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The Discharge Forecasting of Multiple Monitoring Station for Humber River by Hybrid LSTM Models

Yue Zhang, Zhaohui Gu, Jesse Van Griensven Thé, Simon X. Yang, Bahram Gharabaghi
2022 Water  
) models for flood forecasting.  ...  We evaluated the performance of the Long Short-Term Memory (LSTM), the Convolutional Neural Networks LSTM (CNN-LSTM), the Convolutional LSTM (ConvLSTM), and the Spatio-Temporal Attention LSTM (STA-LSTM  ...  For the spatio-temporal attention LSTM model (STA-LSTM), the main LSTM network is used for feature extraction, temporal correlation, and final classification.  ... 
doi:10.3390/w14111794 fatcat:qosx2b3pdjakbo57iry4e7mene

Flood Discharge Prediction Based on Remote-Sensed Spatiotemporal Features Fusion and Graph Attention

Chen Chen, Dingbin Luan, Song Zhao, Zhan Liao, Yang Zhou, Jiange Jiang, Qingqi Pei
2021 Remote Sensing  
to extract the spatial characteristics of rainfall, and employs long-term and short-term memory (LSTM) network to fuse the spatial and temporal characteristics for flood prediction.  ...  According to the hydrological prediction specifications, the model can be evaluated as a Class B flood forecasting model.  ...  In the field of flood forecasting, Yukai Ding [27] and others propose an interpretable Spatio-Temporal Attention Long Short Term Memory model (STA-LSTM) based on LSTM and attention mechanism.  ... 
doi:10.3390/rs13245023 fatcat:7qz5aeylhzgylfwnbcclkggwxq

Learning Space-Time Crop Yield Patterns with Zigzag Persistence-Based LSTM: Toward More Reliable Digital Agriculture Insurance

Tian Jiang, Meichen Huang, Ignacio Segovia-Dominguez, Nathaniel Newlands, Yulia R. Gel
Our approach is based on extracting time-conditioned topological features which characterize complex spatio-temporal dependencies between crop production regions and integrating such topological signatures  ...  We discuss utility and limitations of the resulting zigzag persistence-based LSTM (ZZTop-LSTM) as a new tool for developing more informed crop insurance rate-making and accurate tracking of changing risk  ...  for spatio-temporal evolution of crop yields.  ... 
doi:10.1609/aaai.v36i11.21524 fatcat:y34pz2m6vvhvdeilysrgi7x7nu

Hybrid CNN-LSTM models for river flow prediction

Xia Li, Wei Xu, Minglei Ren, Yanan Jiang, Guangtao Fu
2022 Water Science and Technology : Water Supply  
This study shows that the hybrid network has great promise in learning nonlinear and high spatio-temporal variability in river flow forecasting.  ...  Here we present a hybrid network of convolutional neural network (CNN) and long short-term memory (LSTM) network for river flow prediction.  ...  We would like to thank the Hun River Cascade Hydropower Reservoirs Development Ltd for collecting the data ( resource/9e851535b3fd42a49d00c41bf277652c), and the observed data  ... 
doi:10.2166/ws.2022.170 fatcat:ynk2x6jrxfhvvfgnw4kqzy6wpi

Subway Passenger Flow Forecasting with Multi-station and External Factors

Yan Danfeng, Wang Jing
2019 IEEE Access  
In addition, the hierarchical attention mechanism is used to model the hierarchical relationship between subway lines and stations.  ...  Therefore, accurate passenger flow forecasting is of great significance for passengers and municipal construction and contributes to smart city services.  ...  [9] proposes a hybrid temporal-spatio forecasting approach to obtain the passenger flow status in HRTH.  ... 
doi:10.1109/access.2019.2914239 fatcat:dikce3yiqrcozhnyaasuqhczda

Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting [article]

Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Conguri Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, Qi Zhang
2021 arXiv   pre-print
It combines Graph Fourier Transform (GFT) which models inter-series correlations and Discrete Fourier Transform (DFT) which models temporal dependencies in an end-to-end framework.  ...  In this paper, we propose Spectral Temporal Graph Neural Network (StemGNN) to further improve the accuracy of multivariate time-series forecasting.  ...  For example, FC-LSTM [36] forecasts univariate time-series with LSTM and fully-connected layers.  ... 
arXiv:2103.07719v1 fatcat:ysqgbsalfjehpojx22d2ae77gm

A Spatial-temporal Graph Deep Learning Model for Urban Flood Nowcasting Leveraging Heterogeneous Community Features [article]

Hamed Farahmand, Yuanchang Xu, Ali Mostafavi
2021 arXiv   pre-print
human-sensed data for flood nowcasting,  ...  We present a new computational modeling framework including an attention-based spatial-temporal graph convolution network (ASTGCN) model and different streams of data that are collected in real-time, preprocessed  ...  Acknowledgements The authors would like to acknowledge funding support from the National Science Foundation project CRISP 2.0 Type 2 #1832662: Anatomy of Coupled Human-Infrastructure Systems Resilience to Urban Flooding  ... 
arXiv:2111.08450v2 fatcat:6ockrwwz7fgfnhnmaynmcgtwwy

