Article: A hybrid CNN-LSTM approach to enhancing temperature forecasting for environmental threats and risk management Journal: International Journal of Business Continuity and Risk Management (IJBCRM) 2024 Vol.14 No.4 pp.371 - 391 Abstract: Temperature is the most important element of weather, which is applicable in varied study areas such as environmental, ecological, industry, agriculture sectors, etc. This research platforms the practicality of utilising a combination of convolutional neural networks and learning paradigms to forecast weather conditions in the eastern region of India, New Delhi. The authors propose long short-term memory (LSTM) networks, convolutional neural networks (CNNs) and examine how they compare to hybrid CNN-LSTM model for temperature forecasting. Our aim is to address these issues through a representation, which jointly predicts temperature over time. Experiments on actual meteorological data used in our evaluation of the models highlight the approach's potential. We also used accuracy, mean square error (MSE), and root mean square error (RMSE) to estimate these models' outcome. Our findings demonstrate that the proposed CNN-LSTM model delivers the best outcomes because of its accuracy and small error rates. Inderscience Publishers - linking academia, business and industry through research

Title: A hybrid CNN-LSTM approach to enhancing temperature forecasting for environmental threats and risk management

Authors: Sasmita Kumari Nayak; Satyajit Pattnaik; Mohammed Siddique; Mamata Garanayak; Bijay Kumar Paikaray

Addresses: Department of Computer Science and Engineering, Centurion University of Technology and Management, Odisha, India ' Faculty of Engineering and Technology, Sri Sri University, Cuttack, Odisha, India ' Department of Mathematics, Centurion University of Technology and Management, Odisha, India ' Kalinga Institute of Social Sciences (Deemed to be University), Bhubaneswar, Odisha, India ' Centre for Data Science, Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Odisha, India

Abstract: Temperature is the most important element of weather, which is applicable in varied study areas such as environmental, ecological, industry, agriculture sectors, etc. This research platforms the practicality of utilising a combination of convolutional neural networks and learning paradigms to forecast weather conditions in the eastern region of India, New Delhi. The authors propose long short-term memory (LSTM) networks, convolutional neural networks (CNNs) and examine how they compare to hybrid CNN-LSTM model for temperature forecasting. Our aim is to address these issues through a representation, which jointly predicts temperature over time. Experiments on actual meteorological data used in our evaluation of the models highlight the approach's potential. We also used accuracy, mean square error (MSE), and root mean square error (RMSE) to estimate these models' outcome. Our findings demonstrate that the proposed CNN-LSTM model delivers the best outcomes because of its accuracy and small error rates.

Keywords: convolutional neural network; CNN; deep learning; long short-term memory; LSTM; temperature prediction.

DOI: 10.1504/IJBCRM.2024.142649

International Journal of Business Continuity and Risk Management, 2024 Vol.14 No.4, pp.371 - 391

Received: 31 Dec 2023
Accepted: 29 Apr 2024
Published online: 14 Nov 2024 *

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Title: A hybrid CNN-LSTM approach to enhancing temperature forecasting for environmental threats and risk management

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