t \chi +\eta x)\right)\right.\right.\\&&\pm \sqrt{p q} \csc _{p q}\left.\left.\left(\sqrt{2} \sqrt{u} (t \chi +\eta x)\right)\right)\right)^2, \end{eqnarray}$

(3.16)

$\begin{eqnarray} Q_{14}^{\pm}(x, t) = &&\frac{1}{3} \left(c_2 u-\sqrt{c_2^2 u^2-3 c_2^2 \xi }\right)+c_2 \left(\pm \frac{\sqrt{u}}{2 \sqrt{2}}\left(\tan _{p q}\left(\frac{\sqrt{u} (t \chi +\eta x)}{2 \sqrt{2}}\right)\right.\right.+\\&&\cot _{p q}\left.\left.\left(\frac{\sqrt{u} (t \chi +\eta x)}{2 \sqrt{2}}\right)\right)\right)^2. \end{eqnarray}$

(3.17)

3.1. Results and discussion

Mathematical calculations of achieved exact solutions are more proficient and advantageous in analyzing the dynamical behavior of non-linear wave phenomena based upon their graphical depiction. These obtained solutions elaborates different types of soliton solutions. Some of which are presented in 3D, 2D and contour plots with the help of maple. Figure 1 represents bright soliton solution, Figure 2 represents singular soliton solution for Eqs (3.4) and (3.5) respectively. Figures 3 and 5 demonstrate periodic singular soliton solutions for Eqs (3.7) and (3.17). Solution (3.10) represents dark-bright soliton and is plotted in Figure 4.

Figure 1. (a) and (b), 3D and contour graphs of

$Q_{1}^{\pm}(x, t)$ are sketched with

$\eta$ = 0.91,

$\xi = 0, p = 0.98, q = 0.95, u = 0.2,$ for

$-8\leq x\leq 8$ ,

$-8\leq t \leq 8$ respectively. (c) 2D graph with

$\eta$ = 0.91,

$\xi = 0, p = 0.98, q = 0.95, u = 0.2,$ and

$t = 0, 0.2, 0.4, 0.6, 0.8, 1$ for

$-10\leq x \leq 10$ .

DownLoad: Full-Size Img PowerPoint

Figure 2. (a) and (b), 3D and contour graphs of

$Q_{2}^{\pm}(x, t)$ are sketched with

$\eta$ = 0.9,

$\xi = 0, p = 0.98, q = 0.99, u = 0.21$ for

$-10\leq x\leq 10$ ,

$-10\leq t \leq 10$ respectively. (c) 2D graph with

$\eta$ = 0.9,

$\xi = 0, p = 0.98, q = 0.99, u = 0.1,$ and

$t = 0, 0.1, 0.2, 0.3, 0.4, 0.5$ for

$-20\leq x \leq 20$ .

DownLoad: Full-Size Img PowerPoint

Figure 3. (a) and (b), 3D and contour graphs of

$Q_{4}^{\pm}(x, t)$ are sketched with

$\eta$ = 0.91,

$\xi = 0, p = 0.98, q = 0.95, u = -0.2,$ for

$-7\leq x\leq 7$ ,

$-7\leq t \leq 7$ respectively. (c) 2D graph with

$\eta$ = 0.91,

$\xi = 0, p = 0.98, q = 0.95, u = -0.2,$ and

$t = 0, 0.1, 0.14, 0.16, 0.18, 0.2$ for

$-12\leq x \leq 12$ .

DownLoad: Full-Size Img PowerPoint

Figure 4. (a) and (b), 3D and contour graphs of

$Q_{7}^{\pm}(x, t)$ are sketched with

$\eta$ = 0.91,

$\xi = 0.0200, p = 0.98, q = 0.95, u = -0.2,$ for

$-8\leq x\leq 8$ ,

$-8\leq t \leq 8$ respectively. (c) 2D graph with

$\eta$ = 0.91,

$\xi = 0.0200, p = 0.98, q = 0.95, u = -0.2,$ and

$t = 0, 0.01, 0.02, 0.03, 0.04, 0.05$ for

$-15\leq x \leq 15$ .

DownLoad: Full-Size Img PowerPoint

Figure 5. (a) and (b), 3D and contour graphs of

$Q_{14}^{\pm}(x, t)$ are sketched with

$\eta$ = 0.91,

$\xi = 0.0200, p = 0.98, q = 0.95, u = 0.2,$ for

$-8\leq x\leq 8$ ,

$-8\leq t \leq 8$ respectively. (c) 2D graph with

$\eta$ = 0.91,

$\xi = 0.0200, p = 0.98, q = 0.95, u = 0.2,$ and

$t = 0, 0.5, 1, 1.5, 2, 2.5$ for

$-25\leq x \leq 25$ .

DownLoad: Full-Size Img PowerPoint

4. Conclusions

SSET is a realistic, effective, and expressive tool that has been successfully implemented in the Boussinesq equation to extract exact traveling wave solutions that are highly beneficial. The obtained results are in the form of rational, hyperbolic and trigonometric functions. As we can see, this method is powerful, efficient and simple tool for solving various types of nonlinear PDEs found in different models of engineering and natural sciences. The obtained results may be practical, beneficial and can explain the water waves in marine engineering, shallow water with long wavelength, optics, nonlinear grids, coupled circuits, elastic rods and so on. At the end 3D, 2D and contour plots of these solutions are sketched using maple.

Acknowledgments

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University, Abha, Saudi Arabia for funding this work through research groups program under grant number RGP.2/20/43.

Conflict of interest

The authors declare no conflict of interest.

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