Enhancing Coastal Dew Point Prediction: A Hybrid Deep Learning Framework for Southeastern Bangladesh

Enhancing Coastal Dew Point Prediction: A Hybrid Deep Learning Framework for Southeastern Bangladesh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Enhancing Coastal Dew Point Prediction: A Hybrid Deep Learning Framework for Southeastern Bangladesh Ishrat Jahan, Dipayan Bhadra, Mohammad Mahboob Hussain Khan, Rumana Rois This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8927564/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The dew point temperature is one of the main indicators of atmospheric humidity, and its accurate prediction is a vital factor in dynamic coastlands to help attain climate resilience, agriculture, and human health. This study proposes a new hybrid deep learning architecture, the Convolutional Neural Network-Residual Long Short-Term Memory-Concatenation Network (CNN-ResLSTM-ConNet) model that can be used to improve the dew point forecast in the Sitakunda coast area in southeastern Bangladesh. The model is a synergistic combination of the Convolutional Neural Networks (CNNs) to obtain spatial-level features, Long Short-Term Memory (LSTM) networks to consider the complex temporal dependencies, Residual Networks to overcome the problem of vanishing gradient, and the final multilayer perceptron (MLP) to optimize the dense layer. The proposed model was compared to the traditional time-series (TS) models, namely Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing State Space Model (ETS) and Trigonometric Box-Cox ARMA Trend Seasonal Model (TBATS); machine learning (ML) models, which are Support Vector Regression (SVR), Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost) and Prophet; seasonally adjusted TS and ML models; and deep learning (DL) models such as CNN, LSTM, MLP and Gated Recurrent Unit (GRU) based on 43 years (1981–2024) of daily data from the Bangladesh Meteorological Department (BMD). The CNN-ResLSTM-ConNet model performed better than other models in predictive ability with a RMSE of 1.063, a MAE of 0.735, a MAPE of 3.650, and a MASE of 0.868. The results show that the hybrid framework can accurately predict both short-term changes and long-term climate patterns and ensures a more accurate forecast which makes it a strong and dependable model for predicting dew point and planning for climate change in coastal Bangladesh. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Earth and environmental sciences/Natural hazards Dew Point Temperature Coastal climate Hybrid deep learning Residual Network Machine learning Time-series forecasting Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8927564","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":594727225,"identity":"1d964deb-9dd0-48be-ba2f-07dce4653805","order_by":0,"name":"Ishrat Jahan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIie3PvWrDMBAH8DOGdBHNekPTvIKNIHQw9aucMDiLk66eQkIhWUS7Om/RR5AJZHLfwV06ZfCYQqE90YZAQSndCtEfoQ+4H3cC8PH5h+mBOFxDY6jE62AOYPhpzxOEgI+eatvmRv5K4EiEjF+WpTpUOsll/3nbve0hTZFGSA2O76tJvRGQDJ6MYzCcZmtNoDRSjvyXyaKaEpNcuomQIAhIIG1tFyZFVFewUU7Sb2TwTpAKVEu7xsEX+XATKGTIXQKNWRgxIUtMB8ZNkMlVjko3r0HLg8ULvWMSZXLtIMNHHmyXJOnF6q6r9+VsGK8K2VF5O3hwkO/g8RrP7R6dLP/Z9y/FPj4+PmeRTyhGXM3lLQwfAAAAAElFTkSuQmCC","orcid":"","institution":"Jahangirnagar University","correspondingAuthor":true,"prefix":"","firstName":"Ishrat","middleName":"","lastName":"Jahan","suffix":""},{"id":594727226,"identity":"968d95e8-a810-4767-9b0f-fc82dad20355","order_by":1,"name":"Dipayan Bhadra","email":"","orcid":"","institution":"Jahangirnagar University","correspondingAuthor":false,"prefix":"","firstName":"Dipayan","middleName":"","lastName":"Bhadra","suffix":""},{"id":594727227,"identity":"dc2209d0-65b1-4292-b762-34ef33ddb5ef","order_by":2,"name":"Mohammad Mahboob Hussain Khan","email":"","orcid":"","institution":"Jahangirnagar University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Mahboob Hussain","lastName":"Khan","suffix":""},{"id":594727228,"identity":"e821c952-4ce2-4dff-ae65-924e8da7504a","order_by":3,"name":"Rumana Rois","email":"","orcid":"","institution":"Jahangirnagar University","correspondingAuthor":false,"prefix":"","firstName":"Rumana","middleName":"","lastName":"Rois","suffix":""}],"badges":[],"createdAt":"2026-02-20 16:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8927564/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8927564/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103505270,"identity":"4dfb4fda-644b-4b41-b4b4-66080e3b4c38","added_by":"auto","created_at":"2026-02-26 13:29:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1339605,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8927564/v1_covered_c12de765-2cd5-42c4-8270-2e9d595a93b3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Coastal Dew Point Prediction: A Hybrid Deep Learning Framework for Southeastern Bangladesh","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dew Point Temperature, Coastal climate, Hybrid deep learning, Residual Network, Machine learning, Time-series forecasting","lastPublishedDoi":"10.21203/rs.3.rs-8927564/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8927564/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe dew point temperature is one of the main indicators of atmospheric humidity, and its accurate prediction is a vital factor in dynamic coastlands to help attain climate resilience, agriculture, and human health. This study proposes a new hybrid deep learning architecture, the Convolutional Neural Network-Residual Long Short-Term Memory-Concatenation Network (CNN-ResLSTM-ConNet) model that can be used to improve the dew point forecast in the Sitakunda coast area in southeastern Bangladesh. The model is a synergistic combination of the Convolutional Neural Networks (CNNs) to obtain spatial-level features, Long Short-Term Memory (LSTM) networks to consider the complex temporal dependencies, Residual Networks to overcome the problem of vanishing gradient, and the final multilayer perceptron (MLP) to optimize the dense layer. The proposed model was compared to the traditional time-series (TS) models, namely Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing State Space Model (ETS) and Trigonometric Box-Cox ARMA Trend Seasonal Model (TBATS); machine learning (ML) models, which are Support Vector Regression (SVR), Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost) and Prophet; seasonally adjusted TS and ML models; and deep learning (DL) models such as CNN, LSTM, MLP and Gated Recurrent Unit (GRU) based on 43 years (1981\u0026ndash;2024) of daily data from the Bangladesh Meteorological Department (BMD). The CNN-ResLSTM-ConNet model performed better than other models in predictive ability with a RMSE of 1.063, a MAE of 0.735, a MAPE of 3.650, and a MASE of 0.868. The results show that the hybrid framework can accurately predict both short-term changes and long-term climate patterns and ensures a more accurate forecast which makes it a strong and dependable model for predicting dew point and planning for climate change in coastal Bangladesh.\u003c/p\u003e","manuscriptTitle":"Enhancing Coastal Dew Point Prediction: A Hybrid Deep Learning Framework for Southeastern Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 07:20:57","doi":"10.21203/rs.3.rs-8927564/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e622b09b-b0b0-4445-ae83-6ecae364e084","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63292391,"name":"Earth and environmental sciences/Climate sciences"},{"id":63292392,"name":"Earth and environmental sciences/Environmental sciences"},{"id":63292393,"name":"Physical sciences/Mathematics and computing"},{"id":63292394,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2026-02-23T08:56:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 07:20:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8927564","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8927564","identity":"rs-8927564","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}