Machine Learning Model Based Urban Temperature Analysis with Fuzzy Reinforcement Neural Network

 

 

 

L. Pallavi1,*, Gattu Shravani2, J. Sirisha Devi3, Bandaru Satya Lakshmi4, M. Pushpalatha5, S. Gopinath6,
M. Rajesh7

 

1Associate Professor, Department of Computer Science and Engineering, B V Raju Institute of Technology,

 

Narsapur, Telangana, India

 

2Assistant Professor, Dept of CSE (Cys, DS, AI&DS), VNRVJIET, Bachupally, Hyderabad Telangana -500090, India

 

3Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad 500043, Telangana, India

 

4Assistant Professor, Computer Science and Engineering, Aditya University, Surampalem, Andhra Pradesh, India

 

5Assistant Professor, Department of Computer science and Technology, Karpagam College of Engineering, Tamil Nadu, India

 

6Assistant Professor, Gnanamani College of Technology, Namakkal, Tamilnadu-637018, India

 

7Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (DU), Tamilnadu, India

 

Emails: pallavi503@gmail.com; gattushravani513@gmail.com; Siri.cse21@gmail.com; satyalakshmi91.bandaru@gmail.com; pushpalatha18494@gmail.com; sgopicse@gmail.com; rajesmano@gmail.com

 

Text Box: Abstract 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.

 

Received: March 19, 2025 Revised: June 08, 2025 Accepted: July 13, 2025

 

Keywords: Urban heat island; Encoder markov model; Fuzzy reinforcement; Geographical region diversity; Gaussian encoder