Abstract
ANFIS, ARIMA and Hybrid Multiple Inflows Muskingum models (HMIM) were applied to simulate and forecast downstream discharge and flow top widths in a river system. The ANFIS model works on a set of linguistic rules while the ARIMA model uses a set of past values to predict the next value in a time series. The HMIM model assumes a power-law relationship between water discharge and flow top width at a section. The models were used to simulate and forecast discharge and flow top width at a downstream section in the Barak River system in India. Flow top widths corresponding to different flow depths at the downstream section were estimated using a digital elevation model (DEM). The parameters in the hybrid model were estimated by applying Non-dominated Sorting Genetic Algorithm II (NSGA-II). The study shows that the power-law relationship involving section characteristics can describe the top width versus discharge relationship for a section. The models allow direct estimation of the downstream flow top width on the basis of upstream flow variables. Results obtained in the study show that performances of the HMIM, ANFIS and ARIMA models are satisfactory, having average prediction errors of less than 7% of the average value of the observed series. Application of the ANFIS, ARIMA and the HMIM models to the studied river system demonstrate the suitability of the models in simulating and forecasting downstream flow top width in river systems.Keywords: River system, flow top width, genetic algorithm, flood flow, hybrid model
Highlights
River flood forecasting and warning is one of the most important components of flood management
Comparing the results obtained in forecasting downstream values by using the models, it can be seen that the adaptive neurofuzzy inference system (ANFIS) model performance in terms of ‘root mean square error (RMSE)’ is the best when downstream discharges are forecast, while the Autoregressive Integrated Moving Average (ARIMA) model gives the best performances when downstream top widths are forecast
The results obtained in the present study demonstrate the efficiency of the models in simulating and forecasting downstream flow top width in a river system
Summary
River flood forecasting and warning is one of the most important components of flood management. HMIM, ANFIS and ARIMA models incorporating discharge and flow top width variables are applied to forecast and/or simulate outflow and flow top width at a downstream station in a river system in India.
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