Soil moisture (SM) is of paramount importance in irrigation scheduling, infiltration, runoff, and agricultural drought monitoring. This work aimed at evaluating the performance of the classical ANFIS (Adaptive Neuro-Fuzzy Inference System) model as well as ANFIS coupled with three bio-inspired metaheuristic optimization methods including whale optimization algorithm (ANFIS-WOA), krill herd algorithm (ANFIS-KHA) and firefly algorithm (ANFIS-FA) in estimating SM. Daily air temperature, relative humidity, wind speed and sunshine hours data at Istanbul Bolge station in Turkey and soil temperature values measured over 2008–2009 were fed into the models under six different scenarios. ANFIS-WOA (RMSE = 1.68, MAPE = 0.04) and ANFIS (RMSE = 2.55, MAPE = 0.07) exhibited the best and worst performance in SM estimation, respectively. All three hybrid models (ANFIS-WOA, ANFIS-KHA and ANFIS-FA) improved SM estimates, reducing RMSE by 34, 28 and 27% relative to the base ANFIS model, respectively. A more detailed analysis of model performances in estimating moisture content over three intervals including [15–25), [25–35) and ≥35% revealed that ANFIS-WOA has had the lowest errors with RMSEs of 1.69, 1.89 and 1.55 in the three SM intervals, respectively. From the perspective of under- or over-estimation of moisture values, ANFIS-WOA (RMSE = 1.44, MAPE = 0.03) in under-estimation set and ANFIS-KHA (RMSE = 1.94, MAPE = 0.05) in over-estimation set showed the highest accuracies. Overall, all three hybrid models performed better in the underestimation set compared to overestimation set.

土壤水分 ( SM ) 在灌溉计划、入渗、径流和农业干旱监测中至关重要。这项工作旨在评估经典ANFIS（自适应神经模糊推理系统）模型以及ANFIS与三种仿生元启发式优化方法的性能，包括鲸鱼优化算法（ANFIS-WOA）、磷虾群算法（ANFIS-KHA） ) 和萤火虫算法 (ANFIS-FA) 估计SM。土耳其伊斯坦布尔博尔格站的每日气温、相对湿度、风速和日照时数数据以及 2008-2009 年测量的土壤温度值被输入六种不同情景下的模型。ANFIS-WOA ( RMSE  = 1.68, MAPE  = 0.04) 和 ANFIS (RMSE  = 2.55，MAPE  = 0.07）分别在SM估计中表现出最好和最差的性能。所有三种混合模型（ANFIS-WOA、ANFIS-KHA 和 ANFIS-FA）都改进了SM估计，相对于基本 ANFIS 模型，RMSE 分别降低了 34%、28% 和 27%。对模型性能在三个区间（包括 [15-25)、[25-35) 和 ≥35% 的估计水分含量的更详细分析表明，ANFIS-WOA 的误差最低，RMSE分别为 1.69、1.89 和 1.55。三个SM间隔，分别。从水分值低估或高估的角度来看，ANFIS-WOA ( RMSE  = 1.44, MAPE = 0.03) 在低估集和 ANFIS-KHA ( RMSE  = 1.94, MAPE  = 0.05) 在高估集显示出最高的准确度。总体而言，与高估集相比，所有三种混合模型在低估集上的表现都更好。