{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T21:18:54Z","timestamp":1779311934863,"version":"3.51.4"},"reference-count":34,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T00:00:00Z","timestamp":1651622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Precipitation in any form\u2014such as rain, snow, and hail\u2014can 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, Na\u00efve 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.<\/jats:p>","DOI":"10.3390\/s22093504","type":"journal-article","created":{"date-parts":[[2022,5,4]],"date-time":"2022-05-04T10:49:30Z","timestamp":1651661370000},"page":"3504","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":135,"title":["Rainfall Prediction System Using Machine Learning Fusion for Smart Cities"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6696-277X","authenticated-orcid":false,"given":"Atta-ur","family":"Rahman","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sagheer","family":"Abbas","sequence":"additional","affiliation":[{"name":"School of Computer Science, National College of Business Administration and Economics, Lahore 54000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7521-5757","authenticated-orcid":false,"given":"Mohammed","family":"Gollapalli","sequence":"additional","affiliation":[{"name":"Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rashad","family":"Ahmed","sequence":"additional","affiliation":[{"name":"ICS Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7662-1394","authenticated-orcid":false,"given":"Shabib","family":"Aftab","sequence":"additional","affiliation":[{"name":"School of Computer Science, National College of Business Administration and Economics, Lahore 54000, Pakistan"},{"name":"Department of Computer Science, Virtual University of Pakistan, Lahore 54000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5240-0984","authenticated-orcid":false,"given":"Munir","family":"Ahmad","sequence":"additional","affiliation":[{"name":"School of Computer Science, National College of Business Administration and Economics, Lahore 54000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4854-9935","authenticated-orcid":false,"given":"Muhammad Adnan","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Software, Gachon University, Seongnam 13120, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4842-0613","authenticated-orcid":false,"given":"Amir","family":"Mosavi","sequence":"additional","affiliation":[{"name":"John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary"},{"name":"Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, 81107 Bratislava, Slovakia"},{"name":"Faculty of Civil Engineering, TU-Dresden, 01062 Dresden, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,4]]},"reference":[{"key":"ref_1","first-page":"254","article-title":"Rainfall Prediction in Lahore City using Data Mining Techniques","volume":"9","author":"Aftab","year":"2018","journal-title":"Int. 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