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

Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3952 2019 2019 Machine Learning Model Based Urban Temperature Analysis with Fuzzy Reinforcement Neural Network Associate Professor, Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Telangana, India L. L. Assistant Professor, Dept of CSE (Cys, DS, AI&DS), VNRVJIET, Bachupally, Hyderabad Telangana -500090, India Gattu Shravani Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Bowrampet, Hyderabad 500043, Telangana, India J. Sirisha Devi Assistant Professor, Computer Science and Engineering, Aditya University, Surampalem, Andhra Pradesh, India Bandaru Satya Lakshmi Assistant Professor, Department of Computer science and Technology, Karpagam College of Engineering, Tamil Nadu, India M. Pushpalatha Assistant Professor, Gnanamani College of Technology, Namakkal, Tamilnadu-637018, India S. Gopinath Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (DU), Tamilnadu, India M. Rajesh 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 2026 288 297 10.54216/JISIoT.180122 https://www.americaspg.com/articleinfo/18/show/3952