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DOI	10.1016/j.envres.2024.118171
	An enhanced drought forecasting in coastal arid regions using deep learning approach with evaporation index
	Al Moteri, Moteeb; Alrowais, Fadwa; Mtouaa, Wafa; Aljehane, Nojood O.; Alotaibi, Saud S.; Marzouk, Radwa; Hilal, Anwer Mustafa; Ahmed, Noura Abdelaziz
通讯作者	Hilal, AM
来源期刊	ENVIRONMENTAL RESEARCH
ISSN	0013-9351
EISSN	1096-0953
出版年	2024
卷号	246
英文摘要	Coastal arid regions are similar to deserts, where it receives significantly less rainfall, less than 10 cm. Perhaps the world's worst natural disaster, coastal area droughts, can only be detected using reliable monitoring systems. Creating a reliable drought forecast model and figuring out how well various models can analyze drought factors in coastal arid regions are two of the biggest obstacles in this field. Different time-series methods and machinelearning models have traditionally been utilized in forecasting strategies. Deep learning is promising when describing the complex interplay between coastal drought and its contributing variables. Considering the possibility of enhancing our understanding of drought features, applying deep learning approaches has yet to be tried widely. The current investigation employs a deep learning strategy. Coastal Drought indices are commonly used to comprehend the situation better; hence the Standard Precipitation Evaporation Index (SPEI) was used since it incorporates temperatures and precipitation into its computation. An integrated coastal drought monitoring model was presented and validated using convolutional long short-term memory with self-attention (SACLSTM). The Climatic Research Unit (CRU) dataset, which spans 1901-2018, was mined for the drought index and predictor data. To learn how LSTM forecasting could enhance drought forecasting, we analyzed the findings regarding numerous drought parameters (drought severity, drought category, or geographic variation). The model's ability to predict drought intensity was assessed using the Coefficient of Determination (R2), the Root Mean Square Error (RMSE), and the Mean Absolute Error (MAE). Both the SPEI 1 and SPEI 3 examples had R2 values more than 0.99 for the model. The range of predicted outcomes for each drought group was analyzed using a multi-class Receiver Operating Characteristic based Area under Curves (ROC-AUC) method. The research showed that the AUC for SPEI 1 was 0.99 and for SPEI 3, 0.99. The study's results indicate progress over machine learning models for one month in advance, accounting for various drought conditions. This work's findings may be used to mitigate drought, and additional improvement can be achieved by testing other models.
英文关键词	Coastal arid region Drought forecasting Standard precipitation evaporation index Deep learning Natural disaster Attention
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001161096100001
WOS类目	Environmental Sciences ; Public, Environmental & Occupational Health
WOS研究方向	Environmental Sciences & Ecology ; Public, Environmental & Occupational Health
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403617
推荐引用方式 GB/T 7714	Al Moteri, Moteeb,Alrowais, Fadwa,Mtouaa, Wafa,et al. An enhanced drought forecasting in coastal arid regions using deep learning approach with evaporation index[J],2024,246.
APA	Al Moteri, Moteeb.,Alrowais, Fadwa.,Mtouaa, Wafa.,Aljehane, Nojood O..,Alotaibi, Saud S..,...&Ahmed, Noura Abdelaziz.(2024).An enhanced drought forecasting in coastal arid regions using deep learning approach with evaporation index.ENVIRONMENTAL RESEARCH,246.
MLA	Al Moteri, Moteeb,et al."An enhanced drought forecasting in coastal arid regions using deep learning approach with evaporation index".ENVIRONMENTAL RESEARCH 246(2024).