ARTIFICIAL NEURAL NETWORK APPLICATION FOR THE ESTIMATION OF ANNUAL RAINFALL DATA UNRECORDED IN CISADANE WATERSHED - Elhamida Rezkia Amien; Roh Santoso Budi Waspodo; Budi Indra Setiawan; Rudi Yanto (Jurnal Teknik Sipil dan Lingkungan, 2016)
Naturally in a watershed rainfall distributes spatially. To know rainfall in the watershed needs information from many installed rain gauges. However, rainfall data is found not completely recorded. It is then important to estimate missing or unrecorded  rainfall data. This study aims to estimate annual rainfall data in stations by using ANN (Artificial Neural Network). This study was conducted in Cisadane watershed. This study perfomed using rainfall data for 14  periods, the location of rainfall post (coordinates and elevation), DEM map, and watershed map. Data processing and analyzing performed using Ms. Excel 2010, ArcGIS 10.0, and BackPropogation Neural Network 1.0 program. Data used as input in ANN to estimates unrecorded rainfall data were coordinates (X,Y) and elevation (Z) of each rainfall post. ANN can be used to predict the amount of rainfall in cisadane watershed marked with a value of determination (R2) 0,97. After all data complete, average of rainfall in Cisadane watershed can be calculate using arithmetic, thiessen polygon, and isohyet. The amount of rainfall watershed in Cisadane using the arithmetic mean produce rainfall of 2.609 mm, with Thiessen Polygon of 2.539 mm, and with Isohyets of 2.594 mm. Keywords: ANN, annual rainfall, Cisadane watershed, estimation of rainfall