Accurate reservoir inflow predictions are critical for effective flood control and optimizing hydropower generation, thereby enhancing water resource management. This study introduces an advanced hydrological modeling approach that leverages a basic recurrent neural network (bRNN), convolutional neural network (CNN) with gated recurrent units (GRU) (bRNN-CNN-GRU), GRU with long short-term memory (LSTM) (GRU-LSTM) hybrid models, and deep neural network (DNN) to predict daily reservoir inflow at the Sefid Roud Dam. By utilizing historical data from 2018 to 2024, this study examined the following two multivariate scenarios: one incorporating water parameters such as water level, evaporation, and temperature extremes, and another focused solely on inflow delays. Training and testing sets were created from the dataset, with 80% for training and 20% for testing. For benchmarking purposes, the performance of the bRNN-CNN-GRU was evaluated against a deep neural network (DNN) and a GRU-LSTM hybrid. The evaluation metrics used were root mean square error (RMSE), correlation coefficient (r), and Nash Sutcliffe coefficient (NSE). Results demonstrated that, while all models performed better under the scenario incorporating inflow delays, the bRNN-CNN-GRU model achieved the best performance, with an RMSE of 0.71, r of 0.97, and NSE of 0.95, outperforming both the DNN and GRU-LSTM models. These findings highlight the significant advancements in hydrological modeling and affirm the applicability of the bRNN-CNN-GRU model for improved reservoir management in diverse settings.

Predicting dam inflow is critical for human life safety, water resource management, and hydroelectric power generation. While machine learning (ML) models address complex, nonlinear hydrological problems, quantum machine learning (QML) offers greater potential to overcome classical computational limits. This study compares a hybrid quantum neural network (HQNN) with the following two classical models: bidirectional CNN-LSTM and support vector regression (SVR). These models were evaluated to predict monthly inflow to the Mile Mughan Dam, a transboundary hydroelectric and irrigation dam located on the Aras River between Azerbaijan and Iran, using a 14-year dataset (2010–2023) under two scenarios. In total, 70% of data was used for training and 30% for testing. The first scenario encompassed meteorological variables plus three months of inflow lags, and the second included inflow lags only. Model performance was assessed using Coefficient of Determination (R²), Root Mean Squared Error (RMSE), Nash–Sutcliffe efficiency (NSE), Mean Absolute Percentage Error (MAPE), and graphical plots. HQNN showed superior performance across all metrics. In Scenario 1, HQNN achieved R² = 0.915, RMSE = 37.318 MCM, NSE = 0.908, MAPE = 8.343%; CNN-BiLSTM had R² = 0.867, RMSE = 46.506 MCM, NSE = 0.858, MAPE = 10.795%; SVR had R² = 0.846, RMSE = 52.372 MCM, NSE = 0.821, MAPE = 12.772%. In Scenario 2, HQNN maintained strong performance (R² = 0.855, RMSE = 48.56 MCM, NSE = 0.845, MAPE = 9.979%) and outperformed CNN-BiLSTM (R² = 0.810, RMSE = 56.126 MCM, NSE = 0.793, MAPE = 11.456%) and SVR (R² = 0.801, RMSE = 60.336 MCM, NSE = 0.761, MAPE = 12.901%). In Scenario 1 and Scenario 2, HQNN increased the prediction accuracy by 19.76% and 13.47%, respectively, compared to the CNN-BiLSTM model. These results confirm HQNN’s reliability in both multivariate and univariate modeling.