| 000 | 02535ntm a2200289 a 4500 | ||
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| 001 | vtls000092187 | ||
| 003 | KUKTEM | ||
| 005 | 20251117113300.0 | ||
| 008 | 151123t2015 my da f abm 000 0 eng d | ||
| 020 | _aTHE0000232(Local) | ||
| 039 | 9 |
_a201905141455 _bnazirah _c201711291005 _dsaini _y201511231001 _zasma |
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| 040 | _aUMP | ||
| 090 | _aFKASA .A357 2015 r Bc. | ||
| 100 | 0 | _aMuhamad Afiq Mustafa | |
| 245 | 1 | 0 |
_aPrediction of Temerloh River water level for prediction of flood using Artificial Neural Network (ANN) method / _cMuhamad Afiq Mustafa |
| 260 |
_aKuantan, Pahang : _bUMP, _c2015 |
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| 300 |
_axii, 54 p. : _bill. (some col.) ; _c30 cm. + _e1 CD ROM |
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| 500 | _aFaculty of Civil Engineering and Earth Resources | ||
| 502 | _aProject Paper (Bachelors of Civil Engineering) -- Universiti Malaysia Pahang – 2015 | ||
| 504 | _aBibliography : p. 48-50 | ||
| 520 | 3 | _aThe purpose of this project is to research more about the flood occurrence in Temerloh, Pahang. The data mining approaches using artificial neural network (ANN) techniques will be use to conduct this research for flood estimation. ANN model will be use to estimate river water level by taking present river water level data. The research will be trained using back propagation method to estimate the flood water level at Temerloh River. ANN’s trained using backpropagation are also known as “feed forward multilayered networks” trained using the backpropagation algorithm. 14 years of rainfall data is get from Department of irrigation and drainage (DID). Rainfall data of 10 years(2000- 2010) will be training data to predict the others 4 years(2010-2014) river water level using python software with 1000-4000 iteration of data. At the end of the project we can make parameter model that can use as a tools to predict accurately water level data and achieve high accuracy of flood forecasting. From the result we can see that in this research the best prediction for water level data at Temerloh River is 3-hr lead-time with 6 input 1 output in 4000 iteration because it produce the best CE with 0.998. The average RMSE also less than 500 mm with only small difference error in percentage | |
| 610 | 2 | 0 |
_aFaculty of Civil Engineering and Earth Resources _xDissertations |
| 650 | 0 |
_aUniversities and Colleges _xDissertations |
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| 650 | 0 | _aTheses | |
| 856 | 4 | 0 |
_uhttp://ecollib.ump.edu.my/id/eprint/25903 _zAccess in library only |
| 999 |
_aVIRTUA40 _c6045 _d6051 |
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