干旱区生态环境知识资源中心(Arid): Evaluation of artificial intelligence models for flood and drought forecasting in arid and tropical regions

Arid

DOI	10.1016/j.envsoft.2021.105136
	Evaluation of artificial intelligence models for flood and drought forecasting in arid and tropical regions
	Adikari, Kasuni E.; Shrestha, Sangam; Ratnayake, Dhanika T.; Budhathoki, Aakanchya; Mohanasundaram, S.; Dailey, Matthew N.
通讯作者	Shrestha, S (corresponding author), Asian Inst Technol, Water Engn & Management, Pathum Thani 12120, Thailand.
来源期刊	ENVIRONMENTAL MODELLING & SOFTWARE
ISSN	1364-8152
EISSN	1873-6726
出版年	2021
卷号	144
英文摘要	With the advancement of computer science, Artificial Intelligence (AI) is being incorporated into many fields to increase prediction performance. Disaster management is one of the main fields embracing the techniques of AI. It is essential to forecast the occurrence of disasters in advance to take the necessary mitigation steps and reduce damage to life and property. Therefore, many types of research are conducted to predict such events due to climate change in advance using hydrological, mathematical, and AI-based approaches. This paper presents a comparison of three major accepted AI-based approaches in flood and drought forecasting. In this study, fluvial floods are measured by the runoff change in rivers whereas meteorological droughts are measured using the Standard Precipitation Index (SPI). The performance of the Convolutional Neural Network (CNN), Long-Short Term Memory network (LSTM), and Wavelet decomposition functions combined with the Adaptive NeuroFuzzy Inference System (WANFIS) are compared in flood and drought forecasting, with five statistical performance criteria and accepted flood and drought indicators used for comparison, extending to two climatic regions: arid and tropical. The results suggest that the CNN performs best in flood forecasting with WANFIS for meteorological drought forecasting, regardless of the climate of the region under study. Besides, the results demonstrate the increased accuracy of the CNN in applications with multiple features in the input.
英文关键词	Forecasting Floods Droughts Artificial intelligence Convolutional neural network Long-short term memory network
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000696669300005
WOS关键词	INDEX ; PROVINCE
WOS类目	Computer Science, Interdisciplinary Applications ; Engineering, Environmental ; Environmental Sciences ; Water Resources
WOS研究方向	Computer Science ; Engineering ; Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368414
作者单位	[Adikari, Kasuni E.; Shrestha, Sangam; Budhathoki, Aakanchya; Mohanasundaram, S.] Asian Inst Technol, Water Engn & Management, Pathum Thani 12120, Thailand; [Ratnayake, Dhanika T.] Asian Inst Technol, Ind Syst Engn, Pathum Thani 12120, Thailand; [Dailey, Matthew N.] Asian Inst Technol, Dept Informat & Commun Technol, Pathum Thani 12120, Thailand; [Budhathoki, Aakanchya] Kyoto Univ, Dept Civil & Earth Resources Engn, Nishikyo Ku, Kyoto 6158540, Japan
推荐引用方式 GB/T 7714	Adikari, Kasuni E.,Shrestha, Sangam,Ratnayake, Dhanika T.,et al. Evaluation of artificial intelligence models for flood and drought forecasting in arid and tropical regions[J],2021,144.
APA	Adikari, Kasuni E.,Shrestha, Sangam,Ratnayake, Dhanika T.,Budhathoki, Aakanchya,Mohanasundaram, S.,&Dailey, Matthew N..(2021).Evaluation of artificial intelligence models for flood and drought forecasting in arid and tropical regions.ENVIRONMENTAL MODELLING & SOFTWARE,144.
MLA	Adikari, Kasuni E.,et al."Evaluation of artificial intelligence models for flood and drought forecasting in arid and tropical regions".ENVIRONMENTAL MODELLING & SOFTWARE 144(2021).