Imputation of missing sub-hourly precipitation data in a large sensor network: a machine learning approach

• URL: http://arxiv.org/abs/2004.11123v2
• Date: Sat, 2 May 2020 09:11:03 GMT
• Title: Imputation of missing sub-hourly precipitation data in a large sensor network: a machine learning approach
• Authors: Benedict Delahaye Chivers, John Wallbank, Steven J. Cole, Ondrej Sebek, Simon Stanley, Matthew Fry and Georgios Leontidis
• Abstract summary: We present a two-step analysis utilising current machine learning techniques for imputing precipitation data sampled at 30-minute intervals. Investigating 37 weather stations in the UK, this machine learning process produces more accurate predictions for recovering precipitation data. Capturing complex non-linear relationships from weakly correlated datasets is critical for data recovery at sub-hourly resolutions.
• Score: 4.648824029505978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precipitation data collected at sub-hourly resolution represents specific challenges for missing data recovery by being largely stochastic in nature and highly unbalanced in the duration of rain vs non-rain. Here we present a two-step analysis utilising current machine learning techniques for imputing precipitation data sampled at 30-minute intervals by devolving the task into (a) the classification of rain or non-rain samples, and (b) regressing the absolute values of predicted rain samples. Investigating 37 weather stations in the UK, this machine learning process produces more accurate predictions for recovering precipitation data than an established surface fitting technique utilising neighbouring rain gauges. Increasing available features for the training of machine learning algorithms increases performance with the integration of weather data at the target site with externally sourced rain gauges providing the highest performance. This method informs machine learning models by utilising information in concurrently collected environmental data to make accurate predictions of missing rain data. Capturing complex non-linear relationships from weakly correlated variables is critical for data recovery at sub-hourly resolutions. Such pipelines for data recovery can be developed and deployed for highly automated and near instantaneous imputation of missing values in ongoing datasets at high temporal resolutions.

Related papers

• UNet with Axial Transformer : A Neural Weather Model for Precipitation Nowcasting [0.06906005491572399]
  We develop a novel method that employs Transformer-based machine learning models to forecast precipitation. This paper represents an initial research on the dataset used in the domain of next frame prediciton.
  arXiv  Detail & Related papers  (2025-04-28T01:20:30Z)
• Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales [5.453657018459705]
  We demonstrate the viability of score-based data assimilation in the context of realistically complex km-scale weather. By incorporating observations from 40 weather stations, 10% lower RMSEs on left-out stations are attained. It is a ripe time to explore extensions that combine increasingly ambitious regional state generators with an increasing set of in situ, ground-based, and satellite remote sensing data streams.
  arXiv  Detail & Related papers  (2024-06-19T10:28:11Z)
• 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)
• An evaluation of deep learning models for predicting water depth evolution in urban floods [59.31940764426359]
  We compare different deep learning models for prediction of water depth at high spatial resolution. Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model. Our results show that the deep learning models present in general lower errors compared to the other methods.
  arXiv  Detail & Related papers  (2023-02-20T16:08:54Z)
• A Bayesian Generative Adversarial Network (GAN) to Generate Synthetic Time-Series Data, Application in Combined Sewer Flow Prediction [3.3139597764446607]
  In machine learning, generative models are a class of methods capable of learning data distribution to generate artificial data. In this study, we developed a GAN model to generate synthetic time series to balance our limited recorded time series data. The aim is to predict the flow using precipitation data and examine the impact of data augmentation using synthetic data in model performance.
  arXiv  Detail & Related papers  (2023-01-31T16:12:26Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Deep Learning for Rain Fade Prediction in Satellite Communications [6.619650459583444]
  Line of sight satellite systems, unmanned aerial vehicles, high-altitude platforms, and microwave links are extremely susceptible to rain. Rain fade forecasting for these systems is critical because it allows the system to switch between ground gateways proactively before a rain fade event to maintain seamless service. Deep learning architecture is proposed that forecasts future rain fade using satellite and radar imagery data as well as link power measurements.
  arXiv  Detail & Related papers  (2021-10-02T00:43:02Z)
• 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)
• Semi-Supervised Video Deraining with Dynamic Rain Generator [59.71640025072209]
  This paper proposes a new semi-supervised video deraining method, in which a dynamic rain generator is employed to fit the rain layer. Specifically, such dynamic generator consists of one emission model and one transition model to simultaneously encode the spatially physical structure and temporally continuous changes of rain streaks. Various prior formats are designed for the labeled synthetic and unlabeled real data, so as to fully exploit the common knowledge underlying them.
  arXiv  Detail & Related papers  (2021-03-14T14:28:57Z)
• Detection of Anomalies in a Time Series Data using InfluxDB and Python [0.0]
  This paper demonstrates data cleaning and preparation for time-series data. It further proposes cost-sensitive machine learning algorithms as a solution to detect anomalous data points in time-series data.
  arXiv  Detail & Related papers  (2020-12-15T17:27:39Z)
• From Rain Generation to Rain Removal [67.71728610434698]
  We build a full Bayesian generative model for rainy image where the rain layer is parameterized as a generator. We employ the variational inference framework to approximate the expected statistical distribution of rainy image. Comprehensive experiments substantiate that the proposed model can faithfully extract the complex rain distribution.
  arXiv  Detail & Related papers  (2020-08-08T18:56:51Z)

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.