Abstract The present investigation aims to develop symbolic regression based substitute to the FAO56 Penman-Monteith (FAO56-P-M) equation for calculating reference evapotranspiration (ET0) using meteorological data of thirty sites in northeast India. Further, five different input combination models were developed using a symbolic regression (SR) technique. Extraterrestrial radiation(Ra), altitude (Alt), maximum temperature (Tmax), and minimum temperature (Tmin) were used as default input variables in all the developed models. Temperature (T) based model showed high values of performance indices in all the selected stations and is highly dominated by the Ra and Tmin. Temperature and relative humidity (Rh) based (T-Rh) model had reduced error metrics and improved correlation than the T based model, suggesting that T-Rh models have better prediction capability. Temperature and sunshine hour (n) T-n based model had the lowest error metrics and highest correlation among all combinations, suggesting that T-n based model is the best suitable practical alternative to FAO56-P-M. Temperature, sunshine hour, and Rh (T-n-Rh) based model had almost similar prediction ability as found in T-n based model; hence Rh is a reluctant input variable in ET0 modeling. Temperature, sunshine hour, and wind speed (T-n-Vw) based model had the lowest error metrics and highest correlation among all combinations tested, suggesting that T-n-Vw based model is the best suitable alternative to FAO56-P-M. Sensitivity analysis also showed that the T-n-Vw combination has a cent percent positive contribution in controlling the ET0 process. The performance of the T-n-Vw model on T-n based model was found almost similar accuracy, which could be due to minimal influence of wind speed.

中文翻译：

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在印度东北部潮湿气候下使用基于人工智能的函数发现方法开发参考蒸发蒸腾方程

摘要 本调查旨在开发基于符号回归的替代FAO56 Penman-Monteith (FAO56-PM) 方程，以使用印度东北部30 个地点的气象数据计算参考蒸发量(ET0)。此外，使用符号回归 (SR) 技术开发了五种不同的输入组合模型。在所有开发的模型中，地外辐射（Ra）、海拔高度（Alt）、最高温度（Tmax）和最低温度（Tmin）被用作默认输入变量。基于温度 (T) 的模型在所有选定的站点中都显示出较高的性能指标值，并且主要受 Ra 和 Tmin 支配。基于温度和相对湿度 (Rh) 的 (T-Rh) 模型比基于 T 的模型减少了误差指标并提高了相关性，表明 T-Rh 模型具有更好的预测能力。温度和日照时数 (n) 基于 Tn 的模型在所有组合中具有最低的误差指标和最高的相关性，表明基于 Tn 的模型是 FAO56-PM 最合适的实用替代方案。基于温度、日照时和 Rh (Tn-Rh) 的模型与基于 Tn 的模型具有几乎相似的预测能力；因此，Rh 在 ET0 建模中是不情愿的输入变量。在所有测试组合中，基于温度、日照时数和风速 (Tn-Vw) 的模型具有最低的误差指标和最高的相关性，表明基于 Tn-Vw 的模型是最适合替代 FAO56-PM 的模型。敏感性分析还表明，Tn-Vw 组合在控制 ET0 过程中具有百分之一的积极贡献。