In water resource studies, long‐term measurements of river streamflow are essential. They allow us to observe trends and natural cycles and are prerequisites for hydraulic and hydrology models. This paper presents a new application of the stage‐discharge rating curve model introduced by Maghrebi et al. (2016) to estimate continuous streamflow along the Gono River, Japan. The proposed method, named single stage‐discharge (SSD) method, needs only one observed data to estimate the continuous streamflow. However, other similar methods require more than one observational data to fit the curve. The results of the discharge estimation by the SSD are compared with the improved fluvial acoustic tomography system (FATS), conventional rating curve (RC), and flow‐area rating curve (FARC). Some statistical indicators, such as the coefficient of determination (R2), root mean square error (RMSE), percent bias (PBAIS), mean absolute error (MAE), and Kling‐Gupta efficiency (KGE), are used to assess the performance of the proposed model. ADCP data are used as a benchmark for comparing four studied models. As a result of the comparison, the SSD method outperformed of FATS method. Also, the three studied RC methods were highly accurate at estimating streamflow if all observed data were used in calibration. However, if the observed data in calibration was reduced, the SSD method by R2 = 0.99, RMSE = 2.83 (m3/s), PBIAS = 0.715(%), MAE = 2.30 (m3/s), and KGE = 0.972 showed the best performance compared to other methods. It can be summarized that the SSD method is the feasible method in the data‐scarce region and delivers a strong potential for streamflow estimation.
One of the essential phases in water resource planning and management is streamflow forecast. It is necessary for the functioning of hydropower plants, agricultural planning, and flood control. The present study applied Artificial Neural Network (ANN) model, Adaptive Neuro-Fuzzy Inference System (ANFIS), Bidirectional LSTM (BiLSTM), and hybrid Convolutional Neural Network (CNN) Gated Recurrent Unit (GRU) Long-Short Term Memory (LSTM) model to predict the long-term daily streamflow in the Colorado River, USA. 60% of the data (1921–1981) was used for training, while 40% of the data (1981–2021) was utilized for testing the model's performance. The obtained outcomes of the suggested models were assessed using four assessment indices, including by Normalized Root Mean Square Error (NRMSE), Mean Absolute Error (MAE), Correlation Coefficient (r), and Nash–Sutcliffe Coefficient (ENS). Based on the comparison of outputs, in the testing phase, it was determined that the ANFIS model with NRMSE = 0.116, MAE = 24.66, r = 0.968, and ENS = 0.936 outperformed the other studied models in terms of reliability and accuracy. While the CNN-GRU-LSTM and BiLSTM models are complex, they do not perform better. The comparison demonstrates that the performance of their respective models is not much better than the two standard models-ANN and ANFIS.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.