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Performance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System (ANFIS) with optimization algorithms

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dc.citedby21
dc.contributor.authorEhteram M.en_US
dc.contributor.authorYenn Teo F.en_US
dc.contributor.authorNajah Ahmed A.en_US
dc.contributor.authorDashti Latif S.en_US
dc.contributor.authorFeng Huang Y.en_US
dc.contributor.authorAbozweita O.en_US
dc.contributor.authorAl-Ansari N.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorid57113510800en_US
dc.contributor.authorid35249518400en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid57216081524en_US
dc.contributor.authorid55807263900en_US
dc.contributor.authorid57219806365en_US
dc.contributor.authorid51664437800en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:07:34Z
dc.date.available2023-05-29T09:07:34Z
dc.date.issued2021
dc.descriptionFuzzy neural networks; Infiltration; Irrigation; Particle swarm optimization (PSO); Water management; Adaptive neuro-fuzzy inference; Cross sectional area; Infiltration opportunities; Infiltration process; Irrigation management; Optimization algorithms; Sine-cosine algorithm; Sustainable management; Fuzzy inferenceen_US
dc.description.abstractThe infiltration process during irrigation is an essential variable for better water management and hence there is a need to develop an accurate model to estimate the amount infiltration water during irrigation. However, the fact that the infiltration process is a highly non-linear procedure and hence required special modeling approach to accurately mimic the infiltration procedure. Therefore, the ability of Adaptive Neuro-Fuzzy Interface System (ANFIS) models in estimating infiltrated water during irrigation in the furrow for sustainable management is proposed. The main innovation of current research is the first attempt to employ the ANFIS model for predicating infiltration rates, in addition, integrate the ANFIS model with three new optimization algorithms. Three optimizing algorithms, viz. Sine Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), and Firefly Algorithm (FFA) were used to tune the ANFIS-parameters. Experimental data from six different studies in different countries have been used in this study to validate the proposed model. The inflow rate, furrow length, infiltration opportunity time, cross-sectional area, and waterfront advance time have been utilized as the input parameters. The results indicated that the ANFIS-SCA could provide a better estimation for the infiltration rate compared to ANFIS-PSO. The Mean Absolute Error (MAE) and Percent Bias (PBIAS) errors computed for the ANIFS-SCA (0.007 m3/m and 0.12) was significantly better than those achieved from the ANFIS-FFA and the ANFIS-PSO In addition to that, ANIFS-SCA model outperformed ANFIS-FFA with high level of accuracy. The proposed Hybrid ANFIS-SCA showed outstanding performance over the other optimizer algorithms in estimating the infiltration rate and could be applied in different irrigation systems for better sustainable irrigation management. � 2020 THE AUTHORSen_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.asej.2020.08.019
dc.identifier.epage1676
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85095589392
dc.identifier.spage1665
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85095589392&doi=10.1016%2fj.asej.2020.08.019&partnerID=40&md5=84884935712a072b59a1bf8a6f6d3ce4
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26189
dc.identifier.volume12
dc.publisherAin Shams Universityen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleAin Shams Engineering Journal
dc.titlePerformance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System (ANFIS) with optimization algorithmsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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