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008			191010t20192019my a|||fram|| 000 0 eng d
020			_aTHE0008298(Local) _qhardback
040			_aUMP _beng _cUMP _erda
090			_aFKASA .A93 2019 r Bc.
100	0		_aNur Awatif Ahmad Shukri, _eauthor.
245	1	0	_aAssessment of the ungauge rainfall forecasting using SDSM-GIS / _cNur Awatif Ahmad Shukri
264		1	_aKuantan, Pahang : _bUMP, _c2019
264		4	_c© 2019
300			_axi , 84 pages : _billustrations ; _c30 cm. + _e1 CD-ROM
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
337			_acomputer _2rdamedia
338			_avolume _2rdacarrier
338			_acomputer disc _2rdacarrier
347			_atext file _bPDF _2rda
500			_aFaculty of Civil Engineering and Earth Resources
502			_aProject Paper (Bachelors of Civil Engineering) -- Universiti Malaysia Pahang – 2019
504			_aIncludes bibliographical references
520	3		_aAn accuracy in the hydrological modelling will be effected when having limited data sources especially at ungauged areas. Due to this matter, it will not receiving any significant attention especially on the potential hydrologic extremes. Three of rainfall stations Pam Paya Pinang station, Paya Besar station and Kg. Sg. Soi acr oss Kuantan river were considered in this research. Thus, the objective was to analyses the accuracy of the long-term projected rainfall at ungauged rainfall station using integrated SDSM GIS model. The SDSM was used as a climate agent to predict the changes of the climate trend in ∆ 2030s by gauged stations. Five predictors were selected to form the local climate at the region which provided by NCEP (validated) and CanESM2-RCP4.5 (projected). According to the statistical analyses, the SDSM was successfully to produced reliable validated results with lesser % MAE (<23%) and higher R (1.0). The projected rainfall was suspected to decrease 14% in ∆2030s. These findings then used to compare the accuracy of monthly rainfall at ungauged station (Stn 2). The GIS-Kriging method being as an interpolation agent to treat Stn 2. Meanwhile, the next objective was to estimate the accuracy of the forecasted monthly rainfall using Kriging-GIS interpolation. Comparing between ungauged and gauged stations, the small %MAE in the projected monthly results between gauged and ungauged stations as a proved the integrated SDSMGIS model can producing a reliable long-term rainfall generation at ungauged station(station 2). Based on the performance GIS interpolation, for the result its historical rainfall (JPS) and projected rainfall between gauged and ungauged stations can be accepted because the difference in percentage error of MAE is less than 30%. In July was recorded value with higher error in MAE with 26.6% for historical rainfall. While the higher error for projected rainfall is 25.81% which happened in December.
610	2	0	_aFaculty of Civil Engineering and Earth Resources _xDissertations
650		0	_aUniversities and Colleges _xDissertations
650		0	_aTheses
942			_2lcc _cPSM