Dual-Branched Spatio-temporal Fusion Network for Multi-horizon Tropical Cyclone Track Forecast

• URL: http://arxiv.org/abs/2202.13336v1
• Date: Sun, 27 Feb 2022 10:45:18 GMT
• Title: Dual-Branched Spatio-temporal Fusion Network for Multi-horizon Tropical Cyclone Track Forecast
• Authors: Zili Liu and Kun Hao and Xiaoyi Geng and Zhenwei Shi
• Abstract summary: We propose a novel multi-horizon tropical cyclone track forecasting model which fuses the multi-temporal features efficiently. Through the encoder-decoder-based architecture and efficient feature fusion, DBF-Net can fully mine the information of the two types of data, and achieve good TC tracks prediction results.
• Score: 13.123672819646336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tropical cyclone (TC) is an extreme tropical weather system and its trajectory can be described by a variety of spatio-temporal data. Effective mining of these data is the key to accurate TCs track forecasting. However, existing methods face the problem that the model complexity is too high or it is difficult to efficiently extract features from multi-modal data. In this paper, we propose the Dual-Branched spatio-temporal Fusion Network (DBF-Net) -- a novel multi-horizon tropical cyclone track forecasting model which fuses the multi-modal features efficiently. DBF-Net contains a TC features branch that extracts temporal features from 1D inherent features of TCs and a pressure field branch that extracts spatio-temporal features from reanalysis 2D pressure field. Through the encoder-decoder-based architecture and efficient feature fusion, DBF-Net can fully mine the information of the two types of data, and achieve good TCs track prediction results. Extensive experiments on historical TCs track data in the Northwest Pacific show that our DBF-Net achieves significant improvement compared with existing statistical and deep learning TCs track forecast methods.

Related papers

• Time Distributed Deep Learning models for Purely Exogenous Forecasting. Application to Water Table Depth Prediction using Weather Image Time Series [1.4436965372953483]
  We propose two different Deep Learning models to predict the water table depth in the Grana-Maira (Piemonte, IT) To deal with the image time series, both models are made of a first Time Distributed Convolutional Neural Network (TDC) which encodes the image available at each time step into a vectorial representation. The two models have focused on different learnable information: TDC-LSTM has focused more on lowering the bias, while the TDC-UnPWaveNet has focused more on the temporal dynamics maximising correlation and KGE.
  arXiv  Detail & Related papers  (2024-09-20T07:25:54Z)
• TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals [58.865901821451295]
  We present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain, we introduce a "TC" stream that uses Continuous Wavelet Transform (CWT) to represent information in a 2D tensor form.
  arXiv  Detail & Related papers  (2024-04-15T06:01:48Z)
• Prediction of Vessel Arrival Time to Pilotage Area Using Multi-Data Fusion and Deep Learning [9.458664533786994]
  This paper investigates the prediction of vessels' arrival time to the pilotage area using multi-data fusion and deep learning approaches. Tests on two real-world data sets from Singapore have been conducted and the following promising results have been obtained.
  arXiv  Detail & Related papers  (2024-03-15T02:25:04Z)
• Deep Learning for Day Forecasts from Sparse Observations [60.041805328514876]
  Deep neural networks offer an alternative paradigm for modeling weather conditions. MetNet-3 learns from both dense and sparse data sensors and makes predictions up to 24 hours ahead for precipitation, wind, temperature and dew point. MetNet-3 has a high temporal and spatial resolution, respectively, up to 2 minutes and 1 km as well as a low operational latency.
  arXiv  Detail & Related papers  (2023-06-06T07:07:54Z)
• Long-term Wind Power Forecasting with Hierarchical Spatial-Temporal Transformer [112.12271800369741]
  Wind power is attracting increasing attention around the world due to its renewable, pollution-free, and other advantages. Accurate wind power forecasting (WPF) can effectively reduce power fluctuations in power system operations. Existing methods are mainly designed for short-term predictions and lack effective spatial-temporal feature augmentation.
  arXiv  Detail & Related papers  (2023-05-30T04:03:15Z)
• CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting [50.23240107430597]
  We design a special Transformer, i.e., Channel Aligned Robust Blend Transformer (CARD for short), that addresses key shortcomings of CI type Transformer in time series forecasting. First, CARD introduces a channel-aligned attention structure that allows it to capture both temporal correlations among signals. Second, in order to efficiently utilize the multi-scale knowledge, we design a token blend module to generate tokens with different resolutions. Third, we introduce a robust loss function for time series forecasting to alleviate the potential overfitting issue.
  arXiv  Detail & Related papers  (2023-05-20T05:16:31Z)
• PCT-CycleGAN: Paired Complementary Temporal Cycle-Consistent Adversarial Networks for Radar-Based Precipitation Nowcasting [3.4956929165638764]
  We propose a paired complementary temporal cycle-consistent adversarial networks (PCT-CycleGAN) for radar-based precipitation nowcasting. PCT-CycleGAN shows strong performance in image-to-image translation. It provides a reliable prediction of up to 2 hours with iterative forecasting.
  arXiv  Detail & Related papers  (2022-11-28T04:08:55Z)
• Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information [52.635997570873194]
  This work proposes a novel deep cellular recurrent neural network (DCRNN) architecture to process complex multi-dimensional time series data with spatial information. The proposed architecture achieves state-of-the-art performance while utilizing substantially less trainable parameters when compared to comparable methods in the literature.
  arXiv  Detail & Related papers  (2021-01-12T20:08:18Z)
• Multi-view Depth Estimation using Epipolar Spatio-Temporal Networks [87.50632573601283]
  We present a novel method for multi-view depth estimation from a single video. Our method achieves temporally coherent depth estimation results by using a novel Epipolar Spatio-Temporal (EST) transformer. To reduce the computational cost, inspired by recent Mixture-of-Experts models, we design a compact hybrid network.
  arXiv  Detail & Related papers  (2020-11-26T04:04:21Z)
• Hurricane Forecasting: A Novel Multimodal Machine Learning Framework [2.829284162137884]
  Our framework, called Hurricast, efficiently combines spatial-temporal data with statistical data. The inclusion of Hurricast into an operational forecast consensus model could improve over the National Hurricane Center's official forecast.
  arXiv  Detail & Related papers  (2020-11-11T23:55:33Z)
• MSDPN: Monocular Depth Prediction with Partial Laser Observation using Multi-stage Neural Networks [1.1602089225841632]
  We propose a deep-learning-based multi-stage network architecture called Multi-Stage Depth Prediction Network (MSDPN) MSDPN is proposed to predict a dense depth map using a 2D LiDAR and a monocular camera. As verified experimentally, our network yields promising performance against state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-04T08:27:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.