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ITS » PhD Theses » S3 - Statistika
Posted by tondoindra@gmail.com at 23/09/2014 16:33:00  •  1381 Views


PENGEMBANGAN METODE DISAGREGASI CURAH HUJAN LOKASI-WAKTU DENGAN MODEL STATE-SPACE PENDEKATAN BAYESIAN

DEVELOPMENT OF SPATIO-TEMPORAL RAINFALL DISAGGREGATION METHOD USING BAYESIAN STATE-SPACE MODELING APPROACH

Author :
ASTUTIK, SUCI  ( 1309301003 )




ABSTRAK

Kontribusi utama penelitian ini adalah pengembangan metode disagregasi curah hujan lokasi-waktu melalui kombinasi model state-space pendekatan Bayesian dan transformasi adjusting untuk menghasilkan data curah hujan lokasi-waktu skala waktu rendah per-jam dari skala waktu tinggi harian. Parameter model state-space diestimasi dengan pendekatan Bayesian melalui Markov Chain Monte Carlo MCMC dengan algoritma Gibbs sampler. Algoritma dan komputasi penggabungan kedua model ini diselesaikan dengan WinBUGS. Selanjutnya algoritma ini digunakan untuk mendisagregasi curah hujan lokasi-waktu di Daerah Aliran Sungai DAS Sampean Bondowoso yang hanya memiliki 3 stasiun hujan dengan skala waktu rendah per-jam. Penerapan metode disagregasi curah hujan lokasi-waktu di DAS Sampean diharapkan dapat menghasilkan data curah hujan per-jam di lokasi lain sehingga bermanfaat untuk pemodelan simulasi hidrologi yang memerlukan input data curah hujan skala waktu rendah. Pemodelan simulasi hidrologi dapat digunakan untuk mendisain prediksi banjir di DAS melalui perhitungan hidrograf. Kombinasi model state-space pendekatan Bayesian dan transformasi adjusting menghasilkan MAE dan MSE lebih kecil dibandingkan tanpa adjusting. Model ini telah berhasil meningkatkan akurasi statistik model dibandingkan hasil penerapan MuDRain. Keakuratan model ini dilakukan melalui prediksi h-step ke depan. Hasil yang diperoleh menunjukkan bahwa model masih akurat sampai 24 jam ke depan.


ABSTRACT

The main contribution of this research is the development of spatio-temporal rainfall disaggregation method through a combination of Bayesian approach state-space model and adjusting transformation to generate the low time scales hourly of high time scales daily spatio-temporal data. Parameters of state-space model have been estimated by using Bayesian approach through Markov Chain Monte Carlo MCMC and Gibbs sampler algorithm. Algorithm and computational of the combined model are solved by using WinBUGS. Furthermore the algorithm is used to disaggregate spatio-temporal rainfall data on the Sampean Watershed DAS Bondowoso which only has 3 stations in the low time scale rainfall hourly. Application of spatio-temporal disaggregation method on the Sampean Watershed is expected to generate the hourly rainfall data in other locations. This generated data are useful for hydrologic simulation modeling which requires the low time scale input rainfall data. Hydrologic simulation modeling can be used to predict flood in the watershed design through hydrograph calculation. Combination of Bayesian approach for state-space model coupled with adjusting transformation produces MAE and MSE smaller than without adjusting. This model has succeeded in increasing the accuracy of statistics performance compared to the MuDRain results. The accuracy of this model could show up to 24 hours ahead prediction.



Keywordsdisagregasi curah hujan lokasi-waktu; state-space, Bayesian; MCMC; Gibbs sampler
 
Subject:  Analisis multivarian; Statistik
Contributor
  1. Prof. Drs. Nur Iriawan, M.IKomp., Ph.D.
  2. Dr. Suhartono, S.Si., M.Sc.
Date Create: 18/09/2013
Type: Text
Format: PDF
Language: Indonesian
Identifier: ITS-PhD-13004140000090
Collection ID: 13004140000090
Call Number: RDSt 519.542 Ast p


Source
PhdTheses of Statistic RDSt 519.542 Ast p,2014

Coverage
ITS Community

Rights
Copyright @2013 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-31709-1309301003-Abstract_id.pdf - 552 KB
  2.  ITS-PhD-31709-1309301003-Abstract_en.pdf - 378 KB
  3.  ITS-PhD-31709-1309301003-Conclusion.pdf - 381 KB




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