Machine learning based automated identification of thunderstorms from anemometric records using shapelet transform

• URL: http://arxiv.org/abs/2101.04516v1
• Date: Sun, 10 Jan 2021 11:06:41 GMT
• Title: Machine learning based automated identification of thunderstorms from anemometric records using shapelet transform
• Authors: Monica Arul and Ahsan Kareem
• Abstract summary: This paper proposes a new course of research that uses machine learning techniques to autonomously identify and separate thunderstorms. The novel shape based representation when combined with machine learning algorithms yields a practical event detection procedure. A total of 235 non-stationary records associated with thunderstorms were identified using this method.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Detection of thunderstorms is important to the wind hazard community to better understand extreme winds field characteristics and associated wind induced load effects on structures. This paper contributes to this effort by proposing a new course of research that uses machine learning techniques, independent of wind statistics based parameters, to autonomously identify and separate thunderstorms from large databases containing high frequency sampled continuous wind speed measurements. In this context, the use of Shapelet transform is proposed to identify key individual attributes distinctive to extreme wind events based on similarity of shape of their time series. This novel shape based representation when combined with machine learning algorithms yields a practical event detection procedure with minimal domain expertise. In this paper, the shapelet transform along with Random Forest classifier is employed for the identification of thunderstorms from 1 year of data from 14 ultrasonic anemometers that are a part of an extensive in situ wind monitoring network in the Northern Mediterranean ports. A collective total of 235 non-stationary records associated with thunderstorms were identified using this method. The results lead to enhancing the pool of thunderstorm data for more comprehensive understanding of a wide variety of thunderstorms that have not been previously detected using conventional gust factor-based methods.

Related papers

• TRG-Net: An Interpretable and Controllable Rain Generator [61.2760968459789]
  This study proposes a novel deep learning based rain generator, which fully takes the physical generation mechanism underlying rains into consideration. Its significance lies in that the generator not only elaborately design essential elements of the rain to simulate expected rains, but also finely adapt to complicated and diverse practical rainy images. Our unpaired generation experiments demonstrate that the rain generated by the proposed rain generator is not only of higher quality, but also more effective for deraining and downstream tasks.
  arXiv  Detail & Related papers  (2024-03-15T03:27:39Z)
• A Deep Learning Approach to Radar-based QPE [0.11184789007828977]
  We propose a volume-to-point framework for quantitative precipitation estimation (QPE) based on the Quantitative Precipitation Estimation and Segregation Using Multiple Sensor (QPESUMS) Mosaic Radar data set. With a data volume consisting of the time series of gridded radar reflectivities over the Taiwan area, we used machine learning algorithms to establish a statistical model for QPE in weather stations.
  arXiv  Detail & Related papers  (2024-02-15T10:05:18Z)
• WindSeer: Real-time volumetric wind prediction over complex terrain aboard a small UAV [29.345342470012724]
  We train a neural network, WindSeer, using only synthetic data from computational fluid dynamics simulations. WindSeer can generate accurate predictions at different resolutions and domain sizes on previously unseen topography without retraining. We demonstrate that the model successfully predicts historical wind data collected by weather stations and wind measured onboard drones.
  arXiv  Detail & Related papers  (2024-01-18T12:46:26Z)
• A machine-learning approach to thunderstorm forecasting through post-processing of simulation data [0.0]
  Thunderstorms pose a hazard to society and economy, which calls for reliable thunderstorm forecasts. In this work, we introduce a Signature-based Approach of identifying Activity using MAchine Central learning (SALAMA), a feedforward neural network model for identifying thunderstorm occurrence in numerical weather prediction (NWP) data.
  arXiv  Detail & Related papers  (2023-03-15T16:21:15Z)
• An unsupervised learning approach for predicting wind farm power and downstream wakes using weather patterns [0.0]
  We develop a novel wind energy workflow that combines weather patterns derived from unsupervised clustering techniques with numerical weather prediction models. We show that our long-term predictions agree with those from a year of WRF simulations but require less than 2% of the computational time. Our approach facilitates multi-year predictions of power output and downstream farm wakes, by providing a fast, accurate and flexible methodology.
  arXiv  Detail & Related papers  (2023-02-12T10:05:25Z)
• Learning-based estimation of in-situ wind speed from underwater acoustics [58.293528982012255]
  We introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics. Our approach bridges data assimilation and learning-based frameworks to benefit both from prior physical knowledge and computational efficiency.
  arXiv  Detail & Related papers  (2022-08-18T15:27:40Z)
• Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds [96.74836678572582]
  We present a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly achieves precise flight control with substantially smaller tracking error than state-of-the-art nonlinear and adaptive controllers.
  arXiv  Detail & Related papers  (2022-05-13T21:55:28Z)
• Physics Informed Shallow Machine Learning for Wind Speed Prediction [66.05661813632568]
  We analyze a massive dataset of wind measured from anemometers located at 10 m height in 32 locations in Italy. We train supervised learning algorithms using the past history of wind to predict its value at a future time. We find that the optimal design as well as its performance vary with the location.
  arXiv  Detail & Related papers  (2022-04-01T14:55:10Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• WaveTransform: Crafting Adversarial Examples via Input Decomposition [69.01794414018603]
  We introduce WaveTransform', that creates adversarial noise corresponding to low-frequency and high-frequency subbands, separately (or in combination) Experiments show that the proposed attack is effective against the defense algorithm and is also transferable across CNNs.
  arXiv  Detail & Related papers  (2020-10-29T17:16:59Z)
• Applications of shapelet transform to time series classification of earthquake, wind and wave data [0.0]
  "Shapelet transform" is based on local similarity in the shape of the time series subsequences. "White-box" machine learning model is proposed with understandable features and a transparent algorithm. Model automates event detection without the intervention of domain practitioners.
  arXiv  Detail & Related papers  (2020-04-22T10:17:24Z)

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.