This study aimed to forecast dam inflows and subsequently predict its capability in producing HEPP using machine learning and evolutionary optimization techniques. Mahabad Dam, located in the northwest of Iran and recognized as one of the nation’s key dams, served as a case study. First, artificial neural networks (ANN) and support vector regression (SVR) were employed to predict dam inflows, with optimization of parameters achieved through Harris hawks optimization (HHO), a robust optimization technique. The data of temperature, precipitation, and dam inflow over a 24-year period on a monthly basis, incorporating various lag times, were used to train these machines. Then, HEPP from the dam was predicted using temperature, precipitation, dam inflow, and dam evaporation as input variables. The models were applied to data covering the years 2000 to 2020. The results of the first part indicated both hybrid models (HHO-ANFIS and HHO-SVR) improved the prediction performance compared to the single models. Based on the results of Taylor’s diagram and the error evaluation criteria, the HHO-ANFIS hybrid model (RMSE, MAE, and NSE of 3.90, 2.41, and 0.86, respectively) exerted better performance than HHO-SVR (RMSE, MAE, and NSE of 4.39, 2.70, and 0.86, respectively). The results of the second part showed that using the HHO algorithm to optimize single models (RMSE, MAE, and NSE of 0.2, 10, and 0.90, respectively) predicted HEPP better than single models (RMSE, MAE, and NSE of 0.2, 10, and 0.90, respectively). The results of Taylor’s diagram also showed that the HHO-ANFIS model exerted better performance. The findings of this study indicated the promising performance of machine learning models optimized by metaheuristic algorithms in the simultaneous prediction of dam inflows and HEPP in multi-purpose dams for better management and allocation of surface water resources.

本研究旨在预测大坝流入量，并随后使用机器学习和进化优化技术预测其生产 HEPP 的能力。马哈巴德大坝位于伊朗西北部，被认为是该国的重要水坝之一，是一个案例研究。首先，采用人工神经网络（ANN）和支持向量回归（SVR）来预测大坝流入量，并通过哈里斯鹰优化（HHO）（一种稳健的优化技术）实现参数优化。使用 24 年期间每月的温度、降水量和大坝入水数据以及各种滞后时间来训练这些机器。然后，使用温度、降水、大坝流入量和大坝蒸发作为输入变量来预测大坝的 HEPP。这些模型适用于 2000 年至 2020 年的数据。第一部分的结果表明，与单一模型相比，两种混合模型（HHO-ANFIS 和 HHO-SVR）都提高了预测性能。基于泰勒图的结果和误差评估标准，HHO-ANFIS混合模型（RMSE、MAE和NSE分别为3.90、2.41和0.86）比HHO-SVR（RMSE、MAE和NSE分别为3.90、2.41和0.86）发挥了更好的性能分别为 4.39、2.70 和 0.86）。第二部分的结果表明，使用HHO算法优化单个模型（RMSE、MAE和NSE分别为0.2、10和0.90）预测HEPP优于单个模型（RMSE、MAE和NSE分别为0.2、10） ，和0.90，分别）。泰勒图的结果也表明HHO-ANFIS模型发挥了更好的性能。 本研究的结果表明，通过元启发式算法优化的机器学习模型在同时预测大坝流入量和多用途水坝的 HEPP 方面具有良好的性能，可以更好地管理和分配地表水资源。