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.

在过去的十年中，越来越多的研究使用机器学习工具来估计参考作物蒸散量 (ET _o )。然而，由于机器学习模型的数据饥渴性质，所有这些研究都不适合数据供应有限的地区。本研究旨在通过模型间集成与各种数据管理方案的耦合来突破瓶颈。贝叶斯建模方法和基于非线性神经集合的模型间集合（BMA-E 和 NNE-E）是在本地开发的，数据来自马来西亚半岛的五个不同气象站。发现 NNE-E 在空间上具有高度稳健性，因此可用于估计每日 ET _o即使输入的气象参数减少了。然而，发现本地训练模型的性能不佳并且波动剧烈。这是通过创建一个包含所有站点数据的数据池和开发通用 NNE 来解决的。通过遵循提议的方案，可以轻松估算整个马来西亚半岛的每日 ET _o。这是，无需在估计站点的历史数据和新模型。