Precisely forecasting air temperature as a significant meteorological parameter has a critical role in environment quality management. Hence, this study employs a hybrid intelligent model for accurately monthly temperature forecasting for one to three times ahead in the hottest and coldest regions of the world. The hybrid model contains the artificial neural network (ANN) hybridized with the powerful hetaeristic Honey Badger Algorithm (HBA-ANN). The average mutual information (AMI) technique is employed to find the optimal time delay values for the temperature variable for different time horizons. Finally, the performance of the developed hybrid model is compared with the classical ANN and the Gene Expression Programming (GEP) using some statistical criteria, and the Taylor and scatter diagrams. Results indicated that in each time horizon, the HBA-ANN model with the lowest distance from observation points based on Taylor diagram, high values for NSE and R², and low values for RMSE, MAE, and RSR outperformed the ANN and GEP models in both training and testing phases. Hence, using the Honey Badger Algorithm could increase the accuracy of the model. This model's precise performance supports the case for it to be employed to forecast other environmental parameters.

准确预报气温作为重要的气象参数在环境质量管理中具有关键作用。因此，本研究采用混合智能模型，对世界最热和最冷地区的每月温度进行精确预测，提前一到三倍。混合模型包含人工神经网络 (ANN) 与强大的异构蜜獾算法 (HBA-ANN) 混合。采用平均互信息 (AMI) 技术为不同时间范围的温度变量找到最佳时间延迟值。最后，使用一些统计标准以及泰勒和散点图将开发的混合模型的性能与经典 ANN 和基因表达编程 (GEP) 进行比较。结果表明，在每个时间范围内，²以及 RMSE、MAE 和 RSR 的低值在训练和测试阶段都优于 ANN 和 GEP 模型。因此，使用 Honey Badger 算法可以提高模型的准确性。该模型的精确性能支持将其用于预测其他环境参数的情况。