TY - GEN
T1 - Forecasting Winter Precipitation based on Weather Sensors Data in Apache Spark
AU - Kanavos, Andreas
AU - Panagiotakopoulos, Theodor
AU - Vonitsanos, Gerasimos
AU - Maragoudakis, Manolis
AU - Kiouvrekis, Yiannis
N1 - Funding Information:
ACKNOWLEDGEMENT Supported by the Erasmus+ KA2 under the project DE-VOPS, “DevOps competences for Smart Cities” (Project No.: 601015-EPP-1-2018-1-EL-EPPKA2-SSA Erasmus+ Program, KA2: Cooperation for innovation and the exchange of good practices-Sector Skills Alliances, started in 2019, January 1).
Publisher Copyright:
© 2021 IEEE.
PY - 2021/7/12
Y1 - 2021/7/12
N2 - The proposed paper introduces an approach providing weather information on winter precipitation types using machine learning techniques. The proposed methodology takes as input the data received from weather sensors and in following the winter precipitation model aims at forecasting the weather type given three precipitation classes, namely rain, freezing rain, and snow, as registered in the Automated Surface Observing System (ASOS). To enable the proposed classification, six supervised machine learning models were selected: Naive Bayes, Decision Stump, Hoeffding Tree, HoeffdingOption Tree, HoeffdingAdaptive Tree, and OzaBag. Results depicted that all the models performed well in terms of accuracy and computation time, while some achieved even better outcomes. Specifically, among all six models, OzaBag presented the best classification results, followed by HoeffdingOption Tree.
AB - The proposed paper introduces an approach providing weather information on winter precipitation types using machine learning techniques. The proposed methodology takes as input the data received from weather sensors and in following the winter precipitation model aims at forecasting the weather type given three precipitation classes, namely rain, freezing rain, and snow, as registered in the Automated Surface Observing System (ASOS). To enable the proposed classification, six supervised machine learning models were selected: Naive Bayes, Decision Stump, Hoeffding Tree, HoeffdingOption Tree, HoeffdingAdaptive Tree, and OzaBag. Results depicted that all the models performed well in terms of accuracy and computation time, while some achieved even better outcomes. Specifically, among all six models, OzaBag presented the best classification results, followed by HoeffdingOption Tree.
KW - Apache Cassandra
KW - Apache Spark
KW - Classification
KW - Machine Learning
KW - Winter Precipitation Forecasting
UR - http://www.scopus.com/inward/record.url?scp=85117443909&partnerID=8YFLogxK
U2 - 10.1109/IISA52424.2021.9555553
DO - 10.1109/IISA52424.2021.9555553
M3 - Conference contribution
AN - SCOPUS:85117443909
T3 - IISA 2021 - 12th International Conference on Information, Intelligence, Systems and Applications
BT - IISA 2021 - 12th International Conference on Information, Intelligence, Systems and Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021
Y2 - 12 July 2021 through 14 July 2021
ER -