Analysis of Rainfall-runoff Neuron Input Model with Artificial Neural Network for Simulation for Availability of Discharge at Bah Bolon Watershed. (2015)

• Record Type: Journal Article
• Authors: Setiono, ; Hadiani, Rintis
• Is Part Of: Procedia engineering. Volume 125(2015)
• Page Start: 150
• Page End: 157
• Publication Date: 2015
• Subjects: Artificial Neural Network -- Discharge -- Hidrology Modeling -- Micro Hydro Power; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2015.11.022
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: Indonesia is a tropical country with two seasons (wet and dry) which play the main role in water cycle process. Occurrence of rain continues into the flow of the discharge in the river with a huge energy potential that can be exploited for the life of the surrounding community. The occurrence and intensity of rain is random and difficult to predict in a certain period of time so that discharge is also difficult to be estimated although it is measured in the field in time of rainfall occurrence. The amount of runoff produced by the same depth of precipitation in a watershed will result a different magnitude with another watershed because it is influenced by land use in the watershed. This paper discusses the modeling of rainfall-runoff in the Watershed of Bolon in Simalungun district of North Sumatra Province using Artificial Neural Network (ANN) to determine the potential of the available discharge in the long term for the purpose of Micro Hydro Power (MHP). The software/program is developed with Scilab mathematical open source software (www.scilab.org ) based on ANN algorithm. The data are record of monthly rainfall and discharge for 12 years (2001 to 2012). The models developed are 12 monthly neurons, 4 year neurons and series neuron (48 neurons) for input (rainfall) - output (runoff) neurons. The result shows that reliability the 12 monthly neurons is 99% (the best) followed by series neuron with 78% and 4 year neuron 77%. The chosen model (12 monthly neurons) then to be used for predicting the monthly discharge availability at Bah Bolon Site. Dependable discharges predicted with this software for year 2013 to 2016 consecutively are as follows: 0.678246 m 3 /s, 0.655288 m 3 /s, 0.678475 m 3 /s and 0.678135 m 3 /s.