Abstract
The uncertainty of climatic or weather variations makes human adaptation a challenging task. Though lots of techno developments have taken place addressing the need of predictions and forecasting of climatic behavior, the uncertainty of atmospheric and geoprocesses poses a severe challenge to the effectivity of efforts in dealing with disasters. It has become very crucial to understand the future atmospheric uncertainty process and further predict and forecast for analysis of different activities based on geographical locations. Climate parameters are mandatorily required to improve system activities and output analysis of acquired data. For a sustainable adaptation, this is very important on how the uncertainty of variations in climatic parameters is dealt and the decisions based upon appropriate forecasting model are taken. The work has primarily focused on the development of an appropriate model, training the model with massive monthly rainfall and temperature data which can be used to analyze any climatic predictions and provide support to deal with adverse future situations. This model determines the appropriate amount of rainfall and temperature varied during the dry season and helps set a relationship with other input variables like humidity and maximum and minimum temperature. The model has been trained and tested using the obtained data and performed well with better accuracy. The model developed using long short-term memory algorithm of deep learning for predicting rainfall and temperature climatic variables is supported with the study of the main factors affecting monthly climatic change analysis. Monthly precipitation, rainfall intensity, and temperature have been used in the multipurpose prediction model to analyze the monthly and yearly climatic change prediction. The model has also been optimized for precision, accuracy, and computational speed, which are affected by number of iterations, neurons of a hidden layers, epochs, and batch size.
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Acknowledgements
I am sincerely thankful to the Department of Indian Meteorological for providing related data for this study.
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All authors are contributed to the study for implementation and execution. The method preparation, data collection, and execution of model by [Surendra Singh Choudhary] and language grammar and methods outputs checked by [Prof. S. K. Ghosh]. All authors read and approved the final manuscript.
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Highlights
• The prediction of climatic rainfall and temperature parameters.
• The areas of applied environment science using deep learning model.
• The model developed using long short-term memory algorithm of deep learning for predicting rainfall and temperature climatic variables is supported with the study of the main factors affecting monthly climatic change analysis.
• Monthly precipitation, rainfall intensity, and temperature have been used in the multipurpose prediction model to analyze the monthly and yearly climatic change prediction.
• The model has also been optimized for precision, accuracy, and computational speed, which are affected by number of iterations, neurons of a hidden layers, epochs, and batch size.
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Choudhary, S.S., Ghosh, S.K. Analysis of rainfall and temperature using deep learning model. Theor Appl Climatol 153, 755–770 (2023). https://doi.org/10.1007/s00704-023-04493-2
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DOI: https://doi.org/10.1007/s00704-023-04493-2