Rainfall Prediction System Using Machine Learning Fusion for Smart Cities
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Rahman, Atta-ur | - |
dc.contributor.author | Abbas, Sagheer | - |
dc.contributor.author | Gollapalli, Mohammed | - |
dc.contributor.author | Ahmed, Rashad | - |
dc.contributor.author | Aftab, Shabib | - |
dc.contributor.author | Ahmad, Munir | - |
dc.contributor.author | Khan, Muhammad Adnan | - |
dc.contributor.author | Mosavi, Amir | - |
dc.date.accessioned | 2022-05-25T08:40:05Z | - |
dc.date.available | 2022-05-25T08:40:05Z | - |
dc.date.created | 2022-05-25 | - |
dc.date.issued | 2022-05 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/84428 | - |
dc.description.abstract | Precipitation in any form-such as rain, snow, and hail-can affect day-to-day outdoor activities. Rainfall prediction is one of the challenging tasks in weather forecasting process. Accurate rainfall prediction is now more difficult than before due to the extreme climate variations. Machine learning techniques can predict rainfall by extracting hidden patterns from historical weather data. Selection of an appropriate classification technique for prediction is a difficult job. This research proposes a novel real-time rainfall prediction system for smart cities using a machine learning fusion technique. The proposed framework uses four widely used supervised machine learning techniques, i.e., decision tree, Naive Bayes, K-nearest neighbors, and support vector machines. For effective prediction of rainfall, the technique of fuzzy logic is incorporated in the framework to integrate the predictive accuracies of the machine learning techniques, also known as fusion. For prediction, 12 years of historical weather data (2005 to 2017) for the city of Lahore is considered. Pre-processing tasks such as cleaning and normalization were performed on the dataset before the classification process. The results reflect that the proposed machine learning fusion-based framework outperforms other models. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | SENSORS | - |
dc.title | Rainfall Prediction System Using Machine Learning Fusion for Smart Cities | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000794708000001 | - |
dc.identifier.doi | 10.3390/s22093504 | - |
dc.identifier.bibliographicCitation | SENSORS, v.22, no.9 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85129373880 | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 22 | - |
dc.citation.number | 9 | - |
dc.contributor.affiliatedAuthor | Khan, Muhammad Adnan | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | rainfall | - |
dc.subject.keywordAuthor | rainfall prediction | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | data fusion | - |
dc.subject.keywordAuthor | fuzzy system | - |
dc.subject.keywordAuthor | smart cities | - |
dc.subject.keywordAuthor | big data | - |
dc.subject.keywordAuthor | hydrological model | - |
dc.subject.keywordAuthor | information systems | - |
dc.subject.keywordAuthor | precipitation | - |
dc.subject.keywordPlus | DATA MINING TECHNIQUES | - |
dc.subject.keywordPlus | SVM | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.