PRAKIRAAN DEBIT AIR SUNGAI MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPROPAGATION LEVENBERG MARQUARDT (STUDI KASUS BENGAWAN SOLO) - , SUNARDI ([Yogyakarta] : Universitas Gadjah Mada, 2011)
River debit prediction in many cases is much needed. Prediction system is needed to see possibility dealing with the changes of nature condition in the forthcoming time. In the water debit prediction, some analysis sign methods are used. They are Artificial Neural Networks (ANN) and Fast Fourier Transform (FFT). Debit sign is transformed into frequency domain to find out the frequency components. The result of transform gives clues about the existence of periodicity cycles of raising and declining debit that is used for prediction. Frequency filtering is employed to decompose the swelling periodicity on each raising or declining debit. Predicted data with certain frequency are able to determine the debit magnitude of the forthcoming time. River debit periodization on each frequency component can be determine being long, middle, or short term flood or dry period. ANN that is trained for the sake of prediction using Levenberg Marquadt algorithm has optimum architecture with 7 input neurons, 11 hidden layer neurons and 1 out layer neuron. The result of ANN performance of long term debit periodization is 2,048 days in dry period and 1.171 days in flood period. The forecasted highest debit will take place on May 21st, 2011 ï± 7 days with the flood debit of 2512 m3/second and the dry debit of 38,28 m3/second in August 2015. Approaching some years before the days, with the additional data that found afterward, it can be better predicted with middle term approach, and then for the following months with short term.