ItemConference Paper

Using Recurrent Neural Networks to Forecast Climate Change: A Time Series Analysis of Global Temperature Variability

Title
Using Recurrent Neural Networks to Forecast Climate Change: A Time Series Analysis of Global Temperature Variability
Creator
Rathore, Nitasha; Anubha; Singh, Kuljeet; Kaur, Gaganpreet; Sharma, Chetna; Sinha, Anurag
Description
Predicting the upcoming weather instances is very crucial. It depends on different climatic parameters like humidity, pressure, temperature, etc. In this paper, the historical data of the weather in the India area is used for future weather instances of the India for farmers' convenience in terms of the agricultural instance which depends on the weather and, functioning according to that which will restore the energy. For weather forecasting we have use the machine learning algorithm and probabilistic predictions of the predictive analytics based on soft computing and. NGBoost algorithm and. Linear models of the machine learning for predictive. Comparative weather incidence spaced on the historical data. We have also used, sliding window algorithm of the statistics for predicting the ideology of the concept of different contrasted windows and year. We've also used utility thirds and machine learning algorithm, classified for predicting the weather based on different features The overall implementation in this paper, shows the accuracy which we have gathered from the data set. An implementation of algorithm which ranges between 80% to 90% and the entire algorithm have been compared based on the feature instances. Work can be concluded on the measurement of the algorithm, which we have got after the implementation of Models. Which rely upon the different data features and thus it can be beneficial for preserving the energy and materials in the India agriculture area and forecasting the weather as per day Agricultural conditions. 2025 IEEE.
Source
1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025;pp.425-430
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Climate change; Deep learning; Recurrent Neural Network
Coverage
Rathore N., Bharatiya Vidyapeeth College of Engineering, Department of Computer Science and Engineering, New Delhi, India; Anubha, KIET Group of Institutions, India; Singh K., School of Sciences, Christ University, Department of Computer Science, Delhi NCR, India; Kaur G., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India; Sharma C., Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India; Sinha A., IGNOU, Department of Computer science, New Delhi, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833151857-8;
Format
online
Language
English
Type
Conference paper

Citation

Rathore, Nitasha; Anubha; Singh, Kuljeet; Kaur, Gaganpreet; Sharma, Chetna; Sinha, Anurag, “Using Recurrent Neural Networks to Forecast Climate Change: A Time Series Analysis of Global Temperature Variability,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25777.

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