Computational Stochastic Investigations for the Socio-Ecological Dynamics with Reef Ecosystems  

作　　者：Thongchai Botmart Zulqurnain Sabir Afaf S.Alwabli Salem Ben Said Qasem Al-Mdallal Maria Emilia Camargo Wajaree Weera 

机构地区：[1]Department of Mathematics,Faculty of Science,Khon Kaen University,Khon Kaen 40002,Thailand [2]Department of Mathematical Sciences,United Arab Emirates University,P.O.Box 15551,Al Ain,UAE [3]Department of Mathematics and Statistics,Hazara University,Mansehra,Pakistan [4]Department of Biological Sciences,Rabigh College of Science and Arts,King Abdulaziz University,Jeddah,Saudi Arabia [5]Graduate Program in Administration,Federal University of Santa Maria,Santa Maria 93458,Brazil

出　　处：《Computers, Materials & Continua》2022年第12期5589-5607,共19页计算机、材料和连续体（英文）

基　　金：This project is funded by National Research Council of Thailand(NRCT)and Khon Kaen University:N42A650291。

摘　　要：The motive of this work is to present a computational design using the stochastic scaled conjugate gradient(SCG)neural networks(NNs)called as SCGNNs for the socio-ecological dynamics(SED)with reef ecosystems and conservation estimation.The mathematical descriptions of the SED model are provided that is dependent upon five categories,macroalgae M(v),breathing coral C(v),algal turf T(v),the density of parrotfish P(v)and the opinion of human opinion X(v).The stochastic SCGNNs process is applied to formulate the SEDmodel based on the sample statistics,testing,accreditation and training.Three different variations of the SED have been provided to authenticate the stochastic SCGNNs performance through the statics for training,accreditation,and testing are 77%,12%and 11%,respectively.The obtained numerical performances have been compared with the Runge-Kutta approach to solve the SEDmodel.The reduction of mean square error(MSE)is used to investigate the numericalmeasures through the SCGNNs for solving the SED model.The precision of the SCGNNs is validated through the comparison of the results and the absolute error performances.The reliability of the SCGNNs is performed by using the correlation values,state transitions(STs),error histograms(EHs),MSE measures and regression analysis.

关 键 词：Socio-ecological state conservation estimation neural networks reef ecosystems scaled conjugate gradient numerical study 

分 类 号：X171[环境科学与工程—环境科学]