Performance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System (ANFIS) with optimization algorithms
Fuzzy 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;...
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Ain Shams University
2023
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my.uniten.dspace-261892023-05-29T17:07:34Z Performance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System (ANFIS) with optimization algorithms Ehteram M. Yenn Teo F. Najah Ahmed A. Dashti Latif S. Feng Huang Y. Abozweita O. Al-Ansari N. El-Shafie A. 57113510800 35249518400 57214837520 57216081524 55807263900 57219806365 51664437800 16068189400 Fuzzy 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 inference The 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 AUTHORS Final 2023-05-29T09:07:34Z 2023-05-29T09:07:34Z 2021 Article 10.1016/j.asej.2020.08.019 2-s2.0-85095589392 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85095589392&doi=10.1016%2fj.asej.2020.08.019&partnerID=40&md5=84884935712a072b59a1bf8a6f6d3ce4 https://irepository.uniten.edu.my/handle/123456789/26189 12 2 1665 1676 All Open Access, Gold, Green Ain Shams University Scopus |
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Fuzzy 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 inference |
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57113510800 |
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57113510800 Ehteram M. Yenn Teo F. Najah Ahmed A. Dashti Latif S. Feng Huang Y. Abozweita O. Al-Ansari N. El-Shafie A. |
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Article |
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Ehteram M. Yenn Teo F. Najah Ahmed A. Dashti Latif S. Feng Huang Y. Abozweita O. Al-Ansari N. El-Shafie A. |
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Ehteram M. Yenn Teo F. Najah Ahmed A. Dashti Latif S. Feng Huang Y. Abozweita O. Al-Ansari N. El-Shafie A. Performance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System (ANFIS) with optimization algorithms |
| author_sort |
Ehteram M. |
| title |
Performance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System (ANFIS) with optimization algorithms |
| title_short |
Performance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System (ANFIS) with optimization algorithms |
| title_full |
Performance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System (ANFIS) with optimization algorithms |
| title_fullStr |
Performance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System (ANFIS) with optimization algorithms |
| title_full_unstemmed |
Performance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System (ANFIS) with optimization algorithms |
| title_sort |
performance improvement for infiltration rate prediction using hybridized adaptive neuro-fuzzy inferences system (anfis) with optimization algorithms |
| publisher |
Ain Shams University |
| publishDate |
2023 |
| _version_ |
1806426044330147840 |
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13.223943 |