Ehteram, Mohammad and Teo, Fang Yenn and Ahmed, Ali Najah and Latif, Sarmad Dashti and Huang, Yuk Feng and Abozweita, Osama and Al-Ansari, Nadhir and El-Shafie, Ahmed (2021) Performance improvement for infiltration rate prediction using hybridized Adaptive Neuro-Fuzzy Inferences System (ANFIS) with optimization algorithms. Ain Shams Engineering Journal, 12 (2). pp. 1665-1676. ISSN 2090-4479, DOI https://doi.org/10.1016/j.asej.2020.08.019.
Full text not available from this repository.Abstract
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 m(3) fin 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. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University.
| Item Type: | Article |
|---|---|
| Funders: | Institute of Postgraduate Studies and Research (IPSR) of Universiti Tunku Abdul Rahman, Malaysia |
| Uncontrolled Keywords: | Infiltration rate; Sine-Cosine Algorithm (SCA); Irrigation process; Adaptive Neuro-Fuzzy Inferences System (ANFIS) |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 24 Jun 2022 02:30 |
| Last Modified: | 24 Jun 2022 02:30 |
| URI: | http://eprints.um.edu.my/id/eprint/34018 |
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