ANNs, SVM and empirical methods for modelling reference evapotranspiration with limited climatic data in the city of Xai-Xai, Mozambique / ANNs, SVM e métodos empíricos para modelar a evapotranspiração de referência com dados climáticos limitados na cidade de Xai-Xai, Moçambique
2022; Brazilian Journal of Development; Volume: 8; Issue: 1 Linguagem: Inglês
10.34117/bjdv8n1-310
ISSN2525-8761
AutoresBartolomeu Félix Tangune, Egas José Armando, Catine António Chimene, Inácio M. Cipriano, Titki Djoal Tarassoum,
Tópico(s)Solar Radiation and Photovoltaics
ResumoReference evapotranspiration (ETo) is useful for water management, calculating crop water requirements and irrigation scheduling. ETo was estimated from 5 empirical methods based on temperature, 5 based on solar radiation and on Machine Learning Technique (MLT). The MLT model consisted of Artificial Neural Networks (ANNs) and Support Vector Machine (SVM), with 6 architectures each. The MLT and empirical methods were tested against the Penman Monteith FAO 56 method based on the following statistical parameters: MBE (Mean Bias Error), RMSE (Mean Square Root Error), d (coefficient of Willmott) and R2 (coefficient of determination). The meteorological data used (maximum temperature, low and average temperature, relative humidity, wind speed and sunshine hours: n) were obtained from the National Institute of Meteorology of Mozambique. The results obtained from the modeling showed the following: Jones and Ritchie (JRICH) > Makkink, SVM3 > SVM6 > SVM1 > SVM2 = SVM4 > SVM5 > ANN5> Abtew > Hargreaves – Samani > ANN1 = ANN6 > ANN4 > Irmak > ANN3 > Jensen Haise > ANN2 > Blaney Criddle Original > Schendel > Kharrufa > Mc Guinness-Bordne. Global solar radiation, which is one of the variables needed for the JRICH method (MBE = -0.17 mm d-1; RMSE = 0.38 mm d-1; d = 0.98 and R2 = 0.98) is not always measured or calculated. In this case, SVM1could be used since it only requires measurements of T (MBE = 0.16 mm d-1; RMSE = 0.62 mm d-1; d = 0.94 and R2 = 0.83).
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