MODELING OF TRIANGULAR UNIT HYDROGRAPHS USING AN ARTIFICIAL NEURAL NETWORK IN A TROPICAL RIVER BASIN

Abstract

Rainfall-runoff models are crucial for estimating floods in a river basin. Most watersheds in
Indonesia have a data deficiency problem, especially in natural watersheds (ungauged river basins), which may
affect the accuracy of design and planning of water resources. Most synthetic unit hydrograph methods are not in
accordance with the characteristics of Indonesian watersheds, and adjustments should be made to obtain accurate
results. This study aimed to develop a simple triangular unit hydrograph generated by using a neural network for
different watersheds in Indonesia. The triangular unit hydrograph consists of the peak discharge, time to peak, and
time base developed using a neural network with a learning process from the observed unit hydrograph, and the
result will be compared to the Snyder-Alexeyev synthetic unit hydrograph after being adjusted to obtain accurate
results in comparison to observed data. An artificial neural network (ANN) model was developed by inputting
basin characteristics such as catchment area (A), river length (L), basin slope (S), shape factor (F), and runoff
coefficient (C). The model will generate the output of a triangular synthetic unit hydrograph consisting of peak
discharge (Qp), time to peak (Tp), and time base (Tb). A case study is discussed in tropical river basins mostly on
Java Island, where flood events are frequent. The simulation result from applying an ANN using generalized
reduced gradient neural network (GRGNN) methods is significantly in line with historical data. The ANN
simulation shows more accurate results than the adjusted Snyder-Alexeyev unit hydrograph. The results indicated
that the synthetic unit hydrograph generated by an ANN can be applied to an ungauged river basin.