Among several complex hydrological process elements, Evapotranspiration (ET) is the most complex one. Estimation of ET is very challenging compared to other hydrological variables as it depends on complex interactions of several hydrometeorological variables. In the current research, the estimation of daily ET from maximum and minimum temperature was established. For this purpose, Dynamic Evolving Neural-Fuzzy Inference System (DENFIS) and Multivariate Adaptive Regression Spline (MARS) were hybridized with two advanced metaheuristic optimization algorithms [i.e., Whale Optimization Algorithm (WOA) and Bat Algorithm (BA)]. Daily ET and temperature data estimated at 3 locations in the coastal region of southwest Bangladesh for the period 2005–2016 were used to develop and validate the models. The results showed a good performance of DENFIS-WOA model with minimum values of normalized root mean square error (NRMSE = 0.35–0.54) in estimating ET using only temperature in the complex climatic setup of southwest Bangladesh. DENFIS-BA also showed reasonable performance (NRMSE = 0.43–0.62), while the performance of MARS–WOA (NRMSE = 0.54–0.97) and MARS-BA (0.60–1.13) was found satisfactory in terms of most of the statistical indices. Obtained results were also evaluated using innovative visual presentations of model outputs, which revealed the better capability of only DENFIS-WOA in estimating mean, variability and distribution of ET for all the months and locations. The results indicate the potential of DENFIS-WOA to be used for reliable estimation of daily ET from the temperature in a tropical humid coastal region.

在几个复杂的水文过程要素中，蒸散（ET）是最复杂的。与其他水文变量相比，ET 的估计非常具有挑战性，因为它取决于几个水文气象变量的复杂相互作用。在目前的研究中，建立了从最高和最低温度估算每日 ET 的方法。为此，动态演化神经模糊推理系统 (DENFIS) 和多元自适应回归样条 (MARS) 与两种高级元启发式优化算法 [即鲸鱼优化算法 (WOA) 和蝙蝠算法 (BA)] 混合。2005-2016 年期间在孟加拉国西南部沿海地区 3 个地点估计的每日 ET 和温度数据用于开发和验证模型。结果表明，在孟加拉国西南部复杂的气候设置中，仅使用温度估计 ET 时，DENFIS-WOA 模型的性能良好，归一化均方根误差最小值（NRMSE = 0.35-0.54）。DENFIS-BA 也表现出合理的性能（NRMSE = 0.43-0.62），而 MARS-WOA（NRMSE = 0.54-0.97）和 MARS-BA（0.60-1.13）的性能在大多数统计指标方面都令人满意。还使用模型输出的创新视觉呈现对获得的结果进行了评估，这表明只有 DENFIS-WOA 在估计所有月份和地点的 ET 平均值、变异性和分布方面具有更好的能力。结果表明 DENFIS-WOA 可用于根据热带潮湿沿海地区的温度可靠估算每日 ET。