In Taiwan, owing to the nonuniform temporal and spatial distribution of rainfall and high mountains all over the country, hydrologic systems are very complex. Therefore, preventing and controlling flood disasters is imperative. Nevertheless, water level and flow records are essential in hydrological analysis for designing related water works of flood management. Due to the complexity of the hydrological process, reliable runoff is hardly predicted by applying linear and non-linear regression methods. Therefore, in this study, a model for estimating runoff by using rainfall data from a river basin is developed and a neural network technique is employed to recover missing data. For achieving the objectives, hourly rainfall and flow data from Nanhe, Taiwu, and Laii rainfall stations and Sinpi flow station in the Linbien basin are used. The data records were of 27 typhoons between the years 2005 and 2009. The feed forward back propagation network (FFBP) and conventional regression analysis (CRA) were employed to study their performances. From the statistical evaluation, it has been found that the performance of FFBP exceeded that of regression analysis as reflected by the determination coefficients R 2 , which were 0.969 and 0.284 for FFBP and CRA, respectively.
The present study evaluates the predictive accuracy of the feed forward backpropagation artificial neural network (BP) in evapotranspiration forecasting from temperature data basis in Dédougou region located in western Burkina Faso, sub-Saharan Africa. BP accuracy is compared to the conventional Blaney-Criddle (BCR) and Reference Model developed for Burkina Faso (RMBF) by referring to the FAO56 Penman-Monteith (PM) as the standard method. Statistically, the models' accuracies were evaluated with the goodness-of-fit measures of root mean square error, mean absolute error and coefficient of determination between their estimated and PM observed values. From the statistical results, BP shows similar contour trends to PM, and performs better than the conventional methods in reference evapotranspiration (ET ref) forecasting in the region. In poor data situation, BP based only on temperature data is much more preferred than the other alternative methods for ET ref forecasting. Furthermore, it is noted that the BP network computing technique accuracy improves significantly with the addition of wind velocity into the network input set. Therefore, in the region, wind velocity is recommended to be incorporated into the BP model for high accuracy management purpose of irrigation water, which relies on accurate values of ET ref.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.