Notice of Retraction: Short-term and local rainfall probability prediction based on a dislocation support vector machine model using satellite and in-situ observational data

Chen, Xunlai; He, Guangjun; Chen, Yuanzhao; Zhang, Shuting; Chen, Jinsong; Qian, Jing; Yu, Han

doi:10.1109/access.2019.2913366

Cited by 6 publications

(2 citation statements)

References 28 publications

(28 reference statements)

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“…In [23], a machine learning method algorithm based on support vector machine with dislocation of temporal variables (DSVM) was used to make short-term rain predictions.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Rainfall Prediction Models Using Machine Learning in Islands with Complex Orography: Tenerife Island

2019

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We present a comparative study between predictive monthly rainfall models for islands of complex orography using machine learning techniques. The models have been developed for the island of Tenerife (Canary Islands). Weather forecasting is influenced both by the local geographic characteristics as well as by the time horizon comprised. Accuracy of mid-term rainfall prediction on islands with complex orography is generally low when carried out with atmospheric models. Predictive models based on algorithms such as Random Forest or Extreme Gradient Boosting among others were analyzed. The predictors used in the models include weather predictors measured in two main meteorological stations, reanalysis predictors from the National Oceanic and Atmospheric Administration, and the global predictor North Atlantic Oscillation, all of them obtained over a period of time of more than four decades. When comparing the proposed models, we evaluated accuracy, kappa and interpretability of the model obtained, as well as the relevance of the predictors used. The results show that global predictors such as the North Atlantic Oscillation Index (NAO) have a very low influence, while the local Geopotential Height (GPH) predictor is relatively more important. Machine learning prediction models are a relevant proposition for predicting medium-term precipitation in similar geographical regions.

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“…In [23], a machine learning method algorithm based on support vector machine with dislocation of temporal variables (DSVM) was used to make short-term rain predictions.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Rainfall Prediction Models Using Machine Learning in Islands with Complex Orography: Tenerife Island

2019

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“…Several studies have been conducted on generating precipitation using ML and DL. In [25], short-term rain forecasts were made using the dislocation support vector machine (DSVM) model. Observations and satellite data were used as the input data.…”

Section: Introductionmentioning

confidence: 99%

Rainfall Forecast Using Machine Learning with High Spatiotemporal Satellite Imagery Every 10 Minutes

et al. 2022

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Increasing the accuracy of rainfall forecasts is crucial as an effort to prevent hydrometeorological disasters. Weather changes that can occur suddenly and in a local scope make fast and precise weather forecasts increasingly difficult to inform. Additionally, the results of the numerical weather model used by the Indonesia Agency for Meteorology, Climatology, and Geophysics are only able to predict the rainfall with a temporal resolution of 1–3 h and cannot yet address the need for rainfall information with high spatial and temporal resolution. Therefore, this study aims to provide the rainfall forecast in high spatiotemporal resolution using Himawari-8 and GPM IMERG (Global Precipitation Measurement: The Integrated Multi-satellite Retrievals) data. The multivariate LSTM (long short-term memory) forecasting is employed to predict the cloud brightness temperature by using the selected Himawari-8 bands as the input and training data. For the rain rate regression, we used the random forest technique to identify the rainfall and non-rainfall pixels from GPM IMERG data as the input in advance. The results of the rainfall forecast showed low values of mean error and root mean square error of 0.71 and 1.54 mm/3 h, respectively, compared to the observation data, indicating that the proposed study may help meteorological stations provide the weather information for aviation purposes.

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A Comparison of the Accuracy of Multi-satellite Precipitation Estimation and Ground Meteorological Records Over Southwestern Nigeria

Akinyemi

Ayanlade

Nwaezeigwe

et al. 2019

Remote Sens Earth Syst Sci

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Notice of Retraction: Short-term and local rainfall probability prediction based on a dislocation support vector machine model using satellite and in-situ observational data

Cited by 6 publications

References 28 publications

Comparative Analysis of Rainfall Prediction Models Using Machine Learning in Islands with Complex Orography: Tenerife Island

Comparative Analysis of Rainfall Prediction Models Using Machine Learning in Islands with Complex Orography: Tenerife Island

Rainfall Forecast Using Machine Learning with High Spatiotemporal Satellite Imagery Every 10 Minutes

A Comparison of the Accuracy of Multi-satellite Precipitation Estimation and Ground Meteorological Records Over Southwestern Nigeria

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