Resolving data-hungry nature of machine learning reference evapotranspiration estimating models using inter-model ensembles with various data management schemes.

Over the past decade, there has been an increasing research on the use of machine learning tools for estimating reference crop evapotranspiration (ET o). However, due to the data-hungry nature of the machine learning models, all of these researches are not suitable for regions with limited data supply. This study aims to provide a breakthrough for the bottleneck through coupling of the inter-model ensemble with various data management schemes. The Bayesian modeling approach and a non-linear neural ensemble based inter-model ensemble (BMA-E and NNE-E) were developed locally with data from five different meteorological stations in the Peninsular Malaysia. The NNE-E was found to be highly robust spatially, whereby it can be used to estimate daily ET o accurately at other stations, even though with reduced input meteorological parameters. However, the performances of the locally trained models were found wanting and were fluctuating violently. This was resolved through creating a data pool that include the data from all stations and developing a universal NNE. By following the proposed scheme of things, the daily ET o can be easily estimated across the whole Peninsular Malaysia. This being, without the need for historical data and new models at estimation site. • Inter-model ensembles trained using exogenous data solve the data-hungry issues. • Bayesian modeling approach (BMA) and non-linear neural ensemble (NNE) were applied. • Black-box WOA-ELM-E model offered better spatial robustness than BMA-E model. • Use of global exogenous data pool further enhanced the ensemble's performance. [ABSTRACT FROM AUTHOR]