A new method for predicting precipitation δ <sup>18</sup> O distribution based on deep learning and spatio-temporal clustering-Reference-Cited by-同舟云学术

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A new method for predicting precipitation δ ¹⁸ O distribution based on deep learning and spatio-temporal clustering

Published:2024-08 Issue:11 Volume:69 Page:1449-1467
ISSN:0262-6667
Container-title:Hydrological Sciences Journal
language:en
Short-container-title:Hydrological Sciences Journal

Author:

Li Yang¹²,Huo Siyuan¹³,Ma Bin²,Pei Bingbing²,Tan Qiankun¹³,Guo Qing⁴,Wang Deng⁵,Yu Longbiao¹³

Affiliation:

1. Hubei Key Laboratory of Petroleum Geochemistry and Environment, Yangtze University, Wuhan, China

2. School of Environmental Studies, China University of Geosciences, Wuhan, China

3. College of Resources and Environment, Yangtze University, Wuhan, China

4. Changjiang Water Resources Commission of the Ministry of Water Resources, Upper Changjiang River Bureau of Hydrological and Water Resources Survey, Chongqing, China

5. The Sixth Geological Team of Hubei Geological Bureau, Xiaogan, China

Funder

Science and Technology Project of Hubei Geological Bureau

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/02626667.2024.2375403

Reference56 articles.

1. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

2. Stable isotope composition of precipitation over southeast Asia

3. The distribution of deuterium and 18O in dry soils

4. Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model

5. Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models

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