aspg-18-3952-1792025453 20261015005053 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2026 18 1 L. Pallavi Associate Professor, Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Telangana, India Gattu Shravani Assistant Professor, Dept of CSE (Cys, DS, AI&DS), VNRVJIET, Bachupally, Hyderabad Telangana -500090, India J. Sirisha Devi Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad 500043, Telangana, India Bandaru Satya Lakshmi Assistant Professor, Computer Science and Engineering, Aditya University, Surampalem, Andhra Pradesh, India M. Pushpalatha Assistant Professor, Department of Computer science and Technology, Karpagam College of Engineering, Tamil Nadu, India S. Gopinath Assistant Professor, Gnanamani College of Technology, Namakkal, Tamilnadu-637018, India M. Rajesh Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (DU), Tamilnadu, India Normal false false false EN-US X-NONE AR-SA DefSemiHidden="false" DefQFormat="false" DefPriority="99" LatentStyleCount="371"> UnhideWhenUsed="true" QFormat="true" Name="heading 2"/> UnhideWhenUsed="true" QFormat="true" Name="heading 3"/> UnhideWhenUsed="true" QFormat="true" Name="heading 4"/> UnhideWhenUsed="true" QFormat="true" Name="heading 5"/> UnhideWhenUsed="true" QFormat="true" Name="heading 6"/> UnhideWhenUsed="true" QFormat="true" Name="heading 7"/> UnhideWhenUsed="true" QFormat="true" Name="heading 8"/> UnhideWhenUsed="true" QFormat="true" Name="heading 9"/> Name="index 1"/> Name="index 2"/> Name="index 3"/> Name="index 4"/> Name="index 5"/> Name="index 6"/> Name="index 7"/> Name="index 8"/> Name="index 9"/> UnhideWhenUsed="true" Name="toc 1"/> UnhideWhenUsed="true" Name="toc 2"/> UnhideWhenUsed="true" Name="toc 3"/> UnhideWhenUsed="true" Name="toc 4"/> UnhideWhenUsed="true" Name="toc 5"/> UnhideWhenUsed="true" Name="toc 6"/> UnhideWhenUsed="true" Name="toc 7"/> UnhideWhenUsed="true" Name="toc 8"/> UnhideWhenUsed="true" Name="toc 9"/> Name="Normal Indent"/> Name="footnote text"/> Name="annotation text"/> UnhideWhenUsed="true" Name="header"/> Name="footer"/> Name="index heading"/> UnhideWhenUsed="true" QFormat="true" Name="caption"/> Name="table of figures"/> Name="envelope address"/> Name="envelope return"/> Name="footnote reference"/> Name="annotation reference"/> Name="line number"/> Name="page number"/> Name="endnote reference"/> Name="endnote text"/> Name="table of authorities"/> Name="macro"/> Name="toa heading"/> Name="List"/> Name="List Bullet"/> Name="List Number"/> Name="List 2"/> Name="List 3"/> Name="List 4"/> Name="List 5"/> Name="List Bullet 2"/> Name="List Bullet 3"/> Name="List Bullet 4"/> Name="List Bullet 5"/> Name="List Number 2"/> Name="List Number 3"/> Name="List Number 4"/> Name="List Number 5"/> Name="Closing"/> Name="Signature"/> UnhideWhenUsed="true" Name="Default Paragraph Font"/> Name="Body Text"/> Name="Body Text Indent"/> Name="List Continue"/> Name="List Continue 2"/> Name="List Continue 3"/> Name="List Continue 4"/> Name="List Continue 5"/> Name="Message Header"/> Name="Salutation"/> Name="Date"/> Name="Body Text First Indent"/> Name="Body Text First Indent 2"/> Name="Note Heading"/> Name="Body Text 2"/> Name="Body Text 3"/> Name="Body Text Indent 2"/> Name="Body Text Indent 3"/> Name="Block Text"/> Name="Hyperlink"/> Name="FollowedHyperlink"/> Name="Document Map"/> Name="Plain Text"/> Name="E-mail Signature"/> Name="HTML Top of Form"/> Name="HTML Bottom of Form"/> Name="Normal (Web)"/> Name="HTML Acronym"/> Name="HTML Address"/> Name="HTML Cite"/> Name="HTML Code"/> Name="HTML Definition"/> Name="HTML Keyboard"/> Name="HTML Preformatted"/> Name="HTML Sample"/> Name="HTML Typewriter"/> Name="HTML Variable"/> Name="Normal Table"/> Name="annotation subject"/> Name="No List"/> Name="Outline List 1"/> Name="Outline List 2"/> Name="Outline List 3"/> Name="Table Simple 1"/> Name="Table Simple 2"/> Name="Table Simple 3"/> Name="Table Classic 1"/> Name="Table Classic 2"/> Name="Table Classic 3"/> Name="Table Classic 4"/> Name="Table Colorful 