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ITS » PhD Theses » Program Doktoral Teknik Sipil
Posted by dee@its.ac.id at 02/11/2011 11:37:49  •  2475 Views


MODEL DISAGREGASI DATA HUJAN TEMPORAL DENGAN PENDEKATAN BAYESIAN SEBAGAI INPUT PEMODELAN BANJIR

TEMPORAL RAINFALL DISAGREGATION MODEL USING BAYESIAN APPROACH AS FLOOD MODELLING

Author :
HIDAYAH, ENTIN  ( 3107301001 )




ABSTRAK

Pemodelan hujan-aliran dalam rangka mengestimasi debit banjir rencana membutuhkan data hujan resolusi tinggi jam-jaman. Pada umumnya di Indonesia yang tersedia adalah alat pengukur hujan harian sedangkan alat pengukur hujan otomatis yang menyediakan data hujan secara jam-jaman jumlahnya terbatas. Penelitian ini bertujuan untuk membuat model disagregasi data hujan harian menjadi jam-jaman guna menyediakan input data pemodelan banjir. Data diambil dari dari satu lokasi stasiun pengukur hujan di Stasiun Sentral Bondowoso Jawa Timur. Data yang digunakan untuk memodelkan adalah data series bulan Desember dari tahun 2005-2008. Penelitian ini mencoba untuk mendisagregasi data hujan skala jamjaman dari data hujan skala harian menggunakan model time series autoregresi Periodik PAR124 yang diberi perlakuan dengan prosedur adjusting dan filtering. Metode yang digunakan dalam proses estimasi model ini adalah Bayesian Markov Chain Monte Carlo MCMC yang dibantu dengan sofware statistik WinBUGS 1.4. Model ini dievaluasi melalui membandingkan model dengan hasil implementasi Heytos. Selanjutnya prediksi model disagregasi hujan ini dibantu dengan Matlab yang dihubungkan dengan WinBUGS. Hasil simulasi model PAR 124 yang diberi perlakuan dengan adjusting dan filtering ini memberikan nilai Mean Absolute Error MAE sebesar 044. Model ini mampu meningkatkan kinerja sebesar 15 jika dibandingkan hasil aplikasi Heytos. Kinerja prediksi model menunjukkan hasil yang bagus untuk tinggi hujan maksimum selisih tinggi hujannya hanya 61 terhadap tinggi hujan observasi. Keandalan model ini telah diuji untuk dua kejadian. Pertama implementasi untuk bulan Desember tahun 2009 memberikan kinerja yang bagus dengan nilai MAE 0.37. Kedua hasil kalibrasi dan implementasi model untuk bulan-bulan lain selain Desember tahun 2005-2008 menunjukkan bahwa model ini mampu mendisagregasi data hujan dari harian ke jam-jaman terutama pada bulan basah. Pemanfaatan data hasil disagregasi telah diuji dalam perhitungan hidrograf banjir dengan hasil yang sangat memuaskan karena menghasilkan hidrograf banjir yang polanya mirip dengan hidrograf banjir yang dibangun dari data observasi.


ABSTRACT

Rainfall-runoff modeling in order to estimate the flood design requires high resolution rainfall hourly data. In general in Indonesia there are lack of automatic rain gauges providing high resolution rainfall and a number of daily rain gauges on the other hand is available. This is an obstacle for rainfallrunoff modeling. This research is aimed to create a model of disaggregated daily rainfall data into hourly rainfall data in order to provide input for flood modeling. The research is conducted in a single location at Sentral Station. The data used in this modeling is the rainfall data series in December from 2005 to 2008 in Sentral Station Bondowoso East Java. This study tries to disaggregating daily scaled rainfall data to hourly scaled rainfall data using periodic auto-regression model PAR 124 coupled with adjusting and filtering procedures. The model is employed for estimating the hourly rainfall from daily rainfall. The Bayesian Markov Chain Monte Carlo MCMC WinBUGS 1.4 is utilized for the purpose. The Evaluation of model is compared the results provided by the Heytos program. Furthermore the prediction of the disaggregated data is modeled by using Matlab linked with WinBUGS. The simulation model of PAR 124 coupled with adjusting and filtering procedures gives Mean Absolute Error MAE value of 0.44. This model has successfully increased the performance of the output by15 compared to the results of Heytos application. This model demonstrates better prediction of maximum rainfall depth only 6.1 differ from the observation than the Heytos. The reliability of this model is tested for 2 conditions. Firstly is by implementing the model to the rainfall data in December 2009. I shows that this model works significantly well in disaggregating the rainfall data from daily to hourly with the MAE value of 0.37. Secondly is by calibrating and implementing the model to the rainfall data in Januari-Nopember 2005-2008. It shows that this parameter model works well mostly for wet seassons.The data obtained from the model has been used for developing a flood hydrograph and the result shows the similarity with the one build by using observed data.



KeywordsDisagregai data hujan; PAR; Bayesian; Adjusting; Filtering
 
Subject:  Pengambilan keputusan
Contributor
  1. Prof. Dr. Ir. Nadjadji Anwar, M.Sc
  2. Prof. Drs. Nur Iriawan, MIKomp. PhD
Date Create: 11/02/2011
Type: Text
Format: pdf
Language: Indonesian
Identifier: ITS-Undergraduate-3100011042955
Collection ID: 3100011042955
Call Number: RDS 519.542 Hid m


Source
Disertasi of Civil Engineering, RDS 519.542 Hid m, 2011

Coverage
ITS Community

Rights
Copyright @2011 by ITS Library. This publication is protected by copyright and per obtained from the ITS Library prior to any prohibited reproduction, storage in a re transmission in any form or by any means, electronic, mechanical, photocopying, reco For information regarding permission(s), write to ITS Library




[ Download - Open Access ]

  1.  ITS-PhD-15746-Abstract_id.pdf - 221 KB
  2.  ITS-PhD-15746-Abstract_en.pdf - 180 KB
  3.  ITS-PhD-15746-Conclusion-1468276.pdf - 1434 KB
  4.  ITS-PhD-15746-Paper-860954.pdf - 841 KB




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