Precipitation forecasting using satellite images and SVMs?

Abstract

The prediction of rainfall is important for planning; it can help individuals plan their days ahead; more importantly it can help governments prepare for potential disasters. This research aims to investigate a data-driven approach to rainfall intensity prediction using support vector machines (SVMs) and sequences of daily satellite precipitation images as input. The primary aim of the work is to accurately predict one day ahead, but is also extended to predict several days into the future.

Keywords

• Machine Learning Rainfall Satellite Sequence Images

Introduction

Weather forecasting aims to predict the state of the atmosphere at a speci c location and time in the future. The accurate prediction of the weather and climate is essential to the production of crops [ 1 ]. Weather can also cause natural disasters, such as typhoon in Mozambique in April 2019. An accurate weather forecasting system can provide early warnings which can help individuals and governments to better prepare for such events.

Recently, a number of papers studied the use of di erent machine learning techniques for weather prediction [[ 2 ], [ 3 ], [ 4 ], [ 5 ]]. Due to space constraints, we only explain Boonyuen et al's [ 6 ] work here. Boonyuen et al proposed the use of the standard \Inception 3" model to forecast rain fall up to three days ahead. Satellite image data taken every ten minutes was used as input. The system used one image as input to predict up to three days ahead on a binary classi cation scale of \rain" versus \no rain" with an accuracy of 63%. This accuracy represents an 13% advantage over random guessing which statistically yields a 50% accuracy for two-class problem. The authors recommended the use of more than one image as input, and to attempt to predict up to one week ahead. They also proposed to extend the classi cation scale to more classes, such as light rain, moderate rain, and heavy rain, which can serve as a more useful source of information. ? Supported by the Openserve/Telkom/Aria Technologies Centre-of-Excellence at

UWC and the use of the ilifu cloud computing facility.

E. Hussein and M. Ghaziasgar.

This research aims to investigate precipitation forecasting on the NCEP data set using SVMs.The data set consists of images of the United States (US) taken at 7am daily. One image of the data set is shown on the left of Figure 1. The parameters that will be investigated are: i) the length of the input image sequence i.e. the number of input days 2 f2; 4; 6; 8g and ii) the size of the input images 2 f100%; 50%; 25%g. Furthermore, we propose to divide up the US using a 5 5 grid seen in the centre of Figure 1; the US is very large and it is expected that the weather patterns that we aim to model and predict are more region-speci c. We therefore aim to develop a separate model for, and investigate each of the two parameters mentioned previously on, each grid cell. Finally, we aim to predict a a multi-class output where we classify rainfall into three classes 2 fno rain; light rain; heavy raing, and to predict k days ahead 2 f1; 2; 3; 4; 5; 6; 7; 14; 30g. 2

Preliminary Results

Precipitation forecasting using satellite images and SVMs

References

1. M. Reyniers , Quantitative Precipitation Forecasts Based on Radar Observations: Principles , Algorithms and Operational Systems. Institut Royal Meteorologique de Belgique Brussel , Belgium, 2008 .

2. H. S. Jang , K. Y. Bae , H.-S. Park, and D. K. Sung , \ Solar power prediction based on satellite images and support vector machine," IEEE Transactions on Sustainable Energy , vol. 7 , no. 3 , pp. 1255 { 1263 , 2016 .

3. S. S. Aung , Y. Senaha , S. Ohsawa , I. Nagayama , and S. Tamaki , \ Short-term prediction of localized heavy rain from radar imaging and machine learning," IEIE Transactions on Smart Processing & Computing , vol. 7 , no. 2 , pp. 107 { 115 , 2018 .

4. B. Klein , L. Wolf , and Y. Afek , \ A dynamic convolutional layer for short range weather prediction," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pp. 4840 { 4848 , 2015 .

5. X. Shi , Z. Chen , H. Wang , D.- Y. Yeung , W.-K. Wong, and W.-c. Woo, \ Convolutional lstm network: A machine learning approach for precipitation nowcasting," in Advances in Neural Information Processing Systems , pp. 802 { 810 , 2015 .

6. K. Boonyuen , P. Kaewprapha , and P. Srivihok , \ Daily rainfall forecast model from satellite image using convolution neural network," in 2018 IEEE International Conference on Information Technology , pp. 1 { 7 , 2018 .