气候变化领域集成服务门户(CCPortal): Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh

CCPortal

DOI	10.1007/s11069-021-04722-9
	Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh
	Azad M.A.K.; Islam A.R.M.T.; Rahman M.S.; Ayen K.
发表日期	2021
ISSN	0921030X
起始页码	1109
结束页码	1135
卷号	108 期号:1
英文摘要	Thunderstorm frequency (TSF) prediction with higher accuracy is of great significance under climate extremes for reducing potential damages. However, TSF prediction has received little attention because a thunderstorm event is a combination of intricate and unique weather scenarios with high instability, making it difficult to predict. To close this gap, we proposed two novel hybrid machine learning models through hybridization of data pre-processing ensemble empirical mode decomposition (EEMD) with two state-of-arts models, namely artificial neural network (ANN), support vector machine for TSF prediction at three categories over Bangladesh. We have demarcated the yearly TSF datasets into three categories for the period 1981–2016 recorded at 28 sites; high (March–June), moderate (July–October), and low (November–February) TSF months. The performance of the proposed EEMD-ANN and EEMD-SVM hybrid models was compared with classical ANN, SVM, and autoregressive integrated moving average. EEMD-ANN and EEMD-SVM hybrid models showed 8.02–22.48% higher performance precision in terms of root mean square error compared to other models at high-, moderate-, and low-frequency categories. Eleven out of 21 input parameters were selected based on the random forest variable importance analysis. The sensitivity analysis results showed that each input parameter was positively contributed to building the best model of each category, and thunderstorm days are the most contributing parameters influencing TSF prediction. The proposed hybrid models outperformed the conventional models where EEMD-ANN is the most skillful for high TSF prediction, and EEMD-SVM is for moderate and low TSF prediction. The findings indicate the potential of hybridization of EEMD with the conventional models for improving prediction precision. The hybrid models developed in this work can be adopted for TSF prediction in Bangladesh as well as different parts of the world. © 2021, The Author(s), under exclusive licence to Springer Nature B.V.
关键词	Bangladesh Ensemble empirical mode decomposition Hybrid model Random forest Sensitivity analysis Thunderstorm
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206093
作者单位	Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh
推荐引用方式 GB/T 7714	Azad M.A.K.,Islam A.R.M.T.,Rahman M.S.,et al. Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh[J],2021,108(1).
APA	Azad M.A.K.,Islam A.R.M.T.,Rahman M.S.,&Ayen K..(2021).Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh.Natural Hazards,108(1).
MLA	Azad M.A.K.,et al."Development of novel hybrid machine learning models for monthly thunderstorm frequency prediction over Bangladesh".Natural Hazards 108.1(2021).