Comparison of the performance of SWAT, IHACRES and artificial neural networks models in rainfall-runoff simulation (case study: Kan watershed, Iran)

Ahmadi, Mehdi; Moeini, Abolfazl; Ahmadi, Hassan; Motamedvaziri, Baharak; Zehtabiyan, Gholam Reza

doi:10.1016/j.pce.2019.05.002

Cited by 46 publications

(23 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results suggest that the ANN model had greater performance (higher NSE and R 2 ) than SWAT for the entire simulation period. This result is similar to those obtained by Tiwari (2017), Jimeno-Sáez et al (2018) and Ahmadi et al (2019), which observed a better performance of the ANN model than the SWAT model, considering both R 2 and NSE coefficients.…”

Section: Comparison Of Model Performancesupporting

confidence: 92%

“…Koycegiz and Buyukyildiz (2019), evaluating the ANN and SWAT models to simulate the streamflow at the headwater of Çarsamba River, located at the Konya Closed Basin, Turkey, reported that the ANN model was more successful than the SWAT model. Ahmadi et al (2019), evaluating the ANN and SWAT models to simulate the daily, monthly, and annual streamflows for the Kan Watershed, Iran, reported that the ANN model performed better in all streamflow simulations. However, when evaluating the literature, studies with diferente results are also found.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Hydrological modeling in a basin of the Brazilian Cerrado biome

Rodrigues¹,

Andrade²,

Viola³

et al. 2021

Rev. ambiente água

View full text Add to dashboard Cite

The Brazilian Cerrado biome (BCB) is among 25 biodiversity hotspots identified worldwide, and covers the recharge area of important aquifers and rivers in South America. The increase in deforestation has been threatening water availability in this region. In order to assist in the water-resource management of the BCB, this study models the daily streamflow in a basin of the Cerrado, using two approaches: a process-based model (Soil and Water Assessment Tool - SWAT) and the data-driven model (Artificial Neural Network - ANN). The performance of the models was evaluated by the Nash-Sutcliffe coefficient (NSE), coefficient of determination (R2) and flow-duration-curves (FDC). The results indicate that SWAT (NSE > 0.61; R2 > 0.68) and ANN (NSE > 0.91; R2 > 0.79) models are suitable tools in daily streamflow modeling of the studied basin, with the ANN model being the most accurate. Based on FDC, the ANN model was also better than the SWAT model for all frequencies evaluated. Thus, the ANN model is a promising new approach for daily streamflow modelling in this region. Moreover, the results of this study can help water-resource managers in planning and implementing appropriate water allocation and conservation measures in the Brazilian Cerrado biome.

show abstract

Section: Comparison Of Model Performancesupporting

confidence: 92%

mentioning

confidence: 99%

Hydrological modeling in a basin of the Brazilian Cerrado biome

Rodrigues¹,

Andrade²,

Viola³

et al. 2021

Rev. ambiente água

View full text Add to dashboard Cite

show abstract

“…Wu et al (2005) developed an artificial neural network (ANN) model and successfully applied it to short-term flow forecasting. Nanda et al (2016) used a linear autoregressive moving average model to forecast floods with a forecast period of 1 d-3 days Ahmadi et al, 2019 used ANN models on a daily, monthly, and annual basis in the Kan watershed, which is located in western Tehran, Iran. Certain data-driven forecasting methods, such as the ANN, adaptive-networkbased fuzzy inference system, and support vector machine methods assume that streamflow series are stable, which contradicts reality and causes the simulated value to deviate from the observed one (Adamowski et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Daily Runoff Forecasting Using Ensemble Empirical Mode Decomposition and Long Short-Term Memory

