###### Abstract  
Accurate rainfall-runoff modeling is crucial for water resource management. However, the available models require more field-measured data to produce accurate results, which has been a long-term issue in hydrological modeling. Machine learning (ML) models have shown superiority in the hydrological field over statistical models. The primary aim of the present study was to advance a new coupled model combining model-driven models and ML models for accurate rainfall-runoff simulation in the Voshmgir basin in northern Iran. Rainfall-runoff data from 2002 to 2007 were collected from the tropical rainfall measuring mission (TRMM) satellite and the Iran water resources management company. The findings revealed that the model-driven model could not fully describe river runoff patterns during the investigated time period. The extreme learning machine and support vector regression models showed similar performances for 1-day-ahead rainfall-runoff forecasting, while the long short-term memory (LSTM) model outperformed these two models. Our results demonstrated that the coupled physically based model and LSTM model outperformed other models, particularly for 1-day-ahead forecasting. The present methodology could be potentially applied in the same hydrological properties catchment. streamflow prediction; data-driven; hybrid modeling; LSTM model +
Footnote †: journal: Applied sciences  
simulations [6; 7; 8; 9]. The HEC-HMS model is applicable for simulating both event-based and continuous-based river runoff through different methods, such as the soil conservation service-curve number (SCS-CN) and deficit and constant-Snyder hydrographs for event-based and continuous-based runoff, respectively. Therefore, simulating river runoff using the HEC-HMS model depends on several input parameters. Data-driven models have conceptual differences in the modeling process [10]. The essence of data-driven models is that they use historical meteorological data to simulate and/or predict river runoff patterns. This aspect makes data-driven models more practical than model-driven models in terms of complexity because they do not require knowledge of hydrological processes. Simulating the river runoff pattern from historical hydrological time series, such as temperature and precipitation, is possible using machine learning (ML) models, viz., the artificial neural network-back propagation (ANN-BP), extreme learning machine (ELM), and support vector regression (SVR) models [11]. [PERSON] and [PERSON] [12] compared the SWAT and long short-term memory (LSTM) models for simulating river runoff, and found that the LSTM model outperformed the SWAT model. Several studies have compared ML models with the HEC-HMS model during typhoon events and have confirmed the superiority of ML models over the HEC-HMS model [13; 14; 15]. Coupled models are manipulated models that use the output of model-driven models as the input of ML models to boost the ML predictor set [16]. [PERSON] and [PERSON] [17] presented a 1-day-ahead river runoff forecasting neuro-wavelet hybrid system at two sites. Other researchers have taken inspiration from their study and recently conducted studies using similar approaches. For example, [PERSON] and [PERSON] [18] used daily rainfall and mean temperature data to simulate daily runoff with the genie rural a 4 parametres journalier (GR4) model and integrated a coupled model by using the calculated time series as the input of the ANN model to the basin in western Turkey. Their results revealed that the coupled model performed better than the GR4] and single ANN models. [PERSON], et al. [19] applied a coupled model comprising two model-driven models, the rural genius model and the water evaluation and planning model (GR2M and WEAP), and an ANN model to simulate flow using meteorological data as input variables. They showed that the coupled model could compute better time series than single models. [PERSON], et al.