Sub-seasonal soil moisture anomaly forecasting using combinations of deep learning, based on the reanalysis soil moisture records

被引：0
|
作者：
Wang, Xiaoyi [1 ,2 ]
Corzo, Gerald [3 ]
Lü, Haishen [2 ,4 ]
Zhou, Shiliang [1 ]
Mao, Kangmin [5 ]
Zhu, Yonghua [2 ,4 ]
Duarte, Santiago [3 ,5 ]
Liu, Mingwen [2 ,4 ]
Su, Jianbin [6 ]
机构：
[1] Southwest Research Institute for Hydraulic and Water Transport Engineering, Chongqing Jiaotong University, Chongqing,400074, China
[2] State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, National Cooperative Innovation Center for Water Safety and Hydro-science, College of Hydrology and Water Resources, Hohai University, Nanjing,210098, China
[3] Hydroinformatics Chair Group, IHE Delft Institute for Water Education, Delft,2611AX, Netherlands
[4] Joint International Research Laboratory of Global Change and Water Cycle, Hohai University, Nanjing,210098, China
[5] Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, Netherlands
[6] National Tibetan Plateau Data Center, Key Laboratory of Tibetan Environmental Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
来源：
Agricultural Water Management | 2024年 / 295卷
基金：
中国国家自然科学基金;
关键词：
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [21] Research on soil moisture prediction model based on deep learning
    Cai, Yu
    Zheng, Wengang
    Zhang, Xin
    Zhangzhong, Lili
    Xue, Xuzhang
    PLOS ONE, 2019, 14 (04):
  • [22] Modulation of summer monsoon sub-seasonal surface air temperature over India by soil moisture-temperature coupling
    Ramarao, M. V. S.
    Sanjay, J.
    Krishnan, R.
    MAUSAM, 2016, 67 (01): : 53 - 66
  • [23] A Virtual Soil Moisture Sensor for Smart Farming Using Deep Learning
    Patrizi, Gabriele
    Bartolini, Alessandro
    Ciani, Lorenzo
    Gallo, Vincenzo
    Sommella, Paolo
    Carratu, Marco
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 71
  • [24] Comparison of soil moisture at different depths for drought monitoring based on improved soil moisture anomaly percentage index
    Xu, Zheng-guang
    Wu, Zhi-yong
    He, Hai
    Guo, Xiao
    Zhang, Yu-liang
    WATER SCIENCE AND ENGINEERING, 2021, 14 (03) : 171 - 183
  • [25] A comprehensive study of deep learning for soil moisture prediction
    Wang, Yanling
    Shi, Liangsheng
    Hu, Yaan
    Hu, Xiaolong
    Song, Wenxiang
    Wang, Lijun
    HYDROLOGY AND EARTH SYSTEM SCIENCES, 2024, 28 (04) : 917 - 943
  • [26] Assessment of a Spatiotemporal Deep Learning Approach for Soil Moisture Prediction and Filling the Gaps in Between Soil Moisture Observations
    ElSaadani, Mohamed
    Habib, Emad
    Abdelhameed, Ahmed M.
    Bayoumi, Magdy
    FRONTIERS IN ARTIFICIAL INTELLIGENCE, 2021, 4
  • [27] Multistep Forecasting of Soil Moisture Using Spatiotemporal Deep Encoder-Decoder Networks
    Li, Lu
    Dai, Yongjiu
    Shangguan, Wei
    Wei, Nan
    Wei, Zhongwang
    Gupta, Surya
    JOURNAL OF HYDROMETEOROLOGY, 2022, 23 (03) : 337 - 350
  • [28] SOIL MOISTURE PREDICTION USING MACHINE LEARNING
    Prakash, Shikha
    Sharma, Animesh
    Sahu, Sitanshu Shekhar
    PROCEEDINGS OF THE 2018 SECOND INTERNATIONAL CONFERENCE ON INVENTIVE COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES (ICICCT), 2018,
  • [29] Assessment of Agricultural Drought Based on Reanalysis Soil Moisture in Southern China
    Wei, Shangguan
    Zhang, Ruqing
    Li, Lu
    Zhang, Shulei
    Zhang, Ye
    Huang, Feini
    Li, Jianduo
    Liu, Wei
    LAND, 2022, 11 (04)
  • [30] Sub-Seasonal Forecasting With a Large Ensemble of Deep-Learning Weather Prediction Models
    Weyn, Jonathan A.
    Durran, Dale R.
    Caruana, Rich
    Cresswell-Clay, Nathaniel
    JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS, 2021, 13 (07)
12345