Claim Missing Document
Check
Articles

Found 2 Documents
Search

SIMULASI DEBIT DENGAN HYDROCAD BERDASARKAN HUJAN BULANAN DI DAS ALANG Berklyson Tarigan; Rintis Hadiani; Suyanto Suyanto
Matriks Teknik Sipil Vol 2, No 2 (2014): Juni 2014
Publisher : Program Studi Teknik Sipil FT UNS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (276.292 KB) | DOI: 10.20961/mateksi.v2i2.37438

Abstract

Rainfall correlate with the characteristics of the DAS. Streamflow for a rainy directly correlate with the duration and intensity of rain. Theresearch is (1) to know the value of discharge parameters based of mounly rainfall in DAS Alang with HydroCAD application, (2) to knowsthe value of discharge from HydroCAD simulation to observation data, (3) to know the value correlation of discharge from HydroCADsimulation with observation data usingthe character of the DAS Alang.This research is descriptive quantitative research, where data used are secondary data. The secondary data from BTKPDAS instance. The researchphase implemented with collec dayly railfall of DAS Alang. The discharge of DAS Alang from 2002 - 2012. The results rainfall areas usingpolygon thieseen method. The rainfall areas insert to the application and then discharge simulation test. From HydroCAD application willproduce discharge simulation of DAS Alang, the produce of discharge simulation will be correlation with discharge from observation data ifcoefficiend's correlation R 0,8 until 1,0 showed a good correlation, if coefficient's correlation more than 0,4 less than 0,8 indicate the goodcorrelation, the coefficient between 0,2 until 0,4 showed not good correlation, if less than 0,2 can be ignored.The result of research to show that Curve Number CN and Time Concentration TC very influential in discharge simulation with HydroCADAplication . the less accurate of discharge observation data so the correlation value and the result discharge correlation and observation data usingTime Concentration method who different between Ponce method, Rasional method, SCS method. Using TC Ponce method to result the correlationvalue 0,8 until 1,0 in 2004,2006,2009,2011,2012, Using TC Rasional method to result the correlation value 0,8 until 1,0 in2004,2006,2011,2012, Using TC SCS method to result the correlation value 0,8 until 1,0 in 2004,2006,2009,2011,2012, it can beconciuded the TC Ponce method and TC SCS method have gred similarity of correlation coefficient. Can be applicatied in DAS Alang
PREDIKSI POTENSI DEBIT BERDASARKAN DATA HUJAN MAKSIMUM BULANAN DENGAN METODE JARINGAN SYARAF TIRUAN BACKPROPAGATION DI DAS ALANG Jonas Eratika Ginting; Rintis Hadiani; Setiono Setiono
Matriks Teknik Sipil Vol 2, No 1 (2014): Maret 2014
Publisher : Program Studi Teknik Sipil FT UNS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (484.206 KB) | DOI: 10.20961/mateksi.v2i1.37467

Abstract

The data flow is important information in the management of water resources. Water resources management has many aspects such as flood controlpurposes, and so on electrical energy potential. For water resources management and watershed planning Alang long-term infrastructure, flow of dataneeded in the future. So we need an approach to the provision of discharge data with neural network models. The purpose of this study is (1) Determinethe coefficient of ANN parameters, (2) Determine the discharge prediction years 2013-2016 and (3) Determine the reliability of the model.This research is descriptive quantitative research, where data used are secondary data. The secondary data used were obtained from the office. Stages ofthe research is to collect data year 2001-2012 rainfall and discharge as well as topographic maps. Perform calculations using the area rain Thiessenpolygon method. Results rainfall areas converted into discharge using the Rational method with the help of software Backpropagation ANN Matlab(R2010b). Then perform simulations until the results obtained are at the limits set and simultaneously obtain discharge predictions. Furthermore, totest the reliability of the model.The results showed that the ANN parameters : Period = 4 years, Hidden Layer = 2 pieces (2 each neuron), Epoch = 150000, Goal Momentum =0.6 and = 0.02. Then for discharge predictions for the year 2013-2016 Alang DAS can be seen in table 5. Reliability models 58.17% derived fromthe analysis of reliability. The model has achieved 58.17% reliability and 95% Confidence qualify, but the parameters of the model need to be modifiedto apply to other watersheds.