@ARTICLE{Obasi_Arinze_A._Rainfall-river_2020,
 author={Obasi, Arinze A. and Ogbu, Kingsley N. and Orakwe, Louis C. and Ahaneku, Isiguzo E.},
 volume={No 44},
 pages={98-105},
 journal={Journal of Water and Land Development},
 howpublished={online},
 year={2020},
 publisher={Polish Academy of Sciences; Institute of Technology and Life Sciences - National Research Institute},
 abstract={This study is aimed at evaluating the applicability of Artificial Neural Network (ANN) model technique for river discharge forecasting. Feed-forward multilayer perceptron neural network trained with back-propagation algorithm was employed for model development. Hydro-meteorological data for the Imo River watershed, that was collected from the Anambra-Imo River Basin Development Authority, Owerri – Imo State, South-East, Nigeria, was used to train, validate and test the model. Coefficients of determination results are 0.91, 0.91 and 0.93 for training, validation and testing periodsrespectively. River discharge forecasts were fitted against actual discharge data for one to five lead days. Model results gave R2 values of 0.95, 0.95, 0.92, 0.96 and 0.94 for first, second, third, fourth, and fifth lead days of forecasts, respectively. It was generally observed that the R2 values decreased with increase in lead days for the model. Generally, this tech-nique proved to be effective in river discharge modelling for flood forecasting for shorter lead-day times, especially in areas with limited data sets.},
 type={Article},
 title={Rainfall-river discharge modelling for flood forecasting using Artificial Neural Network (ANN)},
 URL={http://ochroma.man.poznan.pl/Content/110854/PDF/Obasi_et_al_510_inter.pdf},
 doi={10.24425/jwld.2019.127050},
 keywords={Artificial Neural Network (ANN), rainfall, flood forecasting, river discharge},
}