An Enhanced Data-Driven Weather Forecasting using Deep Learning Model
Convolutional Neural Network; Deep Learning; Weather Forecasting; Weather Prediction
Predicting present climate and the evolution of the ecosystem is more crucial than ever because of the huge climatic shift that has occurred in nature. Weather forecasts normally are made through compiling numerical data on from the atmospheric state at the moment and also applying scientific knowledge in the atmospheric processes to forecast on how the weather atmosphere would evolve. The most popular study subject nowadays is rainfall forecasting because of complexity in handling the data processing in addition to applications in weather monitoring. Four different state temperature data were collected and applied deep learning methods to predict the temperature level in the forthcoming months. The results brought out with the accuracy from 92.5% to 97.2% for different state temperature data. 2023 IEEE.
Krishnasamy L.; Anand D.V.; Vigneshwaran T.; Pathak L.K.; Maheswaran S.
2023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICCCNT56998.2023.10307319 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179841437&doi=10.1109%2fICCCNT56998.2023.10307319&partnerID=40&md5=6d8c79ae8d95be2f6ff764096fd0a33a
Krishnasamy L., Christ University, School of Engineering and Technology, Department of Cse, Bengaluru, India; Anand D.V., Kongu Engineering College, Department of Information Technology, Erode, India; Vigneshwaran T., Christ University, School of Engineering and Technology, Department of Cse, Bengaluru, India; Pathak L.K., Christ University, School of Engineering and Technology, Department of Cse, Bengaluru, India; Maheswaran S., Kongu Engineering College, Department of Electronics and Communication Engineering, Erode, India