Relation patterns extraction from high-dimensional climate data with complicated multi-variables using deep neural networks

Zheng, Jian; Wang, Qingling; Liu, Cong; Wang, Jianfeng; Liu, Hongling; Li, Jiang

doi:10.1007/s10489-022-03737-4

Cited by 1 publication

(1 citation statement)

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“…The supervised Convolutional Neural Network (CNN) algorithm technique has been proven to be highly effective in handling complex climate and environmental data, particularly nonlinear data; an example is the CNN forecast system for the prediction of the El Niño model [17]. This has achieved a training accuracy of up to 94% [18], making it particularly suitable for datasets containing multiple time series of high-dimensional climate variables and multidimensional spatial series with undetermined sequences [19]. The accurate prediction of climate variables is crucial for social and economic activities, and the CNN-LSTM hybrid model outperforms traditional Machine Learning (ML) approaches in predicting rainfall for the next three hours [20].…”

Section: Introductionmentioning

confidence: 99%

Literature Review on Integrating Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) and Deep Neural Networks in Machine Learning for Climate Forecasting

et al. 2023

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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.

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