Reference evapotranspiration (ETo) is one of the foremost elements of the hydrology cycle which is essential for water resources management and irrigation applications. The current study is emphasized on the implementation of evolutionary computing models (i.e., gene expression programming (GEP)) for the simulation daily ETo in different locations of Peninsular Malaysia. The ETo models are developed using various input combinations of meteorological variables including air temperature (mean, maximum, and minimum), relative humidity, solar radiation, and mean wind speed. The in situ measurements of the ET are used to validate the model’s performance. The performance of the proposed GEP model is also compared with five well-established empirical formulations (EFs) developed based on the related climatological variability. The attained results evidenced the potential of GEP-derived ETo models in terms of all the statistical measures used. The best GEP model attained when all the meteorological variables are incorporated. However, the study revealed that the use of only temperature information can provide substantial predictability compared to EFs at all the studied stations across Peninsular Malaysia. This confirms the applicability of GEP in simulating ETo with fewer meteorological variables. The major advantage of GEP compared to other black box artificial intelligence algorithms is that GEP provides a set of equations which can be used by practitioners for reliable estimation of ETo at field with a fewer meteorological variable and, thus, can have wide applicability in water resources management.

参考蒸散量（ETo）是水文循环的最重要要素之一，对水资源管理和灌溉应用至关重要。当前的研究重点是在马来西亚半岛不同地区的每日模拟ETo的进化计算模型（即基因表达编程（GEP））的实现上。使用气象变量的各种输入组合来开发ETo模型，包括空气温度（平均，最大和最小），相对湿度，太阳辐射和平均风速。ET的原位测量用于验证模型的性能。还将提出的GEP模型的性能与基于相关气候变化性开发的五种完善的经验公式（EF）进行比较。所获得的结果证明了就所有使用的统计量而言，GEP衍生的ETo模型的潜力。当所有的气象变量都被纳入时，可获得最佳的GEP模型。但是，研究表明，与马来西亚半岛所有研究站点的EF相比，仅使用温度信息就可以提供实质性的可预测性。这证实了GEP在模拟具有较少气象变量的ETo时的适用性。与其他黑匣子人工智能算法相比，GEP的主要优势在于，GEP提供了一组方程式，实践者可以使用这些方程式来可靠地估算具有较小气象变量的野外ETo，因此可以在水资源中广泛应用管理。