Collection of historical weather data: issues with missing value

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Title: Collection of historical weather data: issues with missing value
Authors: Rafii, FadouaKechadi, Tahar
Permanent link: http://hdl.handle.net/10197/11698
Date: 4-Oct-2019
Online since: 2020-11-13T08:32:37Z
Abstract: Weather data collected from automated weather stations have become a crucial component for making decisions in agriculture and in forestry. Over time, weather stations may become out-oforder or stopped for maintenance, and therefore, during those periods, the data values will be missing. Unfortunately, this will cause huge problems when analysing the data. The main aim of this study is to create high-quality historical weather datasets by dealing efficiently with missing values. In this paper, we present a set of missing data imputation methods and study their effectiveness. These methods were used based on different types of missing values. The experimental results show that two the proposed methods are very promising and can be used at larger scale.
Funding Details: Science Foundation Ireland
Funding Details: Insight Research Centre
Origin Enterprises Plc
Type of material: Conference Publication
Publisher: ACM
Copyright (published version): 2019 ACM
Keywords: TemperatureRainfallMissing dataImputation methodsRMSE
DOI: 10.1145/3368756.3368974
Other versions: http://www.medi-ast.org/sca19/index.html
Language: en
Status of Item: Peer reviewed
Is part of: Ahmed Mohamed, B., Rakip Karaso, I., Saadne, R., Mtalaa, Wassila, Anouar Abdelhakim, B. (eds.). SCA '19: Proceedings of the 4th International Conference on Smart City Applications
Conference Details: The 4th International Conference on Smart City Applications (SCA'19), Casablanca, Morocco, 2-4 October 2019
ISBN: 978-1-4503-6289-4
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Computer Science Research Collection
Insight Research Collection

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