Integration of Volterra model with artificial neural networks for rainfall-runoff simulation in forested catchment of northern Iran

Kashani, Mahsa H.; Ghorbani, Mohammad Ali; Dinpashoh, Yagob; Shahmorad, S.

doi:10.1016/j.jhydrol.2016.06.028

Cited by 36 publications

(9 citation statements)

References 36 publications

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“…The range of PE is found as 0.009-0.299 for prediction of Q t +1 as shown in Table 5. The mean of PE, (PE m ) for 10 events selected in et al, 2008;Nayak et al, 2007Nayak et al, , 2014Kashani et al, 2016). Fig.…”

Section: = +mentioning

confidence: 95%

Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm

Safari

Arashloo

Mehr

2020

Journal of Hydrology

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Section: = +mentioning

confidence: 95%

Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm

Safari

Arashloo

Mehr

2020

Journal of Hydrology

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“…Kalman filter (EnKF) 18,19 can well handle the nonlinear hydrological data, but it requires the rather lengthy historical data. Kashani et al 20 combined the artificial neural network with semidistributed model together, the least squares method is used for solving the nonlinear data prediction, and the accuracy of the simulation results is improved. Bartoletti et al 21 fusing the principal component analysis with adaptive neuro-fuzzy inference systems predicts the runoff pattern.…”

Section: Related Workmentioning

confidence: 99%

Hybrid model of generative adversarial network and Takagi‐Sugeno for multidimensional incomplete hydrological big data prediction

Liu

Song

2020

Concurrency and Computation

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Summary The processing of rainfall‐runoff transmission in the catchment area is a very complex phenomenon, such as temporal and spatial changes of the catchment characteristics and uncertainties of rainfall patterns. To handle this challenge, data‐driven hydrologic models emerge rapidly for the rainfall runoff prediction. However, the incomplete hydrological data constrain the development of digital hydrologic model. This article proposes a rainfall‐runoff prediction method of coupling generative adversarial network (GAN) and Takagi‐Sugeno (T‐S) fuzzy model, in which the GAN is used to generate the data for completing the incomplete hydrological data, and the T‐S fuzzy model is utilized for forecasting the rainfall runoff. The presented method is examined by real data in Huaihe basin just considering the precipitation and streamflow. Experiments show that the model combined GAN and T‐S can achieve satisfactory prediction results.

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“…The model performance in the five watersheds was in the range of 5.04-9.99 m 3 /s for mean absolute error (MAE) and 8.24-16.83 m 3 /s for root mean square error (RMSE), indicating that the developed model adequately reflected the measured values. Kashani et al [23] divided the studied watershed into sub-watersheds and developed an ANN for each sub-watershed to simulate the precipitation-discharge of the entire watershed. Therein, r was 0.9, RMSE was 2.14 m 3 /s, and NSE was 0.7, similar to our experimental results.…”

Section: Introductionmentioning

confidence: 99%

Developing a Discharge Estimation Model for Ungauged Watershed Using CNN and Hydrological Image

Kim

Song²

2020

Water

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This study aimed to estimate the discharge in ungauged watersheds. To this end, we herein deviated from the model development methodology of previous studies and used convolution neural network (CNN), a deep training algorithm, and hydrological images. As the CNN model was developed for solving classification issues in general, it is unsuitable for simulating the discharge, which is a continuous variable. Therefore, the fully connected layer of the CNN model was improved. Moreover, images reflecting the hydrological conditions rather than a general photograph were used as input data for the CNN model. Three study areas that have discharge gauged data were set for the model’s training and testing. The data from two of the three study areas were used for CNN model training, and the data of the other were used to evaluate model prediction performance. The results of this study demonstrate a moderate predictive success of the discharge of an ungauged watershed using the CNN model and hydrological images. Therefore, it can be suitable as a methodology for the discharge estimation of ungauged watersheds. Simultaneously, it is expected that our methodology can be applied to the field of remote sensing or to the field of real-time discharge simulation using satellite imagery on a global scale or across a wide area.

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Integration of Volterra model with artificial neural networks for rainfall-runoff simulation in forested catchment of northern Iran

Cited by 36 publications

References 36 publications

Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm

Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm

Hybrid model of generative adversarial network and Takagi‐Sugeno for multidimensional incomplete hydrological big data prediction

Developing a Discharge Estimation Model for Ungauged Watershed Using CNN and Hydrological Image

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