Estimation of Hourly Flood Hydrograph from Daily Flows Using Artificial Neural Network and Flow Disaggregation Technique

Abstract

Flood data on a high temporal scale are required for the design of hydraulic structures, flood risk assessment, flood protection, and reservoir operations. Such flood data are typically generated using rainfall-runoff models through an accurate calibration process. The data also can be estimated using a simple relationship between the daily and the sub-daily flow records as an alternative to rainfall–runoff modelling. In this study, we propose an approach combining an artificial neural network (ANN) model for peak flow estimation and the steepness index unit volume flood hydrograph (SIUVFH) method for sub-daily flow disaggregation to generate hydrographs on an hourly time scale. The SIUVFH method is based on the strong relationship between the flood peak and the steepness index, which is defined as the difference between the daily flood peak and daily flow several days before the peak; it is also used for selecting a reference unit volume flood hydrograph to be scaled to obtain the sub-daily flood hydrograph. In this study, to improve the applicability of the SIUVFH method for locations with a weak relationship between the flood peak and steepness index, the ANN-based flood peak estimation was used as an additional indicator to determine a reference unit volume flood hydrograph. To apply the proposed method, ANN models for estimating the peak flows from the mean daily flows during peak and adjacent days were constructed for the studied dam sites. The optimal ANN structures were determined through Monte Carlo cross-validation. The results showed a good performance with statistical measurements of relative root mean square errors of 0.155–0.224, 0.208–0.301, and 0.244–0.382 for the training, validation, and testing datasets, respectively. An application of the combined use of the ANN-based peak estimation and the SIUVFH-based flow disaggregation revealed that the disaggregated hourly flows satisfactorily matched the observed flood hydrograph.

Using the General Regression Neural Network Method to Calibrate the Parameters of a Sub-Catchment

“…They indicated that GRNN is capable of yielding satisfactory outputs. Other scholars have reported similar results [33,34]. However, the previous studies focus mostly on the application of runoff forecasting by GRNN without considering its combination with hydrological modeling software such as SWMM.…”

“…Recently, an ANN known as the general regression neural network (GRNN) has shown promise owing to its rapid calculation speed and good nonlinear approximation performance [28,29]. Therefore, it is widely used in many academic fields, including hydrology [31][32][33][34]. Apaydin et al [31] simulated the daily streamflow to the Ermenek hydroelectric dam reservoir using deep recurrent neural network architectures and reported highly accurate results.…”

The Serok River Catchment Flood Hydrograph

“…High-resolution flood event data play a crucial role in various aspects, including the design of hydraulic structures, flood frequency analysis, flood risk assessment, and reservoir operations (Lee, J et al, 2021). For hydraulic design and risk assessments, it is necessary to determine design flood estimates for a specific return period in both gauged and ungauged catchments.…”

Predicting Effects of Non-Point Source Pollution Emission Control Schemes Based on VMD-BiLSTM and MIKE21

“…In order to achieve simulation predictions of water quality response within a certain period after the implementation of emission control schemes, we have applied deep learning techniques to the water environment model. Deep learning is a type of data-driven model that has been proven to be a valuable tool in various applications (Lee et al, 2020). For example, it was initially applied to speech-to-text conversion, machine translation, and sequence data processing.…”