Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1007/s40333-020-0097-3 |
Precipitation forecasting by large-scale climate indices and machine learning techniques | |
Rostam, Mehdi Gholami; Sadatinejad, Seyyed Javad; Malekian, Arash | |
通讯作者 | Malekian, A |
来源期刊 | JOURNAL OF ARID LAND
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ISSN | 1674-6767 |
EISSN | 2194-7783 |
出版年 | 2020 |
卷号 | 12期号:5页码:854-864 |
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 |
开放获取类型 | Bronze |
收录类别 | SCI-E |
WOS记录号 | WOS:000590844100002 |
WOS关键词 | NORTH-ATLANTIC OSCILLATION ; MODEL PERFORMANCE ; MULTIPLE ASPECTS ; SOUTHERN ; SIGNALS ; TELECONNECTION ; OPTIMIZATION ; TEMPERATURE ; VARIABILITY ; CONNECTION |
WOS类目 | Environmental Sciences |
WOS研究方向 | Environmental Sciences & Ecology |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/328515 |
作者单位 | [Rostam, Mehdi Gholami; Sadatinejad, Seyyed Javad; Malekian, Arash] Univ Tehran, Tehran 1417466191, Iran |
推荐引用方式 GB/T 7714 | Rostam, Mehdi Gholami,Sadatinejad, Seyyed Javad,Malekian, Arash. Precipitation forecasting by large-scale climate indices and machine learning techniques[J],2020,12(5):854-864. |
APA | Rostam, Mehdi Gholami,Sadatinejad, Seyyed Javad,&Malekian, Arash.(2020).Precipitation forecasting by large-scale climate indices and machine learning techniques.JOURNAL OF ARID LAND,12(5),854-864. |
MLA | Rostam, Mehdi Gholami,et al."Precipitation forecasting by large-scale climate indices and machine learning techniques".JOURNAL OF ARID LAND 12.5(2020):854-864. |
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