Affiliation:
1. Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Bangalore India
2. Department of Electronics and Telecommunication International Institute of Information Technology Bhubaneswar Odisha India
3. Department of Information Technology Indira Gandhi Delhi Technological University New Delhi India
4. School of Computer Science and Engineering (SCOPE) VIT‐AP University Amaravati Andhra Pradesh India
5. Department of Computer Science and Engineering Ajay Binay Institute of Technology Cuttack Odisha India
Abstract
AbstractThis study is based on temperature prediction in the capital of India (New Delhi). We have adopted different ML models such as (MPR and DNN) which are designed and implemented for temperature prediction. The MPR models are varied on the degree of the polynomial, whereas the DNN models differ in the number of input parameters. DNNM‐1 takes date, time, and temperature as input, and DNNM‐2 receives date, time, temperature, pressure, humidity, and dew point as input parameters, whereas DNNM‐3, is a complex model that takes date, time, temperature, pressure, humidity, dew point, and 32 weather conditions as input. To evaluate the accuracy of the predictions, a comparison of the predicted temperature and the actual recorded temperature is done, and the performance and accuracy of the models are examined. The MPR models work well in case of fewer input features, but as the number of input features grows, the DNN model outperforms the MPR models. The DNN model (DNNM‐3) outperformed the other models with better accuracy as compared to past evidence.
Subject
Management Science and Operations Research,Statistics, Probability and Uncertainty,Strategy and Management,Computer Science Applications,Modeling and Simulation,Economics and Econometrics
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