dc.contributor.author |
Kaya, SM
|
|
dc.contributor.author |
Isler, B
|
|
dc.contributor.author |
Abu-Mahfouz, Adnan MI
|
|
dc.contributor.author |
Rasheed, J
|
|
dc.contributor.author |
AlShammari, A
|
|
dc.date.accessioned |
2023-07-21T09:09:49Z |
|
dc.date.available |
2023-07-21T09:09:49Z |
|
dc.date.issued |
2023-02 |
|
dc.identifier.citation |
Kaya, S., Isler, B., Abu-Mahfouz, A.M., Rasheed, J. & AlShammari, A. 2023. An intelligent anomaly detection approach for accurate and reliable weather forecasting at IoT edges: A case study. <i>Sensors, 23(5).</i> http://hdl.handle.net/10204/12903 |
en_ZA |
dc.identifier.issn |
1424-8220 |
|
dc.identifier.issn |
1424-3210 |
|
dc.identifier.uri |
https://doi.org/10.3390/s23052426
|
|
dc.identifier.uri |
http://hdl.handle.net/10204/12903
|
|
dc.description.abstract |
Industrialization and rapid urbanization in almost every country adversely affect many of our environmental values, such as our core ecosystem, regional climate differences and global diversity. The difficulties we encounter as a result of the rapid change we experience cause us to encounter many problems in our daily lives. The background of these problems is rapid digitalization and the lack of sufficient infrastructure to process and analyze very large volumes of data. Inaccurate, incomplete or irrelevant data produced in the IoT detection layer causes weather forecast reports to drift away from the concepts of accuracy and reliability, and as a result, activities based on weather forecasting are disrupted. A sophisticated and difficult talent, weather forecasting needs the observation and processing of enormous volumes of data. In addition, rapid urbanization, abrupt climate changes and mass digitization make it more difficult for the forecasts to be accurate and reliable. Increasing data density and rapid urbanization and digitalization make it difficult for the forecasts to be accurate and reliable. This situation prevents people from taking precautions against bad weather conditions in cities and rural areas and turns into a vital problem. In this study, an intelligent anomaly detection approach is presented to minimize the weather forecasting problems that arise as a result of rapid urbanization and mass digitalization. The proposed solutions cover data processing at the edge of the IoT and include filtering out the missing, unnecessary or anomaly data that prevent the predictions from being more accurate and reliable from the data obtained through the sensors. Anomaly detection metrics of five different machine learning (ML) algorithms, including support vector classifier (SVC), Adaboost, logistic regression (LR), naive Bayes (NB) and random forest (RF), were also compared in the study. These algorithms were used to create a data stream using the time, temperature, pressure, humidity and other sensor-generated information. |
en_US |
dc.format |
Fulltext |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://www.mdpi.com/1424-8220/23/5/2426 |
en_US |
dc.source |
Sensors, 23(5) |
en_US |
dc.subject |
Edge computing |
en_US |
dc.subject |
Internet-of-things |
en_US |
dc.subject |
IoT |
en_US |
dc.subject |
Data pre-processing |
en_US |
dc.subject |
Weather forecasting |
en_US |
dc.title |
An intelligent anomaly detection approach for accurate and reliable weather forecasting at IoT edges: A case study |
en_US |
dc.type |
Article |
en_US |
dc.description.pages |
17pp |
en_US |
dc.description.note |
Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
en_US |
dc.description.cluster |
Next Generation Enterprises & Institutions |
en_US |
dc.description.impactarea |
EDT4IR Management |
en_US |
dc.identifier.apacitation |
Kaya, S., Isler, B., Abu-Mahfouz, A. M., Rasheed, J., & AlShammari, A. (2023). An intelligent anomaly detection approach for accurate and reliable weather forecasting at IoT edges: A case study. <i>Sensors, 23(5)</i>, http://hdl.handle.net/10204/12903 |
en_ZA |
dc.identifier.chicagocitation |
Kaya, SM, B Isler, Adnan MI Abu-Mahfouz, J Rasheed, and A AlShammari "An intelligent anomaly detection approach for accurate and reliable weather forecasting at IoT edges: A case study." <i>Sensors, 23(5)</i> (2023) http://hdl.handle.net/10204/12903 |
en_ZA |
dc.identifier.vancouvercitation |
Kaya S, Isler B, Abu-Mahfouz AM, Rasheed J, AlShammari A. An intelligent anomaly detection approach for accurate and reliable weather forecasting at IoT edges: A case study. Sensors, 23(5). 2023; http://hdl.handle.net/10204/12903. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Kaya, SM
AU - Isler, B
AU - Abu-Mahfouz, Adnan MI
AU - Rasheed, J
AU - AlShammari, A
AB - Industrialization and rapid urbanization in almost every country adversely affect many of our environmental values, such as our core ecosystem, regional climate differences and global diversity. The difficulties we encounter as a result of the rapid change we experience cause us to encounter many problems in our daily lives. The background of these problems is rapid digitalization and the lack of sufficient infrastructure to process and analyze very large volumes of data. Inaccurate, incomplete or irrelevant data produced in the IoT detection layer causes weather forecast reports to drift away from the concepts of accuracy and reliability, and as a result, activities based on weather forecasting are disrupted. A sophisticated and difficult talent, weather forecasting needs the observation and processing of enormous volumes of data. In addition, rapid urbanization, abrupt climate changes and mass digitization make it more difficult for the forecasts to be accurate and reliable. Increasing data density and rapid urbanization and digitalization make it difficult for the forecasts to be accurate and reliable. This situation prevents people from taking precautions against bad weather conditions in cities and rural areas and turns into a vital problem. In this study, an intelligent anomaly detection approach is presented to minimize the weather forecasting problems that arise as a result of rapid urbanization and mass digitalization. The proposed solutions cover data processing at the edge of the IoT and include filtering out the missing, unnecessary or anomaly data that prevent the predictions from being more accurate and reliable from the data obtained through the sensors. Anomaly detection metrics of five different machine learning (ML) algorithms, including support vector classifier (SVC), Adaboost, logistic regression (LR), naive Bayes (NB) and random forest (RF), were also compared in the study. These algorithms were used to create a data stream using the time, temperature, pressure, humidity and other sensor-generated information.
DA - 2023-02
DB - ResearchSpace
DP - CSIR
J1 - Sensors, 23(5)
KW - Edge computing
KW - Internet-of-things
KW - IoT
KW - Data pre-processing
KW - Weather forecasting
LK - https://researchspace.csir.co.za
PY - 2023
SM - 1424-8220
SM - 1424-3210
T1 - An intelligent anomaly detection approach for accurate and reliable weather forecasting at IoT edges: A case study
TI - An intelligent anomaly detection approach for accurate and reliable weather forecasting at IoT edges: A case study
UR - http://hdl.handle.net/10204/12903
ER -
|
en_ZA |
dc.identifier.worklist |
26868 |
en_US |