Assessment of [GIS] spatial interpolation methods in estimating rainfall missing data / Norazimah Hani Ismail

Rainfall is an important data to identify the complete rainfall record at the gauging station. There is an incompleted rainfall data due to various factors such absence of the observer and the instrument failures. Thus, to fill the gaps of missing observation in data, several techniques were used...

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Bibliographic Details
Main Author:	Ismail, Norazimah Hani
Format:	Thesis
Language:	English
Published:	2018
Subjects:	Geographic information systems Rain and rainfall
Online Access:	https://ir.uitm.edu.my/id/eprint/22447/1/TD_NORAZIMAH%20HANI%20ISMAIL%20AP%20R%2018_5.PDF
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id	my-uitm-ir.22447
record_format	uketd_dc
spelling	my-uitm-ir.224472018-12-18T08:24:01Z Assessment of [GIS] spatial interpolation methods in estimating rainfall missing data / Norazimah Hani Ismail 2018-12 Ismail, Norazimah Hani Geographic information systems Rain and rainfall Rainfall is an important data to identify the complete rainfall record at the gauging station. There is an incompleted rainfall data due to various factors such absence of the observer and the instrument failures. Thus, to fill the gaps of missing observation in data, several techniques were used to predict the missing rainfall data. The aim of this study is to assess GIS spatial interpolation methods in estimating rainfall missing data using Inverse Distance Weighted (IDW), Thiessen Polygon and Kriging in Northern region of Malaysia. Next, the objectives of this study are to generate rainfall spatial interpolation data based on IDW, Thiessen Polygon and Kriging as well as to assess the accuracy of estimated rainfall values for each spatial interpolation methods. The research study area focuses only in the Northern Region of Peninsular Malaysia which is Pulau Pinang, Kedah, Perak and Perils. In this study, 15 out of 143 rainfall stations with completed rainfall data were estimated with monthly basis. The most suitable method in accuracy for each methods were compared based on Root Mean Square Error (RMSE). Overall the best RMSE is found in IDW on January is (16.691) following by the worst RMSE in Thiessen Polygon on November is (2233.526). However, the RMSE for Kriging is the most consistent by annually. The finding of this study shows that Kriging is the most accurate GIS spatial interpolation method in estimating rainfall missing data. Thus, Kriging Interpolation is possible to be used to improve the conventional methods of estimating rainfall missing data. 2018-12 Thesis https://ir.uitm.edu.my/id/eprint/22447/ https://ir.uitm.edu.my/id/eprint/22447/1/TD_NORAZIMAH%20HANI%20ISMAIL%20AP%20R%2018_5.PDF other en public degree Universiti Teknologi Mara Perlis Faculty of Architecture, Planning and Surveying
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Geographic information systems Rain and rainfall
spellingShingle	Geographic information systems Rain and rainfall Ismail, Norazimah Hani Assessment of [GIS] spatial interpolation methods in estimating rainfall missing data / Norazimah Hani Ismail
description	Rainfall is an important data to identify the complete rainfall record at the gauging station. There is an incompleted rainfall data due to various factors such absence of the observer and the instrument failures. Thus, to fill the gaps of missing observation in data, several techniques were used to predict the missing rainfall data. The aim of this study is to assess GIS spatial interpolation methods in estimating rainfall missing data using Inverse Distance Weighted (IDW), Thiessen Polygon and Kriging in Northern region of Malaysia. Next, the objectives of this study are to generate rainfall spatial interpolation data based on IDW, Thiessen Polygon and Kriging as well as to assess the accuracy of estimated rainfall values for each spatial interpolation methods. The research study area focuses only in the Northern Region of Peninsular Malaysia which is Pulau Pinang, Kedah, Perak and Perils. In this study, 15 out of 143 rainfall stations with completed rainfall data were estimated with monthly basis. The most suitable method in accuracy for each methods were compared based on Root Mean Square Error (RMSE). Overall the best RMSE is found in IDW on January is (16.691) following by the worst RMSE in Thiessen Polygon on November is (2233.526). However, the RMSE for Kriging is the most consistent by annually. The finding of this study shows that Kriging is the most accurate GIS spatial interpolation method in estimating rainfall missing data. Thus, Kriging Interpolation is possible to be used to improve the conventional methods of estimating rainfall missing data.
format	Thesis
qualification_level	Bachelor degree
author	Ismail, Norazimah Hani
author_facet	Ismail, Norazimah Hani
author_sort	Ismail, Norazimah Hani
title	Assessment of [GIS] spatial interpolation methods in estimating rainfall missing data / Norazimah Hani Ismail
title_short	Assessment of [GIS] spatial interpolation methods in estimating rainfall missing data / Norazimah Hani Ismail
title_full	Assessment of [GIS] spatial interpolation methods in estimating rainfall missing data / Norazimah Hani Ismail
title_fullStr	Assessment of [GIS] spatial interpolation methods in estimating rainfall missing data / Norazimah Hani Ismail
title_full_unstemmed	Assessment of [GIS] spatial interpolation methods in estimating rainfall missing data / Norazimah Hani Ismail
title_sort	assessment of [gis] spatial interpolation methods in estimating rainfall missing data / norazimah hani ismail
granting_institution	Universiti Teknologi Mara Perlis
granting_department	Faculty of Architecture, Planning and Surveying
publishDate	2018
url	https://ir.uitm.edu.my/id/eprint/22447/1/TD_NORAZIMAH%20HANI%20ISMAIL%20AP%20R%2018_5.PDF
_version_	1783733818933379072

Assessment of [GIS] spatial interpolation methods in estimating rainfall missing data / Norazimah Hani Ismail

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