Statistical Deep Learning for Spatial and Spatiotemporal Data

被引:20
|
作者
Wikle, Christopher K. [1 ]
Zammit-Mangion, Andrew [2 ]
机构
[1] Univ Missouri, Dept Stat, Columbia, MO 65211 USA
[2] Univ Wollongong, Sch Math & Appl Stat, Wollongong, NSW, Australia
基金
澳大利亚研究理事会; 美国国家科学基金会;
关键词
Bayesian hierarchical models; convolutional neural networks; deep Gaussian processes; recurrent neural networks; reinforcement learning; warping; CONVOLUTIONAL NEURAL-NETWORKS; DATA ASSIMILATION; NONSTATIONARY; EMULATOR; MODELS; INFERENCE;
D O I
10.1146/annurev-statistics-033021-112628
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Deep neural network models have become ubiquitous in recent years and have been applied to nearly all areas of science, engineering, and industry. These models are particularly useful for data that have strong dependencies in space (e.g., images) and time (e.g., sequences). Indeed, deep models have also been extensively used by the statistical community to model spatial and spatiotemporal data through, for example, the use of multilevel Bayesian hierarchical models and deep Gaussian processes. In this review, we first present an overview of traditional statistical and machine learning perspectives for modeling spatial and spatiotemporal data, and then focus on a variety of hybrid models that have recently been developed for latent process, data, and parameter specifications. These hybrid models integrate statistical modeling ideas with deep neural network models in order to take advantage of the strengths of each modeling paradigm. We conclude by giving an overview of computational technologies that have proven useful for these hybrid models, and with a brief discussion on future research directions.
引用
收藏
页码:247 / 270
页数:24
相关论文
共 50 条
  • [31] A Spatiotemporal Deep Learning-Based Multisource Data Analytics Framework for Basketball Game
    Lin, Han
    Bao, Muren
    Kang, Chenran
    IEEE ACCESS, 2024, 12 : 73066 - 73078
  • [32] Spatiotemporal Modeling and Prediction in Cellular Networks: A Big Data Enabled Deep Learning Approach
    Wang, Jing
    Tang, Jian
    Xu, Zhiyuan
    Wang, Yanzhi
    Xue, Guoliang
    Zhang, Xing
    Yang, Dejun
    IEEE INFOCOM 2017 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS, 2017,
  • [33] Deep hybrid learning framework for spatiotemporal crash prediction using big traffic data
    Kashifi, Mohammad Tamim
    Al-Turki, Mohammed
    Sharify, Abdul Wakil
    INTERNATIONAL JOURNAL OF TRANSPORTATION SCIENCE AND TECHNOLOGY, 2023, 12 (03) : 793 - 808
  • [34] A REMOTE SENSING SPATIOTEMPORAL FUSION MODEL OF LANDSAT AND MODIS DATA VIA DEEP LEARNING
    Dai, Peiyu
    Zhang, Hongyan
    Zhang, Liangpei
    Shen, Huanfeng
    IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, : 7030 - 7033
  • [35] Abnormal data detection and recovery of sensors network based on spatiotemporal deep learning methodology
    He, Yu
    Ma, Yafei
    Huang, Ke
    Wang, Lei
    Zhang, Jianren
    MEASUREMENT, 2024, 228
  • [36] STNet: Advancing Lithology Identification with a Spatiotemporal Deep Learning Framework for Well Logging Data
    Pang, Qingwei
    Chen, Chenglizhao
    Sun, Youzhuang
    Pang, Shanchen
    NATURAL RESOURCES RESEARCH, 2025, 34 (01) : 327 - 350
  • [37] Exploring the Efficacy of Statistical and Deep Learning Methods for Large Spatial Datasets: A Case Study
    Hazra, Arnab
    Nag, Pratik
    Yadav, Rishikesh
    Sun, Ying
    JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, 2025, 30 (01) : 231 - 254
  • [38] Implicit Learning of Spatiotemporal Contingencies in Spatial Cueing
    Rieth, Cory A.
    Huber, David E.
    JOURNAL OF EXPERIMENTAL PSYCHOLOGY-HUMAN PERCEPTION AND PERFORMANCE, 2013, 39 (04) : 1165 - 1180
  • [39] Spatial and Temporal Data Analysis with Deep Learning for Air Quality Prediction
    Alsaedi, Alaa Saleh
    Liyakathunisa
    12TH INTERNATIONAL CONFERENCE ON THE DEVELOPMENTS IN ESYSTEMS ENGINEERING (DESE 2019), 2019, : 581 - 587
  • [40] Spatial multivariate data imputation using deep learning and lambda distribution
    Hadavand, Mostafa
    Deutsch, Clayton V.
    COMPUTERS & GEOSCIENCES, 2023, 177