Reference evapotranspiration prediction using hybridized fuzzy model with firefly algorithm: Regional case study in Burkina Faso
By Hai Tao, Lamine Diop, A. Bodian, Koffi Djaman, Papa Malick Ndiaye, Zaher Mundher Yaseen. Agricultural Water Management, 2018
164 citations recorded by the source corpus.
Reference Evapotranspiration (ETo) is one of the major components of the hydrological cycle that is very essential in water resources planning, irrigation and drainage management and several other hydrology processes. In irrigation system and design, the prediction of ETo is vital and indispensable for the quantification of crop water needs. This study investigates the capabilities of hybridized fuzzy model with firefly algorithm (ANFIS-FA) for predicting daily reference evapotranspiration over Burkina Faso region. Metrological information at Bobo Dioulasso, Bur Dedougou, and Ouahigouya stations, in Sahelian, Sudano-Sahelian, and Sudanian zone, are used for modelling development. Six different climatic input variable combinations corresponding to 6 models are inspected. The daily Penman-Monteith reference evapotranspiration for the time-period (1998–2012) are used to train and test the models. Several numerical indicators in addition to Taylor diagram are considered to evaluate the performance of the models. Results indicated that the hybrid ANFIS-FA model outperformed the classical ANFIS-based model for all three stations and the model with full inputs climatic data gave the best results. Furthermore, ANFIS-FA is performed the best for Bur Dedougou (Sahalian-Soudanian region) and less at Ouahigouya (sahalian region). In quantitative terms and for instance Bur Dedougou station, ANFIS-FA model increased the prediction accuracy remarkably (SI = 0.043, R2 = 0.97, MAPE = 0.035 and RMSE = 0.24) compared with ANFIS-based model (SI = 0.068, R2 = 0.89, MAPE = 0.037 and RMSE = 0.378). Results revealed the influence of utilizing nature-inspired firefly algorithm to substantially improve performance of the classical ANFIS model optimization for the applied application. Also, the applied modelling strategy can bring a trustful expert intelligent system for predicting reference evapotranspiration at the west desert of Africa.
Subfields: Environmental Engineering, Global and Planetary Change, Water Science and Technology
View Paper: https://www.semanticscholar.org/paper/000e67efcfac2cb9f5c536574cd8baea22097457
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In Global and Planetary Change
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- Masoud Karbasi
- Jan Adamowski
- Ravinesh C. Deo
Citations in the corpus
- Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction (2020, background): “The statistical performance indicators are root mean square error (RMSE), scatter index (SI), coefficient of correlation (COC), and Willmott index (WI) (Willmott 1981; Malik and Kumar 2015, 2020; Tao et al. 2018; Malik et al. 2019a, c, 2020b; Tikhamarine et al. 2019a).”