Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.atmosres.2020.104845 |
Development of advanced artificial intelligence models for daily rainfall prediction | |
Binh Thai Pham1; Lu Minh Le2; Tien-Thinh Le3; Kien-Trinh Thi Bui4; Vuong Minh Le2; Hai-Bang Ly1; Prakash, Indra5 | |
2020-06-01 | |
发表期刊 | ATMOSPHERIC RESEARCH
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ISSN | 0169-8095 |
EISSN | 1873-2895 |
出版年 | 2020 |
卷号 | 237 |
文章类型 | Article |
语种 | 英语 |
国家 | Vietnam; India |
英文摘要 | In this study, the main objective is to develop and compare several advanced Artificial Intelligent (AI) models namely Adaptive Network based Fuzzy Inference System optimized with Particle Swarm Optimization (PSOANFIS), Artificial Neural Networks (ANN) and Support Vector Machines (SVM) for the prediction of daily rainfall in Hoa Binh province, Vietnam. For this, meteorological variable parameters such as maximum temperature, minimum temperature, wind speed, relative humidity and solar radiation were collected and used as input parameters and daily rainfall as an output parameter in the models. Validation of the developed models was achieved using various quality assessment criteria such as correlation coefficient (R) and Mean Absolute Error (MAE), Skill Score (SS), Probability of Detection (POD), Critical Success Index (CSI), and False Alarm Ratio (FAR). The results showed that all the AI models provided reasonable predictions of daily rainfall but the SVM was found to be the best method for predicting rainfall. This method was also found to be the most robust and efficient prediction model while taking into account of input variability using the Monte Carlo approach. This AI based study would be helpful in quick and accurate prediction of daily rainfall. |
英文关键词 | Rainfall Artificial Neural Networks Robustness analysis Support Vector Machines Adaptive Network based Fuzzy Inference System Particle Swarm Optimization |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000525323100017 |
WOS关键词 | DAILY SOLAR-RADIATION ; NUMERICAL WEATHER PREDICTION ; NEURAL-NETWORK MODEL ; TARIM RIVER-BASIN ; GENETIC ALGORITHM ; MONSOON RAINFALL ; LANDSLIDE SUSCEPTIBILITY ; SEASONAL PREDICTIONS ; FORECAST SYSTEM ; INPUT SELECTION |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/278901 |
专题 | 地球科学 |
作者单位 | 1.Univ Transport Technol, Hanoi 100000, Vietnam; 2.Vietnam Natl Univ Agr, Fac Engn, Hanoi 100000, Vietnam; 3.Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam; 4.Thuyloi Univ, Geomat Ctr, Hanoi 100000, Vietnam; 5.BISAG, Dept Sci & Technol, Gandhinagar 382007, India |
推荐引用方式 GB/T 7714 | Binh Thai Pham,Lu Minh Le,Tien-Thinh Le,et al. Development of advanced artificial intelligence models for daily rainfall prediction[J]. ATMOSPHERIC RESEARCH,2020,237. |
APA | Binh Thai Pham.,Lu Minh Le.,Tien-Thinh Le.,Kien-Trinh Thi Bui.,Vuong Minh Le.,...&Prakash, Indra.(2020).Development of advanced artificial intelligence models for daily rainfall prediction.ATMOSPHERIC RESEARCH,237. |
MLA | Binh Thai Pham,et al."Development of advanced artificial intelligence models for daily rainfall prediction".ATMOSPHERIC RESEARCH 237(2020). |
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