A Strategy to Optimize the Implementation of a Machine-Learning Scheme for Extreme Meiyu Rainfall Prediction over Southern Taiwan

Jung-Lien Chu,Yi-Chiang Yu,Shih-Hao Su,Chou-Chun Chiang,Li-Rung Hwang,Kuan-Ling Lin,Li-Huan Hsu,Ting-Shuo Yo,Chieh-Ju Wang

doi:10.3390/w13202884

Abstract

This study aims to propose a strategy to optimize the performance of the Support Vector Machine (SVM) scheme for extreme Meiyu rainfall prediction over southern Taiwan. Variables derived from Climate Forecast System Reanalysis (CFSR) dataset are the candidates for predictor selection. A series of experiments with different combinations of predictors and domains are designed to obtain the optimal strategy for constructing the SVM scheme. The results reveal that the accuracy (ACC), positive predictive values (PPV), probability of detection (POD), and F1-score can exceed 0.6 on average. Choosing the predictors associated with the Meiyu system and determine the domain associated with the correlations between selected predictors and predictand can improve the forecast performance. Our strategy shows the potential to predict extreme Meiyu rainfall in southern Taiwan with lead times from 16 h to 64 h. The F1-score analysis further demonstrates that the forecast performance of our scheme is stable, with slight inter-annual fluctuations from 1990 to 2019. Higher performance would be expected when the north of the South China Sea is characterized by stronger southwesterly flow and abundant low-level moisture for a given year.

Highlights

Taiwan is a mountainous island located in the East Asia Summer Monsoon (EASM) region
During the Meiyu season, May and June, the EASM region is often characterized by a quasi-stationary front (Meiyu front) and its associated rain belt, which is elongated northeast to southwest from the Sea of Japan to the Bashi Channel and the northern South China Sea [2]
A well-trained model is able to increase the ratio of hits and reduce both the missing and the false alarms at the same time

Summary

Introduction

Taiwan is a mountainous island located in the East Asia Summer Monsoon (EASM) region. The SVM-based schemes for daily rainfall will be developed over the region of southern Taiwan during the Meiyu season. The station’s daily rainfall data from Central Weather Bureau Taiwan are used as the predictand over the region of southern Taiwan to conduct the SVM-based prediction schemes (Figure 1). The station’s daily rainfall data from Central Weather Bureau Taiwan are used3aosft1h3e predictand over the region of southern Taiwan to conduct the SVM-based prediction schemes (Figure 1). The principles of selecting variables are based on a large number of studies about the Meiyu system and torrential rainfall in Taiwan during the Meiyu season These Meiyu studies could be categorized into five topics: (1) the structure of front, (2) the frontal genesis, (3) the evolution of front, (4) the low-level Jet (LLJ), and (5) the interaction between the Meiyu system and topography [2].

Domain Selection

Evaluation Methods

Results