Comparing single- and two-segment statistical models with a conceptual rainfall-runoff model for river streamflow prediction during typhoons. (November 2016)

• Record Type: Journal Article
• Authors: Wei, Chih-Chiang
• Is Part Of: Environmental modelling & software. Volume 85(2016:Nov.)
• Page Start: 112
• Page End: 128
• Publication Date: 2016-11
• Subjects: Streamflow -- Statistical model -- Conceptual model -- Prediction -- Typhoon; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.08.013
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: This study examined various regression-based techniques and an artificial neural network used for streamflow forecasting during typhoons. A flow hydrograph was decomposed into two segments, rising and falling limbs, and the individual segments were modeled using statistical techniques. In addition, a conceptual rainfall–runoff model, namely the Public Works Research Institute (PWRI)-distributed hydrological model, and statistical models were compared. The study area was the Tsengwen Reservoir watershed in Southern Taiwan. The data used in this study comprised the observed watershed rainfalls, reservoir inflows, typhoon characteristics, and ground weather data. The forecast horizons ranged from 1 to 12 h. A series of assessments, including statistical analyses and simulations, was conducted. According to the improvements in errors, among single-segment statistical models, the multilayer perceptron achieved superior prediction accurary compared with the regression-based methods. However, the pace regression was the most favorable according to an evaluation of model complexity and accuracy. To examine the robustness of the results for forecast horizons varying from 1 to 12 h, statistical significance tests were performed for the single- and two-segment models. The prediction ability of the two-segment models was superior to that of the single-segment models. In addition, Typhoon Sinlaku in 2008 was considered in a comparison between the conceptual PWRI model output and that of the developed statistical models. The results showed that the PWRI model yielded the least favorable results. Highlights: The four regressions and MLP ANN were modeled for river streamflow prediction. The OLS, pace, isotonic, and additive regression-based techniques were examined. The number of time-lagged input data according to a subset selection method was evaluated. The statistical significance tests were performed for single- and two-segment models. Comparisons between the conceptual PWRI model and the statistical models were made.