干旱区生态环境知识资源中心(Arid): Precipitation forecasting by large-scale climate indices and machine learning techniques

Arid

	Precipitation forecasting by large-scale climate indices and machine learning techniques
	Gholami Rostam Mehdi; Sadatinejad Seyyed Javad; Malekian Arash
来源期刊	干旱区科学
ISSN	1674-6767
出版年	2020
卷号	12 期号:5
英文摘要	Global warming is one of the most complicated challenges of our time causing considerable tension on our societies and on the environment. The impacts of global warming are felt unprecedentedly in a wide variety of ways from shifting weather patterns that threatens food production, to rising sea levels that deteriorates the risk of catastrophic flooding. Among all aspects related to global warming, there is a growing concern on water resource management. This field is targeted at preventing future water crisis threatening human beings. The very first stage in such management is to recognize the prospective climate parameters influencing the future water resource conditions. Numerous prediction models, methods and tools, in this case, have been developed and applied so far. In line with trend, the current study intends to compare three optimization algorithms on the platform of a multilayer perceptron (MLP) network to explore any meaningful connection between large-scale climate indices (LSCIs) and precipitation in the capital of Iran, a country which is located in an arid and semi-arid region and suffers from severe water scarcity caused by mismanagement over years and intensified by global warming. This situation has propelled a great deal of population to immigrate towards more developed cities within the country especially towards Tehran. Therefore, the current and future environmental conditions of this city especially its water supply conditions are of great importance. To tackle this complication an outlook for the future precipitation should be provided and appropriate forecasting trajectories compatible with this region's characteristics should be developed. To this end, the present study investigates three training methods namely backpropagation (BP), genetic algorithms (GAs), and particle swarm optimization (PSO) algorithms on a MLP platform. Two frameworks distinguished by their input compositions are denoted in this study: Concurrent Model Framework (CMF) and Integrated Model Framework (IMF). Through these two frameworks, 13 cases are generated: 12 cases within CMF, each of which contains all selected LSCIs in the same lead-times, and one case within IMF that is constituted from the combination of the most correlated LSCIs with Tehran precipitation in each lead-time. Following the evaluation of all model performances through related statistical tests, Taylor diagram is implemented to make comparison among the final selected models in all three optimization algorithms, the best of which is found to be MLP-PSO in IMF.
英文关键词	backpropagation genetic algorithms machine learning multilayer perceptron particle swarm optimization Taylor diagram
类型	Article
语种	英语
收录类别	CSCD
WOS类目	Agriculture
CSCD记录号	CSCD:6866304
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/353493
推荐引用方式 GB/T 7714	Gholami Rostam Mehdi,Sadatinejad Seyyed Javad,Malekian Arash. Precipitation forecasting by large-scale climate indices and machine learning techniques[J],2020,12(5).
APA	Gholami Rostam Mehdi,Sadatinejad Seyyed Javad,&Malekian Arash.(2020).Precipitation forecasting by large-scale climate indices and machine learning techniques.干旱区科学,12(5).
MLA	Gholami Rostam Mehdi,et al."Precipitation forecasting by large-scale climate indices and machine learning techniques".干旱区科学 12.5(2020).