Literature Review on Integrating Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) and Deep Neural Networks in Machine Learning for Climate Forecasting
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Published:2023-07-03
Issue:13
Volume:11
Page:2975
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ISSN:2227-7390
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Container-title:Mathematics
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language:en
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Short-container-title:Mathematics
Author:
Munandar Devi1ORCID, Ruchjana Budi Nurani1ORCID, Abdullah Atje Setiawan2, Pardede Hilman Ferdinandus3ORCID
Affiliation:
1. Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Bandung 40132, Indonesia 2. Department of Computer Science, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Bandung 40132, Indonesia 3. Research Center for Artificial Intelligence and Cybersecurity, National Research and Innovation Agency (BRIN), Jakarta Pusat 10340, Indonesia
Abstract
The issue of climate change holds immense significance, affecting various aspects of life, including the environment, the interaction between soil conditions and the atmosphere, and agriculture. Over the past few decades, a range of spatio-temporal and Deep Neural Network (DNN) techniques had been proposed within the field of Machine Learning (ML) for climate forecasting, using spatial and temporal data. The forecasting model in this paper is highly complex, particularly due to the presence of nonlinear data in the residual modeling of General Space-Time Autoregressive Integrated Moving Average (GSTARIMA), which represented nonstationary data with time and location dependencies. This model effectively captured trends and seasonal data with time and location dependencies. On the other hand, DNNs proved reliable for modeling nonlinear data that posed challenges for spatio-temporal approaches. This research presented a comprehensive overview of the integrated approach between the GSTARIMA model and DNNs, following the six-stage Data Analytics Lifecycle methodology. The focus was primarily on previous works conducted between 2013 and 2022. The review showed that the GSTARIMA–DNN integration model was a promising tool for forecasting climate in a specific region in the future. Although spatio-temporal and DNN approaches have been widely employed for predicting the climate and its impact on human life due to their computational efficiency and ability to handle complex problems, the proposed method is expected to be universally accepted for integrating these models, which encompass location and time dependencies. Furthermore, it was found that the GSTARIMA–DNN method, incorporating multivariate variables, locations, and multiple hidden layers, was suitable for short-term climate forecasting. Finally, this paper presented several future directions and recommendations for further research.
Funder
Padjadjaran University
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)
Reference91 articles.
1. Box, G.E.P., and Jenkins, G.M. (1976). Time Series Analysis Forecasting and Control, Holden-Day Inc. 2. A Three-Stage Iterative Procedure for Space-Time Modeling;Pfeifer;Technometrics,1980 3. Borovkova, S.A., Lopuhaa, H.P., and Ruchjana, B.N. (2002, January 8–12). Generalized STAR Model with Experimental Weights. Proceedings of the 17th International Workshop on Statistical Modeling, Trieste, Italy. 4. Min, X., Hu, J., and Zhang, Z. (2010, January 19–22). Urban Traffic Network Modeling and Short-Term Traffic Flow Forecasting Based on GSTARIMA Model. Proceedings of the IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, Funchal, Portugal. 5. Akbar, M.S., Ruchjana, B.N., Prastyo, D.D., Muhaimin, A., and Setyowati, E. (2020, January 19). A Generalized Space-Time Autoregressive Moving Average (GSTARMA) Model for Forecasting Air Pollutant in Surabaya. Proceedings of the Journal of Physics: Conference Series, Surabaya, Indonesia.
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