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DOI10.1016/j.atmosres.2020.104845
Development of advanced artificial intelligence models for daily rainfall prediction
Pham B.T.; Le L.M.; Le T.-T.; Bui K.-T.T.; Le V.M.; Ly H.-B.; Prakash I.
Date Issued2020
ISSN0169-8095
Volume237
Other AbstractIn 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. © 2020 Elsevier B.V.
enkeywordsAdaptive Network based Fuzzy Inference System; Artificial Neural Networks; Particle Swarm Optimization; Rainfall; Robustness analysis; Support Vector Machines
Subject AreaForecasting; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Monte Carlo methods; Neural networks; Particle swarm optimization (PSO); Rain; Support vector machines; Wind; Adaptive network based fuzzy inference system; Artificial intelligent; Correlation coefficient; Efficient predictions; Meteorological variables; Minimum temperatures; Probability of detection; Robustness analysis; Fuzzy inference; artificial intelligence; artificial neural network; modeling; optimization; precipitation assessment; prediction; rainfall; support vector machine; Hoa Binh; Viet Nam
Language英语
scopus keywordsForecasting; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Monte Carlo methods; Neural networks; Particle swarm optimization (PSO); Rain; Support vector machines; Wind; Adaptive network based fuzzy inference system; Artificial intelligent; Correlation coefficient; Efficient predictions; Meteorological variables; Minimum temperatures; Probability of detection; Robustness analysis; Fuzzy inference; artificial intelligence; artificial neural network; modeling; optimization; precipitation assessment; prediction; rainfall; support vector machine; Hoa Binh; Viet Nam
journalAtmospheric Research
Document Type期刊论文
Identifierhttp://gcip.llas.ac.cn/handle/2XKMVOVA/120458
AffiliationUniversity of Transport Technology, Hanoi, 100000, Viet Nam; Faculty of Engineering, Vietnam National University of Agriculture, Gia Lam, Hanoi, 100000, Viet Nam; Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam; Geomatics Center, Thuyloi University, Hanoi, 100000, Viet Nam; Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar, 382007, India
Recommended Citation
GB/T 7714
Pham B.T.,Le L.M.,Le T.-T.,et al. Development of advanced artificial intelligence models for daily rainfall prediction[J],2020,237.
APA Pham B.T..,Le L.M..,Le T.-T..,Bui K.-T.T..,Le V.M..,...&Prakash I..(2020).Development of advanced artificial intelligence models for daily rainfall prediction.Atmospheric Research,237.
MLA Pham B.T.,et al."Development of advanced artificial intelligence models for daily rainfall prediction".Atmospheric Research 237(2020).
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