1"/> Name="Table Colorful 2"/> Name="Table Colorful 3"/> Name="Table Columns 1"/> Name="Table Columns 2"/> Name="Table Columns 3"/> Name="Table Columns 4"/> Name="Table Columns 5"/> Name="Table Grid 1"/> Name="Table Grid 2"/> Name="Table Grid 3"/> Name="Table Grid 4"/> Name="Table Grid 5"/> Name="Table Grid 6"/> Name="Table Grid 7"/> Name="Table Grid 8"/> Name="Table List 1"/> Name="Table List 2"/> Name="Table List 3"/> Name="Table List 4"/> Name="Table List 5"/> Name="Table List 6"/> Name="Table List 7"/> Name="Table List 8"/> Name="Table 3D effects 1"/> Name="Table 3D effects 2"/> Name="Table 3D effects 3"/> Name="Table Contemporary"/> Name="Table Elegant"/> Name="Table Professional"/> Name="Table Subtle 1"/> Name="Table Subtle 2"/> Name="Table Web 1"/> Name="Table Web 2"/> Name="Table Web 3"/> Name="Balloon Text"/> Name="Table Theme"/> Name="List Paragraph"/> Name="Intense Quote"/> Name="Subtle Emphasis"/> Name="Intense Emphasis"/> Name="Subtle Reference"/> Name="Intense Reference"/> UnhideWhenUsed="true" Name="Bibliography"/> UnhideWhenUsed="true" QFormat="true" Name="TOC Heading"/> Name="Grid Table 1 Light Accent 1"/> Name="Grid Table 6 Colorful Accent 1"/> Name="Grid Table 7 Colorful Accent 1"/> Name="Grid Table 1 Light Accent 2"/> Name="Grid Table 6 Colorful Accent 2"/> Name="Grid Table 7 Colorful Accent 2"/> Name="Grid Table 1 Light Accent 3"/> Name="Grid Table 6 Colorful Accent 3"/> Name="Grid Table 7 Colorful Accent 3"/> Name="Grid Table 1 Light Accent 4"/> Name="Grid Table 6 Colorful Accent 4"/> Name="Grid Table 7 Colorful Accent 4"/> Name="Grid Table 1 Light Accent 5"/> Name="Grid Table 6 Colorful Accent 5"/> Name="Grid Table 7 Colorful Accent 5"/> Name="Grid Table 1 Light Accent 6"/> Name="Grid Table 6 Colorful Accent 6"/> Name="Grid Table 7 Colorful Accent 6"/> Name="List Table 1 Light Accent 1"/> Name="List Table 6 Colorful Accent 1"/> Name="List Table 7 Colorful Accent 1"/> Name="List Table 1 Light Accent 2"/> Name="List Table 6 Colorful Accent 2"/> Name="List Table 7 Colorful Accent 2"/> Name="List Table 1 Light Accent 3"/> Name="List Table 6 Colorful Accent 3"/> Name="List Table 7 Colorful Accent 3"/> Name="List Table 1 Light Accent 4"/> Name="List Table 6 Colorful Accent 4"/> Name="List Table 7 Colorful Accent 4"/> Name="List Table 1 Light Accent 5"/> Name="List Table 6 Colorful Accent 5"/> Name="List Table 7 Colorful Accent 5"/> Name="List Table 1 Light Accent 6"/> Name="List Table 6 Colorful Accent 6"/> Name="List Table 7 Colorful Accent 6"/> /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0in; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif;} Temperature increases in metropolitan areas are referred to as urban heat island (UHI) effect. In recent decades, urbanization as well as dramatic increase in population of cities have exacerbated the impact of UHI. The uneven development and growth of the metropolis will lead to an uneven rate of temperature growth in the corresponding area. This work proposes a new machine learning approach based on temperature pattern analysis to determine the rate of deforestation, representing the diversity of geographical regions. The proposed model collect temperature pattern based deforestation data as well as processed for noise removal and normalization. Then this data features has been extracted as well as classified utilizing kernel principal fuzzy reinforcement NN with variational Gaussian encoder markov model. Experimental analysis is carried out in terms of random accuracy, mean precision, AUC, normalized co-efficient, F1 score. Proposed method mean precision was 94%, normalized co-efficient was 97%, AUC was 95%, random accuracy 98%, F1-score 93%. The most important land use categories causing LST increases were determined by analyzing the landscape composition at the class level. 2026 288 297 3952 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.180122 https://americaspg.com/journal/18/article/3952 https://americaspg.com/storage/41753400640.pdf