{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T14:48:24Z","timestamp":1777387704595,"version":"3.51.4"},"reference-count":28,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T00:00:00Z","timestamp":1691366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>At present, there is no suitable instrument available to simulate modeling the thermal performance of various areas of our states due to its complicated meteorological behavior. To accurately predict a heatwave, we studied the research gaps and current ongoing research on the prediction of heatwaves. For the accurate prediction of a heatwave, we considered two soft computing concepts, (a) Rough Set Theory (RST) and (b) Support Vector Machine (SVM). All the ongoing research on the prediction of heatwaves is based on future predictions with an error margin. All the available techniques use a particular pattern of heatwave data, and these methods do not apply to vague data. This paper used an innovative RST and SVM technique, which can be applied to vague and imprecise datasets to produce the best outcomes. RST is helpful in finding the most significant attributes that will be alarming in the future. This analysis identifies the heat wave as the most prominent characteristic among various meteorological data. SVM is responsible for the future prediction of heat waves, which includes various parameters. By further classification of heatwaves, we found that a lack of greenery will increase the heatwave in the future. Although the survey was conducted based on a sampling distribution, we expect this result to represent the population as we collected our sample in a heterogeneous environment. These outcomes are validated using a statistical method.<\/jats:p>","DOI":"10.3390\/info14080447","type":"journal-article","created":{"date-parts":[[2023,8,8]],"date-time":"2023-08-08T12:38:59Z","timestamp":1691498339000},"page":"447","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Prediction of Heatwave Using Advanced Soft Computing Technique"],"prefix":"10.3390","volume":"14","author":[{"given":"Ratnakar","family":"Das","sequence":"first","affiliation":[{"name":"Department of Computer Science & Application, Odisha University of Technology and Research, Bhubaneswar 751029, Odisha, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jibitesh","family":"Mishra","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Application, Odisha University of Technology and Research, Bhubaneswar 751029, Odisha, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9551-4837","authenticated-orcid":false,"given":"Pradyumna Kumar","family":"Pattnaik","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Odisha University of Technology and Research, Bhubaneswar 751029, Odisha, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3219-7579","authenticated-orcid":false,"given":"Muhammad Mubashir","family":"Bhatti","sequence":"additional","affiliation":[{"name":"College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China"},{"name":"Material Science Innovation and Modelling (MaSIM) Research Focus Area, North-West University (Mafikeng Campus), Private Bag X2046, Mmabatho 2735, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100001","DOI":"10.1016\/j.pdisas.2019.100001","article-title":"Key Assessments from the IPCC Special Report on Global Warming of 1.5 \u00b0C and the Implications for the Sendai Framework for Disaster Risk Reduction","volume":"1","author":"Djalante","year":"2019","journal-title":"Prog. 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