ARTIFICIAL NEURAL NETWORKS UNTUK PEMODELAN CURAH HUJAN-LIMPASAN PADA DAERAH ALIRAN SUNGAI (DAS) DI PULAU BALI - IGB. Sila Dharma; IGA. Adnyana Putera; Putu Doddy Heka Ardana (Environmental Research Center (PPLH) of Udayana University, 2011)
Rainfall-runoff transformation of a watershed is one of the most complex hydrology phenomena, nonlinier process, time-varying and spatial distribution. Rainfall-runoff relationships play an important role in water resource management planning and therefore, different types of models with various degrees of complexity have been developed for this purpose. The application of Artificial Neural Networks (ANNs) on rainfall-runoff modelling has studied more extensively in order to appreciate and fulfil the potential of this modelling approach. Back propagation method has used in this study for modelling monthly rainfall for small size catchments areas. ANN model developed in this study successfully predicts relationship for rainfall-runoff with 90.14% accuracy on learning process and 72.41% accuracy on testing process. These results show that ANN provides a systematic approach for runoff estimation and represents improvement in prediction accuracy.