干旱区生态环境知识资源中心(Arid): Spatio-temporal modeling of climate change impacts on drought forecast using Generative Adversarial Network: A case study in Africa

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DOI	10.1016/j.eswa.2023.122211
	Spatio-temporal modeling of climate change impacts on drought forecast using Generative Adversarial Network: A case study in Africa
	Ferchichi, Ahlem; Chihaoui, Mejda; Ferchichi, Aya
通讯作者	Ferchichi, A
来源期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
EISSN	1873-6793
出版年	2024
卷号	238
英文摘要	Drought is an extreme weather event, affecting the ecological conditions of vegetation and agricultural productivity, poses challenges for millions of people in Africa, and its long-term prediction is definitely important. Accurate drought forecasting is a challenging subject due to its dependence on different climatic variables, and its spatio-temporal, nonstationary and non-linear characteristics. In particular, Deep Learning technologies have achieved excellent results in long-term time series forecasting. Thus, this study proposes a Generative Adversarial Networks (GAN) model which combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network for drought forecasting in Africa. This approach focuses on how the future spatio-temporal variations of drought will vary under climate change effects using multivariate remote sensing data over Africa from 1999-2022. We considered hydrological, meteorological and vegetation spectral factors for GAN as model input variables. The study assessed agricultural drought using the soil moisture index (SMI) as a response parameter. Experimental results confirmed the reliability of the proposed model for forecasting agricultural drought. Compared to existing deep learning models, the proposed GAN based CNN-LSTM model achieved the lowest RMSE, MAPE, and MAE values of 1.008, 0.009, and 0.739, respectively. The findings demonstrate that the proposed model can be used as a reliable forecasting method that helps to estimate drought in arid and semi-arid regions.
英文关键词	GAN Spatio-temporal modeling Climate change impacts Drought forecasting Multivariate time series Remote sensing data Africa
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001105381700001
WOS关键词	AREAS
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403712
推荐引用方式 GB/T 7714	Ferchichi, Ahlem,Chihaoui, Mejda,Ferchichi, Aya. Spatio-temporal modeling of climate change impacts on drought forecast using Generative Adversarial Network: A case study in Africa[J],2024,238.
APA	Ferchichi, Ahlem,Chihaoui, Mejda,&Ferchichi, Aya.(2024).Spatio-temporal modeling of climate change impacts on drought forecast using Generative Adversarial Network: A case study in Africa.EXPERT SYSTEMS WITH APPLICATIONS,238.
MLA	Ferchichi, Ahlem,et al."Spatio-temporal modeling of climate change impacts on drought forecast using Generative Adversarial Network: A case study in Africa".EXPERT SYSTEMS WITH APPLICATIONS 238(2024).