Appropriate outflow from a barrage should be maintained to avoid flooding on the downstream side during the rainy season. Due to the nonlinear and fuzzy behaviour of hydrological processes, and in cases of scarcity of relevant data, it is difficult to simulate the desired outflow using physically-based models. Artificial intelligence techniques, namely artificial neural networks (ANN) and an adaptive neurofuzzy inference system (ANFIS), were used in the reported study to estimate the flow at the downstream stretch of a river using flow data for upstream locations. Comparison of the performance of ANN and ANFIS was made by estimating daily outflow from a barrage located in the downstream region of Mahanadi River basin, India, using daily release data from the Hirakud Reservoir, located some distance upstream of the barrage. To obtain the best input-output mapping, five different models with various input combinations were evaluated using both techniques. The significance of the contribution of two upstream tributaries to barrage outflow estimation was also evaluated. Three feed-forward back-propagation training algorithms were used to train the models. Standard performance indices, such as correlation coefficient, index of agreement, root mean square error, modelling efficiency and percentage deviation in peak flow, were used to compare the performance of the models, as well as the training techniques. The results revealed that the neural network with conjugate gradient algorithm performs better than Levenberg-Marquardt and gradient descent algorithms. The model which considers as input the reservoir release up to three antecedent time steps produced the best results. It was found that barrage outflow could be better estimated by the ANFIS than by the ANN technique.

No abstract

River cross-sections are the prime input to any river hydraulic model for simulation of water level and discharge. Field measurements of river cross-sections are labour intensive and expensive activities. Availability of measured river crosssections is scanty in most of the developing countries, thereby making it difficult to simulate the water level and discharge using hydraulic models. A methodology for extracting river cross-sections from Shuttle Radar Topographic Mission digital elevation model (SRTM DEM) of 3-arc second has been proposed in the reported study. The extracted river cross-sections were used to simulate the magnitude of flood in the deltaic reaches of Brahmani river basin located in the eastern India. Forty cross-sections along the reaches of the rivers were extracted from the DEM and were used in the MIKE 11 hydrodynamic (MIKE 11HD) model. Prior to using the DEMextracted river cross-sections in the model, the cross-sections were modified based on the results of the DEM error analysis. Four available measured river cross-sections were compared with the DEM-extracted modified cross-sections to examine their geometric and hydraulic similarity. By changing Manning's roughness coefficient (n), same stage-discharge relationship could be obtained in both types of crosssections. Subsequently, the DEM-extracted cross-sections were used in the MIKE 11HD model for the simulation of discharge and water levels at various sections of the rivers. The model was calibrated for the period of June 15-October 31 of the year 1999 and validated for the year 2003. The model validation results showed a close agreement between the simulated and observed stage hydrographs. The N. Pramanik et al. calibrated values of Manning's n were found to vary within the range of 0.02 to 0.033. The study revealed that freely available SRTM DEM-extracted river cross-sections could be used in hydraulic models to simulate stage and discharge hydrographs with considerable accuracy under the scarcity of measured cross-section data.

No abstract

Abstract:Probability density functions (PDFs) are used to fit the shape of hydrographs and have been popularly used for the development of synthetic unit hydrographs by many hydrologists. Nevertheless, modelling the shapes of continuous stream flow hydrographs, which are probabilistic in nature, is rare. In the present study, a novel approach was followed to model the shape of stream flow hydrographs using PDF and subsequently to develop design flood hydrographs for various return periods. Four continuous PDFs, namely, two parameter Beta, Weibull, Gamma and Lognormal, were employed to fit the shape of the hydrographs of 22 years at a site of Brahmani River in eastern India. The shapes of the observed and PDF fitted hydrographs were compared and root mean square errors, error of peak discharge (EQ P ) and error of time to peak (ET P ) were computed. The best-fitted shape and scale parameters of all PDFs were subjected to frequency analysis and the quartiles corresponding to 20-, 50-, 100-and 200-year were estimated. The estimated parameters of each return period were used to develop the flood hydrographs for 20-, 50-, 100-and 200-year return periods. The peak discharges of the developed design flood hydrographs were compared with the design discharges estimated from the frequency analysis of 22 years of annual peak discharges at that site. Lognormalproduced peak discharge was very close to the estimated design discharge in case of 20-year flood hydrograph. On the other hand, peak discharge obtained using the Weibull PDF had close agreement with the estimated design discharge obtained from frequency analysis in case of 50-, 100-and 200-year return periods. The ranking of the PDFs based on estimation of peak of design flood hydrograph for 50-, 100-and 200-year return periods was found to have the following order: Weibull > Beta > Lognormal > Gamma.