Vector Autoregression Model-Based Forecasting of Reference Evapotranspiration in Malaysia

Phon Sheng Hou, Lokman Mohd Fadzil (), Selvakumar Manickam and Mahmood A. Al-Shareeda ()
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Phon Sheng Hou: National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, Penang 11800, Malaysia
Lokman Mohd Fadzil: National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, Penang 11800, Malaysia
Selvakumar Manickam: National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, Penang 11800, Malaysia
Mahmood A. Al-Shareeda: National Advanced IPv6 Centre (NAv6), Universiti Sains Malaysia, Penang 11800, Malaysia

Sustainability, 2023, vol. 15, issue 4, 1-18

Abstract: Evapotranspiration is one of the hydrological cycle’s most important elements in water management across economic sectors. Critical applications in the agriculture domain include irrigation practice improvement and efficiency, as well as water resource preservation. The main objective of this research is to forecast reference evapotranspiration using the vector autoregression (VAR) model and investigate the meteorological variables’ causal relationship with reference evapotranspiration using a statistical approach. The acquired 20-year, 1-year, and 2-month research climate datasets from Penang, Malaysia, were split into 80% training data and 20% validation data. Public weather data are used to train the initial VAR model. A Raspberry Pi IoT device connected to a DHT11 temperature sensor was outfitted at the designated experimental crop site. In situ data acquisition was done using DHT11 temperature sensors to measure the ambient temperature and humidity. The collected temperature and humidity data were used in conjunction with the vector autoregression (VAR) model to calculate the reference evapotranspiration forecast. The results demonstrated that the 20-year dataset showed better performance and consistent results in forecasting general reference evapotranspiration, derived using root mean square error (RMSE) and correlation coefficient (CORR) of 1.1663 and −0.0048, respectively. As for the 1-year dataset model, RMSE and CORR were recorded at 1.571 and −0.3932, respectively. However, the 2-month dataset model demonstrated both positive and negative performance due to seasonal effects in Penang. The RMSE ranged between 0.5297 to 2.3562 in 2020, 0.8022 to 1.8539 in 2019, and 0.8022 to 2.0921 in 2018. As for CORR, it ranged between −0.5803 to 0.2825 in 2020, −0.3817 to 0.2714 in 2019, and −0.3817 to 0.2714 in 2018. In conclusion, the model tested using 20-year, 1-year, and 2-month meteorological datasets for estimating reference evapotranspiration ( E T 0 ) based on smaller RMSEs demonstrates better performance at predicting the true values, as well as producing both positive and negative CORR performance due to seasonal variations in Penang.

Keywords: forecasting reference evapotranspiration; vector autoregression (VAR); machine learning; Malaysia climate (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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