Data-driven modelling of nonlinear spatio-temporal fluid flows using a deep convolutional generative adversarial network [article]

M. Cheng, F. Fang, C.C. Pain, I.M. Navon
2020 arXiv   pre-print
Deep learning techniques for improving fluid flow modelling have gained significant attention in recent years.  ...  In this work, an Artificial Intelligence (AI) fluid model based on a general deep convolutional generative adversarial network (DCGAN) has been developed for predicting spatio-temporal flow distributions  ...  Deep convolutional GAN for spatio-temporal data-driven modelling Details about model development for spatio-temporal fluid flow modelling, please see full paper.  ... 
arXiv:2004.00707v1 fatcat:pojqa7jvw5g3vkefku6khx5xkm

Short-term Flood Forecasting via ST-DTW

Yuelong Zhu, Jun Feng, Le Yan, Tao Guo, Xiaodong Li, Tingting Hang
2020 IEEE Access  
This suggests that the algorithm is suitable for hydrological studies and improves the accuracy of real-time flood forecasting for longer forecast periods.  ...  We need a new way to solve the forecasting problem for small-and medium-sized rivers.  ...  However, although the LSTM is the best performing model, it has a flood predicting process line that is unsmooth for real-time forecasting.  ... 
doi:10.1109/access.2020.2971264 fatcat:crmi4vrzszgutdsvi3ap2cnemi

An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [article]

Ehsan Adeli, Luning Sun, Jianxun Wang, Alexandros A. Taflanidis
2022 arXiv   pre-print
Therefore, the temporal and spatial correlations of data are captured by the combination of the mentioned models, the ConvLSTM model.  ...  This model can serve as a fast and affordable emulator for the very expensive CFD solvers.  ...  Acknowledgement Authors would like to thank the Army Corp of Engineers, Coastal Hydraulics Laboratory of the Engineering Research and Development Center for providing access to the storm surge data, though  ... 
arXiv:2204.09501v1 fatcat:e4cndux3brdlve5kvmyfbl4dhe

Bagging Machine Learning Algorithms: A Generic Computing Framework Based on Machine-Learning Methods for Regional Rainfall Forecasting in Upstate New York

Ning Yu, Timothy Haskins
2021 Informatics  
while DWNN- and KNN-based methods outstrip other models in regression, particularly those prevailing deep-learning-based methods, for handling uncertain and complex climatic data for precipitation forecasting  ...  Machine learning algorithms especially deep learning methods have emerged as a part of prediction tools for regional rainfall forecasting.  ...  Also, the authors thank Emma Doyle and Reid Hoffmeier for their participation in the early prototype project.  ... 
doi:10.3390/informatics8030047 fatcat:bpo4eeg5sjdq7dc52fo6jyizwq

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

Carlos Gonzales-Inca, Mikel Calle, Danny Croghan, Ali Torabi Haghighi, Hannu Marttila, Jari Silander, Petteri Alho
2022 Water  
The most recent research on hydrological GeoAI has focused on integrating the physical-based models' principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting  ...  A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization.  ...  the coupled ANN with the K-nearest neighbor hybrid machine learning (HML) for flood forecast.  ... 
doi:10.3390/w14142211 fatcat:tjiod5qz45f67kbmkm3f6kuv7i

Deep Neural Mobile Networking [article]

Chaoyun Zhang
2020 arXiv   pre-print
This makes monitoring and managing the multitude of network elements intractable with existing tools and impractical for traditional machine learning algorithms that rely on hand-crafted feature engineering  ...  Convolutional LSTM (ConvLSTM) is a dedicated model for spatio-temporal data forecasting [211] .  ...  We introduce CloudLSTM, a dedicated neural model for spatio-temporal forecasting tailored to point-cloud data streams.  ... 
arXiv:2011.05267v1 fatcat:yz2zp5hplzfy7h5kptmho7mbhe

Deep multi-stations weather forecasting: explainable recurrent convolutional neural networks [article]

Ismail Alaoui Abdellaoui, Siamak Mehrkanoon
2021 arXiv   pre-print
In particular, we show that adding a self-attention block within the models increases the overall forecasting performance.  ...  Deep learning applied to weather forecasting has started gaining popularity because of the progress achieved by data-driven models.  ...  The author in [19] introduced the convolutional neural networks to learn the underlying spatio-temporal patterns of weather data.  ... 
arXiv:2009.11239v6 fatcat:xgvcctw2nveqpkscojhjz6nii4

Spatio-Temporal Graph Convolutional Networks for Road Network Inundation Status Prediction during Urban Flooding [article]

Faxi Yuan, Yuanchang Xu, Qingchun Li, Ali Mostafavi
2021 arXiv   pre-print
Model 1 consists of two spatio-temporal blocks considering the adjacency and distance between road segments, while Model 2 contains an additional elevation block to account for elevation difference between  ...  Using fine-grained traffic speed data related to road sections, this study designed and implemented three spatio-temporal graph convolutional network (STGCN) models to predict road network status during  ...  The authors would also like to acknowledge INRIX for providing the traffic data.  ... 
arXiv:2104.02276v1 fatcat:if6laxc2jvevdekgwxrsa22tgq
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