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ITS » Master Theses » Manajemen & Rekayasa Sumber Air S2
Posted by sarwono@its.ac.id at 22/02/2010 17:35:48  •  3322 Views


MODEL PERAMALAN BANJIR DI DAS BENGAWAN SOLO

FLOOD FORECASTING MODEL OF BENGAWAN SOLO CATCHMENT AREA

Created by :
Mularto, Listiya Hery ( 3107205711 )



SubjectPeramalan banjir
Alt. Subject Flood forecasting
KeywordModel peramalan
Elevasi muka air Bengawan Solo
RMSE
M5 Model Tree
Artificial Neural Network
Data Driven Model

Description:

Sejak tahun 1863 Bengawan Solo telah menimbulkan banjir di daerah hulu, bahkan saat ini banjir sudah masuk kawasan hilir. Kejadian banjir yang terjadi pada akhir Desember 2007 telah banyak menyebabkan kerugian bagi masyarakat di sekitar sungai baik kerugian harta benda maupun nyawa. Salah satu upaya yang penting saat akan terjadi banjir adalah bagaimana cara mengetahui kapan waktu datangnya banjir jauh sebelum banjir tersebut terjadi, sehingga penduduk sekitar sungai memiliki kesempatan untuk menyelamatkan diri dan harta bendanya. Tujuan dari penelitian ini adalah membangun sistem peramalan yang dapat meramalkan waktu datangnya banjir sebelum kejadian banjir tersebut datang. Penelitian diawali dengan melakukan studi literatur dan mengumpulkan datadata elevasi muka air di beberapa stasiun pengamatan yang memiliki korelasi terbesar terhadap daerah yang akan dijadikan fokus peramalan, dalam hal ini stasiun Bojonegoro, Babat dan Kuro. Data-data yang diperoleh digunakan sebagai input dalam membangun model peramalan. Pemilihan variabel input yang berpengaruh terhadap variabel output dilakukan menggunakan analisa korelasi. Metode peramalan menggunakan data driven model antara lain: M5 Model Tree dan Artificial Neural Network (ANN), dimana proses pembelajarannya (learning) menggunakan program bantu Weka Knowledge Explore. Kelayakan performa kedua model tersebut melalui uji verifikasi. Dari kedua model yang digunakan, model peramalan yang terpilih untuk peramalan muka air di Bojonegoro untuk 1, 3 dan 6 jam ke depan adalah model M5 Model Tree dengan nilai RMSE (Root Mean Square Error) saat verifikasi berkisar 0,278 0,717, sedangkan untuk di daerah Babat dan Kuro, model yang terpilih untuk peramalan elevasi muka air saat 6 jam ke depan adalah model ANN dengan nilai RMSE 0,432 dan 0,154.


Alt. Description

Since 1863, Bengawan Solo has causing flood at the upstream area, and even nowadays the flood already entering the downstream area. The flood that occurred in the end of December 2007 was causing a big loss of property and life as well for the people near it. One of the essential effort before the flood happen is how to know when the flood will come, far before it really happens, so the citizens that is settled around the river can have a chance to save themselves and their belongings. The purpose of this research is to build forecasting system which can forecast the coming time of flooding before it really comes. This research started by doing literature study and collecting water level data in some AWLR (Automatic Water Level Record) station which has good correlation toward to the area will be forecast, in this case Bojonegoro station, Babat station and Kuro station. All the data collected will be used as an input to build the forecasting model. The choosing of input variable that may affect output variable performed using correlation analysis. Forecasting method is using data driven model such as M5 Model Tree and Artificial Neural Network (ANN), which the learning process is performed by Weka Knowledge Explore software. Performance capability of both models is tested with verification test. From both of the models, forecasting model that chosen to forecast water level in Bojonegoro for the next 1, 3 and 6 hour is M5 Model Tree with RMSE (Root Mean Square Error) verification value about 0.278 0.717, while for Babat and Kuro, the model that chosen for the next 6 hour is ANN model with RMSE verification value 0.432 and 0.154.

Contributor:
  1. DR. Ir. EDIJATNO
Date Create:22/02/2010
Type:Text
Format:pdf
Language:Indonesian
Identifier:ITS-Master-3100010037305
Collection ID:3100010037305
Call Number:RSE 627.4 Mul m


Source :
Master Theses, Civil Engineering, RTS 627.4 Mul m, 2009

Coverage :
ITS Community Only

Rights :
Copyright @2009 by ITS Library. This publication is protected by copyright and permission should be obtained from the ITS Library prior to any prohibited reproduction, storage in a retrievel system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. For information regarding permission(s), write to ITS Library


Publication URL :
http://digilib.its.ac.id/model-peramalan-banjir-di-das-bengawan-solo-8526.html




[ Free Download - Free for All ]

  1.  ITS-Master-8526-3107205711- MODEL PERAMALAN BANJIR DI DAS BENGAWAN SOLO.pdf - 86 KB
  2.  ITS-Master-8526-3107205711- FLOOD FORECASTING MODEL OF BENGAWAN SOLO CATCHMENT AREA.pdf - 86 KB
  3.  ITS-Master-8526-3107205711- Approval_Sheet.pdf - 46 KB
  4.  ITS-Master-8526-3107205711- Abstract_in.pdf - 89 KB
  5.  ITS-Master-8526-3107205711- Abstract_en.pdf - 88 KB
  6.  ITS-Master-8526-3107205711- Table_of_Content.pdf - 87 KB
  7.  ITS-Master-8526-3107205711- Tables.pdf - 85 KB
  8.  ITS-Master-8526-3107205711- Illustrations.pdf - 106 KB
  9.  ITS-Master-8526-3107205711- Bibliography.pdf - 121 KB
  10.  ITS-Master-8526-3107205711- Chapter1.pdf - 350 KB
  11.  ITS-Master-8526-3107205711- Coclusion.pdf - 144 KB

[ FullText Content - Please, register first ]

  1. ITS-Master-8526-3107205711- Chapter2.pdf - 309 KB
  2. ITS-Master-8526-3107205711- Chapter3.pdf - 966 KB
  3. ITS-Master-8526-3107205711- Chapter4A.pdf - 1446 KB
  4. ITS-Master-8526-3107205711- Chapter4B.pdf - 491 KB
  5. ITS-Master-8526-3107205711- Chapter4C.pdf - 1055 KB
  6. ITS-Master-8526-3107205711- Chapter4D.pdf - 1576 KB
  7. ITS-Master-8526-3107205711- Chapter4E.pdf - 1316 KB
  8. ITS-Master-8526-3107205711- Enclosure.pdf - 542 KB



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Artificial , Artificial Neural Network , Bengawan , Data , Data Driven Model , Driven , Elevasi , Elevasi muka air Bengawan Solo , M5 , M5 Model Tree , Model , Model peramalan , Network , Neural , RMSE , Solo , Tree , air , muka , peramalan



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