et al. 2021

View full text Add to dashboard Cite

Hydrological series data are non-stationary and nonlinear. However, certain data-driven forecasting methods assume that streamflow series are stable, which contradicts reality and causes the simulated value to deviate from the observed one. Ensemble empirical mode decomposition (EEMD) was employed in this study to decompose runoff series into several stationary components and a trend. The long short-term memory (LSTM) model was used to build the prediction model for each sub-series. The model input set contained the historical flow series of the simulation station, its upstream hydrological station, and the historical meteorological element series. The final input of the LSTM model was selected by the MI method. To verify the effect of EEMD, this study used the Radial Basis Function (RBF) model to predict the sub-series, which was decomposed by EEMD. In addition, to study the simulation characteristics of the EEMD-LSTM model for different months of runoff, the GM(group by month)-EEMD-LSTM was set up for comparison. The key difference between the GM-EEMD-LSTM model and the EEMD-LSTM model is that the GM model must divide the runoff sequence on a monthly basis, followed by decomposition with EEMD and prediction with the LSTM model. The prediction results of the sub-series obtained by the LSTM and RBF exhibited better statistical performance than those of the original series, especially for the EEMD-LSTM. The overall GM-EEMD-LSTM model performance in low-water months was superior to that of the EEMD-LSTM model, but the simulation effect in the flood season was slightly lower than that of the EEMD-LSTM model. The simulation results of both models are significantly improved compared to those of the LSTM model.

show abstract

“…They reported that the Nash-Sutcliffe coefficient (NS) of the model is 0.5 in the calibration phase and 0.49 in the validation phase. Ahmadi et al (2019) compared IHACRES, SWAT, and artificial neural networks (ANNs) and reported that IHACRES has acceptable performance in modeling daily, monthly, and annual flows; however, their results showed that ANNs outperform IHACRES and SWAT. Choubin et al (2019) calibrated and validated the IHACRES model in four gauged basins in Iran and reported that the NS of the models range between 0.57 to 0.71 in the calibration phase and 0.53 to 0.62 in the validation phase, suggesting that the performance of the model is good or satisfactory.…”

Section: Introductionmentioning

confidence: 99%

Integrating IHACRES with a data-driven model to investigate the possibility of improving monthly flow estimates

et al. 2021

View full text Add to dashboard Cite

Estimating the outflow of basins is a critical step in surface water resources planning and management, especially in basins that lack reliable long-term observed data of streamflow. Hydrological models, which can simulate the process of rainfall-runoff, can be used to obtain reliable estimates of streamflow from precipitation data and the physical characteristics of basins. The focus of the present study was to estimate the outflow of 19 sub-basins located in Guilan Province, northern Iran. To achieve this, hybrid models were developed by integrating the IHACRES (identification of unit hydrograph and component flows from rainfall, evapotranspiration, and streamflow) hydrological model with the intelligent-based GMDH (group method of data handling) model. The IHACRES model was calibrated using monthly ground-based precipitation and temperature data as well as satellite-based precipitation data. The lowest and highest Nash-Sutcliffe coefficient (NS) for the IHACRES models were, respectively, 0.14 and 0.68 in the calibration phase and 0.11 and 0.73 in the validation phase. It was also observed that using satellite-based precipitation data reduces NS by 10–75% in the 19 sub-basins under study. After calibrating and validating the IHACRES models, the hybrid models were developed by integrating IHACRES and GMDH models. The lowest and highest NS for the hybrid models were, respectively, 0.23 and 0.81 in the calibration phase and 0.11 and 0.81 in the validation phase. It was observed that, on average, integrating IHACRES and GMDH increases the NS by 44.1% in the calibration phase and 37.0% in the validation phase in comparison with the IHACRES model. According to the NS, the hybrid model had ‘acceptable’ performance in six sub-basins in which the IHCRES model had ‘unacceptable’ performance. It was observed that integrating the IHACRES model with a data-driven model (the GMDH model) can generally improve the simulation results in all sub-basins under study.

show abstract

Comparison of the performance of SWAT, IHACRES and artificial neural networks models in rainfall-runoff simulation (case study: Kan watershed, Iran)

Cited by 46 publications

References 55 publications

Hydrological modeling in a basin of the Brazilian Cerrado biome

Hydrological modeling in a basin of the Brazilian Cerrado biome

Daily Runoff Forecasting Using Ensemble Empirical Mode Decomposition and Long Short-Term Memory

Integrating IHACRES with a data-driven model to investigate the possibility of improving monthly flow estimates

Contact Info

Product

Resources

About