Reference Evapotranspiration Estimation With kNN and ANN Models Using Different Climate Input Combinations in the Semi-arid Environment

Year 2021, Volume: 27 Issue: 2, 129 - 137, 04.06.2021

Sevim Seda Yamaç

https://doi.org/10.15832/ankutbd.630303

Cited By: 10

Abstract

The absolute prediction of reference evapotranspiration (ETo) is an important issue for global water balance. Present study demonstrated the performance of k-Nearest Neighbour (kNN) and Artificial Neural Network (ANN) models for prediction of daily ETo using four combinations of climatic data. The kNN and ANN models were studied four combinations of daily climate data during 1996-2015 in the Middle Anatolia region. The findings of ETo estimation with kNN and ANN models were classed with the FAO Penman Monteith equation. The outcomes of ETo values demonstrated that the kNN had higher performances than the ANN in all combinations. The statistical indicators of the kNN model showed ETo values with MSE, RMSE, MAE, NSE and R2 ranging from 0.541-0.031 mm day-1, 0.735-0.175 mm day-1, 0.547-0.124 mm day-1, 0.937-0.997 and 0.900-0.994 in the testing subset. Thus, the kNN can be used for the prediction of reference evapotranspiration with full and limited input meteorological data.

Keywords

Evapotranspiration, Penman-Monteith, k-Nearest Neighbour, Artifical neural network

References

• Ali M H & Shui L T (2009). Potential evapotranspiration model for Muda irrigation project, Malaysia. Water Resources Management 23: 57-69.
• Allen R G, Pereira L S, Raes D & Smith M (1998). Crop Evapotranspiration: Guide Lines for Computing Crop Evapotranspiration. Rome, Italy: FAO Irrigation and Drainage Paper No. 56.
• Antonopoulos V S & Antonopoulos A V (2017). Daily reference evapotranspiration estimates by artificial neural networks technique and empirical equations using limited input climate. Computers and Electronics in Agriculture 132: 86-96.
• Cover T M & Hart P E (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1): 21–27.
• Fan J, Yue W, Wu L, Zhang F, Cai H & Wang X (2018). Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China. Agricultural and Forest Meteorology 263: 225-241.
• Feng Y, Cui N, Zhoa L, Hu X & Gong D (2016). Comparison of ELM, GANN,WNN and emperical models for estimating reference evapotranspiration in humid region of Southwest China. Journal of Hydrology 536: 376-383.
• Feng Y, Cui N, Gong D, Zhang Q & Zhoa L (2017). Evaluation of random forest and generalized regression neural networks for daily reference evapotranspiration modelling. Agricultural Water Management 193: 163-173.
• Ferreira L B, da Cunha F F, de Oliveira R A & Fernandes Filho E I (2019). Estimation of reference evapotranspiration in Brazil with limited meteorological data using ANN and SVM – a new approach. Journal of Hydrology 572: 556-570.
• Gocić M, Motamedi S, Shamshirband S, Petković D, Ch S, Hashim R & Arif M (2015). Soft computing approaches for forecasting reference evapotranspiration. Computers and Electronics in Agriculture 113:164-173.
• Hargreaves G H & Samani Z A (1985). Reference crop evapotranspiration from temperature. Applied Engineering in Agriculture 1: 96-99.
• Khoob A R (2008). Comparative study of Hargreaves’s and artificial neural network’s methodologies in estimating reference evapotranspiration in a semiarid environment. Irrigation Science 26: 253-289.
• Kişi O (2015). Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree. Journal of Hydrology 528: 312-320.
• Kisi O & Çimen M (2009). Evapotranspiration modelling using support vector machines. Hydrological Sciences Journal 54: 918-928.
• Kottek M, Grieser J, Beck C, Rudolf B & Rubel F (2006). World Map of the Köppen-Geiger climate classification updated. Meteorologische Zeitschrift 15: 259-263.
• Kumar M, Raghuwanshi N S & Singh R (2002). Artifical neural networks approach in evapotranspiration modeling: a review. Irrigation Science 128(4): 224-233. Landeras G, Ortiz-Barredo A & López J J (2008). Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain). Agricultural Water Management 95: 553-565.
• Lei Y & Zuo M J (2009). Gear crack level identification based on weighted K nearest neighbour classification algorithm. Mechanical Systems and Signal Processing 23: 1535-1547.
• Lopez-Urrea R, Martin de Santa Olalla F, Fabeiro C & Moratalla A (2006). Testing evapotranspiration equations using lysimeter observations in a semiarid climate. Agricultural Water Management 85: 15-26.
• Pereira L S, Allen R G, Smith M & Raes D (2015). Crop evapotranspiration estimation with FAO56: Past and future. Agricultural Water Management 147: 4-20.
• Tangune B F & Escobedo J F (2018). Reference evapotranspiration in São Paulo State: empirical methods and machine learning techniques. Water Resources and Environmental Engineering 10: 33-44.
• Tabari H, Marofi S, Aeini A, Talaee P H & Mohammadi K (2011). Trend analysis of reference evapotranspiration in the western half of Iran. Agricultural and Forest Meteorology 151: 128-136.
• Torres A F, Walker W R & McKee M (2011). Forecasting daily potential evapotranspiration using machine learning and limited climatic data. Agricultural Water Management 98: 553-562.
• Trabert W (1896). Neue beobachtungen {ü}ber verdampfungsgeschwindigkeiten. Meteorologische Zeitschrift 13: 261-263.
• Traore S, Wang Y-M & Kerh T (2010). Artifical neural network for modelling reference evapotranspiration complex process in Sudano-Sahelian zone. Agricultural Water Management 97: 707-714.
• Wu L, Zhou H, Ma X, Fan J & Zhang F (2019). Daily reference evapotranspiration prediction based on hybridized extreme learning machine model with bio-inspired optimization algorithms: Application in contrasting climates of China. Journal of Hydrology: Regional Studies 577: 123960
• Wu X, Kumar V, Ross Quinlan J, Ghosh J, Yang Q, Motoda H, McLachlan G J, Ng A, Liu B, Yu P S, Zhou Z H, Steinbach M, Hand D J & Steinberg D (2008). Top 10 Algorithms in Data Mining. Knowledge and Information Systems 14: 1–37.